WorldWideScience

Sample records for machine vision techniques

  1. Machine Learning Techniques in Clinical Vision Sciences.

    Science.gov (United States)

    Caixinha, Miguel; Nunes, Sandrina

    2017-01-01

    This review presents and discusses the contribution of machine learning techniques for diagnosis and disease monitoring in the context of clinical vision science. Many ocular diseases leading to blindness can be halted or delayed when detected and treated at its earliest stages. With the recent developments in diagnostic devices, imaging and genomics, new sources of data for early disease detection and patients' management are now available. Machine learning techniques emerged in the biomedical sciences as clinical decision-support techniques to improve sensitivity and specificity of disease detection and monitoring, increasing objectively the clinical decision-making process. This manuscript presents a review in multimodal ocular disease diagnosis and monitoring based on machine learning approaches. In the first section, the technical issues related to the different machine learning approaches will be present. Machine learning techniques are used to automatically recognize complex patterns in a given dataset. These techniques allows creating homogeneous groups (unsupervised learning), or creating a classifier predicting group membership of new cases (supervised learning), when a group label is available for each case. To ensure a good performance of the machine learning techniques in a given dataset, all possible sources of bias should be removed or minimized. For that, the representativeness of the input dataset for the true population should be confirmed, the noise should be removed, the missing data should be treated and the data dimensionally (i.e., the number of parameters/features and the number of cases in the dataset) should be adjusted. The application of machine learning techniques in ocular disease diagnosis and monitoring will be presented and discussed in the second section of this manuscript. To show the clinical benefits of machine learning in clinical vision sciences, several examples will be presented in glaucoma, age-related macular degeneration

  2. Application of Machine Vision Technique in Weed Identification

    Institute of Scientific and Technical Information of China (English)

    LIU Zhen-heng; ZHANG Chang-li; FANG Jun-long

    2004-01-01

    This paper mainly introduces some foreign research methods and fruits about weed identification by applying machine vision. This facet researches is lack in our country, this paper could be reference for domestic studies about weed identification.

  3. Robot Guidance Using Machine Vision Techniques in Industrial Environments: A Comparative Review

    Directory of Open Access Journals (Sweden)

    Luis Pérez

    2016-03-01

    Full Text Available In the factory of the future, most of the operations will be done by autonomous robots that need visual feedback to move around the working space avoiding obstacles, to work collaboratively with humans, to identify and locate the working parts, to complete the information provided by other sensors to improve their positioning accuracy, etc. Different vision techniques, such as photogrammetry, stereo vision, structured light, time of flight and laser triangulation, among others, are widely used for inspection and quality control processes in the industry and now for robot guidance. Choosing which type of vision system to use is highly dependent on the parts that need to be located or measured. Thus, in this paper a comparative review of different machine vision techniques for robot guidance is presented. This work analyzes accuracy, range and weight of the sensors, safety, processing time and environmental influences. Researchers and developers can take it as a background information for their future works.

  4. Robot Guidance Using Machine Vision Techniques in Industrial Environments: A Comparative Review

    Science.gov (United States)

    Pérez, Luis; Rodríguez, Íñigo; Rodríguez, Nuria; Usamentiaga, Rubén; García, Daniel F.

    2016-01-01

    In the factory of the future, most of the operations will be done by autonomous robots that need visual feedback to move around the working space avoiding obstacles, to work collaboratively with humans, to identify and locate the working parts, to complete the information provided by other sensors to improve their positioning accuracy, etc. Different vision techniques, such as photogrammetry, stereo vision, structured light, time of flight and laser triangulation, among others, are widely used for inspection and quality control processes in the industry and now for robot guidance. Choosing which type of vision system to use is highly dependent on the parts that need to be located or measured. Thus, in this paper a comparative review of different machine vision techniques for robot guidance is presented. This work analyzes accuracy, range and weight of the sensors, safety, processing time and environmental influences. Researchers and developers can take it as a background information for their future works. PMID:26959030

  5. Machine Vision Handbook

    CERN Document Server

    2012-01-01

    The automation of visual inspection is becoming more and more important in modern industry as a consistent, reliable means of judging the quality of raw materials and manufactured goods . The Machine Vision Handbook  equips the reader with the practical details required to engineer integrated mechanical-optical-electronic-software systems. Machine vision is first set in the context of basic information on light, natural vision, colour sensing and optics. The physical apparatus required for mechanized image capture – lenses, cameras, scanners and light sources – are discussed followed by detailed treatment of various image-processing methods including an introduction to the QT image processing system. QT is unique to this book, and provides an example of a practical machine vision system along with extensive libraries of useful commands, functions and images which can be implemented by the reader. The main text of the book is completed by studies of a wide variety of applications of machine vision in insp...

  6. Technique for Calibration of Chassis components based on encoding marks and machine Vision metrology

    Institute of Scientific and Technical Information of China (English)

    SONG Li-mei; ZHANG Chun-bo; WEI Yi-ying; CHEN Hua-wei

    2011-01-01

    @@ A novel technique for calibrating crucial parameters of chassis components is proposed, which utilizes the machine vision metrology to measure 3D coordinates of the center of a component's hole for assembling in the 3D world coordinate system.In the measurement, encoding marks with special patterns will be assembled on the chassis component associated with cross drone and staff gauge located near the chassis.The geometry and coordinates of the cross drone consist of two planes orthogonal to each other and the staff gauge is in 3D space with high precision.A few images are taken by a highresolution camera in different orientations and perspectives.The 3D coordinates of 5 key points on the encoding marks will be calculated by the machine vision technique and those of the center of the holes to be calibrated will be calculated by the deduced algorithm in this paper.Experimental results show that the algorithm and the technique can satisfy the precision requirement when the components are assembled, and the average measurement precision provided by the algorithm is 0.0174 mm.

  7. Feature-Free Activity Classification of Inertial Sensor Data With Machine Vision Techniques: Method, Development, and Evaluation.

    Science.gov (United States)

    Dominguez Veiga, Jose Juan; O'Reilly, Martin; Whelan, Darragh; Caulfield, Brian; Ward, Tomas E

    2017-08-04

    Inertial sensors are one of the most commonly used sources of data for human activity recognition (HAR) and exercise detection (ED) tasks. The time series produced by these sensors are generally analyzed through numerical methods. Machine learning techniques such as random forests or support vector machines are popular in this field for classification efforts, but they need to be supported through the isolation of a potentially large number of additionally crafted features derived from the raw data. This feature preprocessing step can involve nontrivial digital signal processing (DSP) techniques. However, in many cases, the researchers interested in this type of activity recognition problems do not possess the necessary technical background for this feature-set development. The study aimed to present a novel application of established machine vision methods to provide interested researchers with an easier entry path into the HAR and ED fields. This can be achieved by removing the need for deep DSP skills through the use of transfer learning. This can be done by using a pretrained convolutional neural network (CNN) developed for machine vision purposes for exercise classification effort. The new method should simply require researchers to generate plots of the signals that they would like to build classifiers with, store them as images, and then place them in folders according to their training label before retraining the network. We applied a CNN, an established machine vision technique, to the task of ED. Tensorflow, a high-level framework for machine learning, was used to facilitate infrastructure needs. Simple time series plots generated directly from accelerometer and gyroscope signals are used to retrain an openly available neural network (Inception), originally developed for machine vision tasks. Data from 82 healthy volunteers, performing 5 different exercises while wearing a lumbar-worn inertial measurement unit (IMU), was collected. The ability of the

  8. Understanding and applying machine vision

    CERN Document Server

    Zeuch, Nello

    2000-01-01

    A discussion of applications of machine vision technology in the semiconductor, electronic, automotive, wood, food, pharmaceutical, printing, and container industries. It describes systems that enable projects to move forward swiftly and efficiently, and focuses on the nuances of the engineering and system integration of machine vision technology.

  9. Using machine vision and data mining techniques to identify cell properties via microfluidic flow analysis

    Science.gov (United States)

    Horowitz, Geoffrey; Bowie, Samuel; Liu, Anna; Stone, Nicholas; Sulchek, Todd; Alexeev, Alexander

    2016-11-01

    In order to quickly identify the wide range of mechanistic properties that are seen in cell populations, a coupled machine vision and data mining analysis is developed to examine high speed videos of cells flowing through a microfluidic device. The microfluidic device contains a microchannel decorated with a periodical array of diagonal ridges. The ridges compress flowing cells that results in complex cell trajectory and induces cell cross-channel drift, both depend on the cell intrinsic mechanical properties that can be used to characterize specific cell lines. Thus, the cell trajectory analysis can yield a parameter set that can serve as a unique identifier of a cell's membership to a specific cell population. By using the correlations between the cell populations and measured cell trajectories in the ridged microchannel, mechanical properties of individual cells and their specific populations can be identified via only information captured using video analysis. Financial support provided by National Science Foundation (NSF) Grant No. CMMI 1538161.

  10. A Novel Approach to Automatic Road-Accident Detection using Machine Vision Techniques

    Directory of Open Access Journals (Sweden)

    Vaishnavi Ravindran

    2016-11-01

    Full Text Available In this paper, a novel approach for automatic road accident detection is proposed. The approach is based on detecting damaged vehicles from footage received from surveillance cameras installed in roads and highways which would indicate the occurrence of a road accident. Detection of damaged cars falls under the category of object detection in the field of machine vision and has not been achieved so far. In this paper, a new supervised learning method comprising of three different stages which are combined into a single framework in a serial manner which successfully detects damaged cars from static images is proposed. The three stages use five support vector machines trained with Histogram of gradients (HOG and Gray level co-occurrence matrix (GLCM features. Since damaged car detection has not been attempted, two datasets of damaged cars - Damaged Cars Dataset-1 (DCD-1 and Damaged Cars Dataset-2 (DCD-2 – was compiled for public release. Experiments were conducted on DCD-1 and DCD-2 which differ based on the distance at which the image is captured and the quality of the images. The accuracy of the system is 81.83% for DCD-1 captured at approximately 2 meters with good quality and 64.37% for DCD-2 captured at approximately 20 meters with poor quality.

  11. Machine vision is not computer vision

    Science.gov (United States)

    Batchelor, Bruce G.; Charlier, Jean-Ray

    1998-10-01

    The identity of Machine Vision as an academic and practical subject of study is asserted. In particular, the distinction between Machine Vision on the one hand and Computer Vision, Digital Image Processing, Pattern Recognition and Artificial Intelligence on the other is emphasized. The article demonstrates through four cases studies that the active involvement of a person who is sensitive to the broad aspects of vision system design can avoid disaster and can often achieve a successful machine that would not otherwise have been possible. This article is a transcript of the key- note address presented at the conference. Since the proceedings are prepared and printed before the conference, it is not possible to include a record of the response to this paper made by the delegates during the round-table discussion. It is hoped to collate and disseminate these via the World Wide Web after the event. (A link will be provided at http://bruce.cs.cf.ac.uk/bruce/index.html.).

  12. On the Use of Machine Vision Techniques to Detect Human Settlements in Satellite Images

    Energy Technology Data Exchange (ETDEWEB)

    Kamath, C; Sengupta, S K; Poland, D; Futterman, J A H

    2003-01-10

    The automated production of maps of human settlement from recent satellite images is essential to studies of urbanization, population movement, and the like. The spectral and spatial resolution of such imagery is often high enough to successfully apply computer vision techniques. However, vast amounts of data have to be processed quickly. In this paper, we propose an approach that processes the data in several different stages. At each stage, using features appropriate to that stage, we identify the portion of the data likely to contain information relevant to the identification of human settlements. This data is used as input to the next stage of processing. Since the size of the data has reduced, we can now use more complex features in this next stage. These features can be more representative of human settlements, and also more time consuming to extract from the image data. Such a hierarchical approach enables us to process large amounts of data in a reasonable time, while maintaining the accuracy of human settlement identification. We illustrate our multi-stage approach using IKONOS 4-band and panchromatic images, and compare it with the straight-forward processing of the entire image.

  13. Machine vision and the OMV

    Science.gov (United States)

    Mcanulty, M. A.

    1986-01-01

    The orbital Maneuvering Vehicle (OMV) is intended to close with orbiting targets for relocation or servicing. It will be controlled via video signals and thruster activation based upon Earth or space station directives. A human operator is squarely in the middle of the control loop for close work. Without directly addressing future, more autonomous versions of a remote servicer, several techniques that will doubtless be important in a future increase of autonomy also have some direct application to the current situation, particularly in the area of image enhancement and predictive analysis. Several techniques are presentet, and some few have been implemented, which support a machine vision capability proposed to be adequate for detection, recognition, and tracking. Once feasibly implemented, they must then be further modified to operate together in real time. This may be achieved by two courses, the use of an array processor and some initial steps toward data reduction. The methodology or adapting to a vector architecture is discussed in preliminary form, and a highly tentative rationale for data reduction at the front end is also discussed. As a by-product, a working implementation of the most advanced graphic display technique, ray-casting, is described.

  14. Machine Learning for Computer Vision

    CERN Document Server

    Battiato, Sebastiano; Farinella, Giovanni

    2013-01-01

    Computer vision is the science and technology of making machines that see. It is concerned with the theory, design and implementation of algorithms that can automatically process visual data to recognize objects, track and recover their shape and spatial layout. The International Computer Vision Summer School - ICVSS was established in 2007 to provide both an objective and clear overview and an in-depth analysis of the state-of-the-art research in Computer Vision. The courses are delivered by world renowned experts in the field, from both academia and industry, and cover both theoretical and practical aspects of real Computer Vision problems. The school is organized every year by University of Cambridge (Computer Vision and Robotics Group) and University of Catania (Image Processing Lab). Different topics are covered each year. A summary of the past Computer Vision Summer Schools can be found at: http://www.dmi.unict.it/icvss This edited volume contains a selection of articles covering some of the talks and t...

  15. Machine vision theory, algorithms, practicalities

    CERN Document Server

    Davies, E R

    2005-01-01

    In the last 40 years, machine vision has evolved into a mature field embracing a wide range of applications including surveillance, automated inspection, robot assembly, vehicle guidance, traffic monitoring and control, signature verification, biometric measurement, and analysis of remotely sensed images. While researchers and industry specialists continue to document their work in this area, it has become increasingly difficult for professionals and graduate students to understand the essential theory and practicalities well enough to design their own algorithms and systems. This book directl

  16. Egg volume prediction using machine vision technique based on pappus theorem and artificial neural network.

    Science.gov (United States)

    Soltani, Mahmoud; Omid, Mahmoud; Alimardani, Reza

    2015-05-01

    Egg size is one of the important properties of egg that is judged by customers. Accordingly, in egg sorting and grading, the size of eggs must be considered. In this research, a new method of egg volume prediction was proposed without need to measure weight of egg. An accurate and efficient image processing algorithm was designed and implemented for computing major and minor diameters of eggs. Two methods of egg size modeling were developed. In the first method, a mathematical model was proposed based on Pappus theorem. In second method, Artificial Neural Network (ANN) technique was used to estimate egg volume. The determined egg volume by these methods was compared statistically with actual values. For mathematical modeling, the R(2), Mean absolute error and maximum absolute error values were obtained as 0.99, 0.59 cm(3) and 1.69 cm(3), respectively. To determine the best ANN, R(2) test and RMSEtest were used as selection criteria. The best ANN topology was 2-28-1 which had the R(2) test and RMSEtest of 0.992 and 0.66, respectively. After system calibration, the proposed models were evaluated. The results which indicated the mathematical modeling yielded more satisfying results. So this technique was selected for egg size determination.

  17. Fresh market carrot inspection by machine vision

    Science.gov (United States)

    Howarth, M. Scott; Searcy, Stephen W.

    1991-02-01

    A machine vision system was developed to inspect fresh market carrots. It was designed to grade carrots with an axial and transverse resolution of 0. 5mmper pixel. Hardware consisted of camera digital signal processing (DSP) imaging board host computer and illumination components. Feature extraction methods detect the major defects. A Bayes classification technique was used to construct the decision function which classify carrots as acceptable or cull. The system was able to image and classify in approximately 2. 5carrots/second. 1.

  18. Computer and machine vision theory, algorithms, practicalities

    CERN Document Server

    Davies, E R

    2012-01-01

    Computer and Machine Vision: Theory, Algorithms, Practicalities (previously entitled Machine Vision) clearly and systematically presents the basic methodology of computer and machine vision, covering the essential elements of the theory while emphasizing algorithmic and practical design constraints. This fully revised fourth edition has brought in more of the concepts and applications of computer vision, making it a very comprehensive and up-to-date tutorial text suitable for graduate students, researchers and R&D engineers working in this vibrant subject. Key features include: Practical examples and case studies give the 'ins and outs' of developing real-world vision systems, giving engineers the realities of implementing the principles in practice New chapters containing case studies on surveillance and driver assistance systems give practical methods on these cutting-edge applications in computer vision Necessary mathematics and essential theory are made approachable by careful explanations and well-il...

  19. A noninvasive technique for real-time detection of bruises in apple surface based on machine vision

    Science.gov (United States)

    Zhao, Juan; Peng, Yankun; Dhakal, Sagar; Zhang, Leilei; Sasao, Akira

    2013-05-01

    Apple is one of the highly consumed fruit item in daily life. However, due to its high damage potential and massive influence on taste and export, the quality of apple has to be detected before it reaches the consumer's hand. This study was aimed to develop a hardware and software unit for real-time detection of apple bruises based on machine vision technology. The hardware unit consisted of a light shield installed two monochrome cameras at different angles, LED light source to illuminate the sample, and sensors at the entrance of box to signal the positioning of sample. Graphical Users Interface (GUI) was developed in VS2010 platform to control the overall hardware and display the image processing result. The hardware-software system was developed to acquire the images of 3 samples from each camera and display the image processing result in real time basis. An image processing algorithm was developed in Opencv and C++ platform. The software is able to control the hardware system to classify the apple into two grades based on presence/absence of surface bruises with the size of 5mm. The experimental result is promising and the system with further modification can be applicable for industrial production in near future.

  20. Insect vision as model for machine vision

    Science.gov (United States)

    Osorio, D.; Sobey, Peter J.

    1992-11-01

    The neural architecture, neurophysiology and behavioral abilities of insect vision are described, and compared with that of mammals. Insects have a hardwired neural architecture of highly differentiated neurons, quite different from the cerebral cortex, yet their behavioral abilities are in important respects similar to those of mammals. These observations challenge the view that the key to the power of biological neural computation is distributed processing by a plastic, highly interconnected, network of individually undifferentiated and unreliable neurons that has been a dominant picture of biological computation since Pitts and McCulloch's seminal work in the 1940's.

  1. Color in machine vision and its application

    Institute of Scientific and Technical Information of China (English)

    2001-01-01

    Color is the phenomenon of human visual perception and the module of machine vision. Color information is widely used in the areas of virtual reality and humancomputer interaction. Color is the product of a visual environment, illumination and the human brain. Research on color information representation and its processing is typically interdisciplinary. Based on our research work on human color perception and machine color vision and its application, we summarized the hotspots of color studies in recent developments and new approaches to color vision,including basic theories and the application of color information in virtual reality, content-based image retrieval, and face recognition.

  2. 3D vision assisted flexible robotic assembly of machine components

    Science.gov (United States)

    Ogun, Philips S.; Usman, Zahid; Dharmaraj, Karthick; Jackson, Michael R.

    2015-12-01

    Robotic assembly systems either make use of expensive fixtures to hold components in predefined locations, or the poses of the components are determined using various machine vision techniques. Vision-guided assembly robots can handle subtle variations in geometries and poses of parts. Therefore, they provide greater flexibility than the use of fixtures. However, the currently established vision-guided assembly systems use 2D vision, which is limited to three degrees of freedom. The work reported in this paper is focused on flexible automated assembly of clearance fit machine components using 3D vision. The recognition and the estimation of the poses of the components are achieved by matching their CAD models with the acquired point cloud data of the scene. Experimental results obtained from a robot demonstrating the assembly of a set of rings on a shaft show that the developed system is not only reliable and accurate, but also fast enough for industrial deployment.

  3. Measurement of seedling growth rate by machine vision

    Science.gov (United States)

    Howarth, M. Scott; Stanwood, Phillip C.

    1993-05-01

    Seed vigor and germination tests have traditionally been used to determine deterioration of seed samples. Vigor tests describe the seed potential to emerge and produce a mature crop under certain field conditions and one measure is seedling growth rate. A machine vision system was developed to measure root growth rate over the entire germination period. The machine vision measurement technique was compared to the manual growth rate technique. The vision system provided similar growth rate measurements as compared to the manual growth rate technique. The average error between the system and a manual measurement was -0.13 for the lettuce test and -0.07 for the sorghum test. This technique also provided an accurate representation of the growth rate as well as percent germination.

  4. Recognition of Mould Colony on Unhulled Paddy Based on Computer Vision using Conventional Machine-learning and Deep Learning Techniques

    Science.gov (United States)

    Sun, Ke; Wang, Zhengjie; Tu, Kang; Wang, Shaojin; Pan, Leiqing

    2016-11-01

    To investigate the potential of conventional and deep learning techniques to recognize the species and distribution of mould in unhulled paddy, samples were inoculated and cultivated with five species of mould, and sample images were captured. The mould recognition methods were built using support vector machine (SVM), back-propagation neural network (BPNN), convolutional neural network (CNN), and deep belief network (DBN) models. An accuracy rate of 100% was achieved by using the DBN model to identify the mould species in the sample images based on selected colour-histogram parameters, followed by the SVM and BPNN models. A pitch segmentation recognition method combined with different classification models was developed to recognize the mould colony areas in the image. The accuracy rates of the SVM and CNN models for pitch classification were approximately 90% and were higher than those of the BPNN and DBN models. The CNN and DBN models showed quicker calculation speeds for recognizing all of the pitches segmented from a single sample image. Finally, an efficient uniform CNN pitch classification model for all five types of sample images was built. This work compares multiple classification models and provides feasible recognition methods for mouldy unhulled paddy recognition.

  5. Handbook of 3D machine vision optical metrology and imaging

    CERN Document Server

    Zhang, Song

    2013-01-01

    With the ongoing release of 3D movies and the emergence of 3D TVs, 3D imaging technologies have penetrated our daily lives. Yet choosing from the numerous 3D vision methods available can be frustrating for scientists and engineers, especially without a comprehensive resource to consult. Filling this gap, Handbook of 3D Machine Vision: Optical Metrology and Imaging gives an extensive, in-depth look at the most popular 3D imaging techniques. It focuses on noninvasive, noncontact optical methods (optical metrology and imaging). The handbook begins with the well-studied method of stereo vision and

  6. Machine Vision Giving Eyes to Robots. Resources in Technology.

    Science.gov (United States)

    Technology Teacher, 1990

    1990-01-01

    This module introduces machine vision, which can be used for inspection, robot guidance and part sorting. The future for machine vision will include new technology and will bring vision systems closer to the ultimate vision processor, the human eye. Includes a student quiz, outcomes, and activities. (JOW)

  7. Automated analysis of retinal imaging using machine learning techniques for computer vision [version 2; referees: 2 approved

    Directory of Open Access Journals (Sweden)

    Jeffrey De Fauw

    2017-06-01

    Full Text Available There are almost two million people in the United Kingdom living with sight loss, including around 360,000 people who are registered as blind or partially sighted. Sight threatening diseases, such as diabetic retinopathy and age related macular degeneration have contributed to the 40% increase in outpatient attendances in the last decade but are amenable to early detection and monitoring. With early and appropriate intervention, blindness may be prevented in many cases. Ophthalmic imaging provides a way to diagnose and objectively assess the progression of a number of pathologies including neovascular (“wet” age-related macular degeneration (wet AMD and diabetic retinopathy. Two methods of imaging are commonly used: digital photographs of the fundus (the ‘back’ of the eye and Optical Coherence Tomography (OCT, a modality that uses light waves in a similar way to how ultrasound uses sound waves. Changes in population demographics and expectations and the changing pattern of chronic diseases creates a rising demand for such imaging. Meanwhile, interrogation of such images is time consuming, costly, and prone to human error. The application of novel analysis methods may provide a solution to these challenges. This research will focus on applying novel machine learning algorithms to automatic analysis of both digital fundus photographs and OCT in Moorfields Eye Hospital NHS Foundation Trust patients. Through analysis of the images used in ophthalmology, along with relevant clinical and demographic information, DeepMind Health will investigate the feasibility of automated grading of digital fundus photographs and OCT and provide novel quantitative measures for specific disease features and for monitoring the therapeutic success.

  8. Automated analysis of retinal imaging using machine learning techniques for computer vision [version 1; referees: 2 approved

    Directory of Open Access Journals (Sweden)

    Jeffrey De Fauw

    2016-07-01

    Full Text Available There are almost two million people in the United Kingdom living with sight loss, including around 360,000 people who are registered as blind or partially sighted. Sight threatening diseases, such as diabetic retinopathy and age related macular degeneration have contributed to the 40% increase in outpatient attendances in the last decade but are amenable to early detection and monitoring. With early and appropriate intervention, blindness may be prevented in many cases.   Ophthalmic imaging provides a way to diagnose and objectively assess the progression of a number of pathologies including neovascular (“wet” age-related macular degeneration (wet AMD and diabetic retinopathy. Two methods of imaging are commonly used: digital photographs of the fundus (the ‘back’ of the eye and Optical Coherence Tomography (OCT, a modality that uses light waves in a similar way to how ultrasound uses sound waves. Changes in population demographics and expectations and the changing pattern of chronic diseases creates a rising demand for such imaging. Meanwhile, interrogation of such images is time consuming, costly, and prone to human error. The application of novel analysis methods may provide a solution to these challenges.   This research will focus on applying novel machine learning algorithms to automatic analysis of both digital fundus photographs and OCT in Moorfields Eye Hospital NHS Foundation Trust patients.   Through analysis of the images used in ophthalmology, along with relevant clinical and demographic information, Google DeepMind Health will investigate the feasibility of automated grading of digital fundus photographs and OCT and provide novel quantitative measures for specific disease features and for monitoring the therapeutic success.

  9. Machine vision and mechatronics in practice

    CERN Document Server

    Brett, Peter

    2015-01-01

    The contributions for this book have been gathered over several years from conferences held in the series of Mechatronics and Machine Vision in Practice, the latest of which was held in Ankara, Turkey. The essential aspect is that they concern practical applications rather than the derivation of mere theory, though simulations and visualization are important components. The topics range from mining, with its heavy engineering, to the delicate machining of holes in the human skull or robots for surgery on human flesh. Mobile robots continue to be a hot topic, both from the need for navigation and for the task of stabilization of unmanned aerial vehicles. The swinging of a spray rig is damped, while machine vision is used for the control of heating in an asphalt-laying machine.  Manipulators are featured, both for general tasks and in the form of grasping fingers. A robot arm is proposed for adding to the mobility scooter of the elderly. Can EEG signals be a means to control a robot? Can face recognition be ac...

  10. Diagnostics and Control of Natural Gas-Fired furnaces via Flame Image Analysis using Machine Vision & Artificial Intelligence Techniques

    Energy Technology Data Exchange (ETDEWEB)

    Shahla Keyvan

    2005-12-01

    A new approach for the detection of real-time properties of flames is used in this project to develop improved diagnostics and controls for natural gas fired furnaces. The system utilizes video images along with advanced image analysis and artificial intelligence techniques to provide virtual sensors in a stand-alone expert shell environment. One of the sensors is a flame sensor encompassing a flame detector and a flame analyzer to provide combustion status. The flame detector can identify any burner that has not fired in a multi-burner furnace. Another sensor is a 3-D temperature profiler. One important aspect of combustion control is product quality. The 3-D temperature profiler of this on-line system is intended to provide a tool for a better temperature control in a furnace to improve product quality. In summary, this on-line diagnostic and control system offers great potential for improving furnace thermal efficiency, lowering NOx and carbon monoxide emissions, and improving product quality. The system is applicable in natural gas-fired furnaces in the glass industry and reheating furnaces used in steel and forging industries.

  11. Machine Learning for Vision-Based Motion Analysis

    CERN Document Server

    Wang, Liang; Cheng, Li; Pietikainen, Matti

    2011-01-01

    Techniques of vision-based motion analysis aim to detect, track, identify, and generally understand the behavior of objects in image sequences. With the growth of video data in a wide range of applications from visual surveillance to human-machine interfaces, the ability to automatically analyze and understand object motions from video footage is of increasing importance. Among the latest developments in this field is the application of statistical machine learning algorithms for object tracking, activity modeling, and recognition. Developed from expert contributions to the first and second In

  12. Bioinspired minimal machine multiaperture apposition vision system.

    Science.gov (United States)

    Davis, John D; Barrett, Steven F; Wright, Cameron H G; Wilcox, Michael

    2008-01-01

    Traditional machine vision systems have an inherent data bottleneck that arises because data collected in parallel must be serialized for transfer from the sensor to the processor. Furthermore, much of this data is not useful for information extraction. This project takes inspiration from the visual system of the house fly, Musca domestica, to reduce this bottleneck by employing early (up front) analog preprocessing to limit the data transfer. This is a first step toward an all analog, parallel vision system. While the current implementation has serial stages, nothing would prevent it from being fully parallel. A one-dimensional photo sensor array with analog pre-processing is used as the sole sensory input to a mobile robot. The robot's task is to chase a target car while avoiding obstacles in a constrained environment. Key advantages of this approach include passivity and the potential for very high effective "frame rates."

  13. Machine Vision Implementation in Rapid PCB Prototyping

    Directory of Open Access Journals (Sweden)

    Yosafat Surya Murijanto

    2012-03-01

    Full Text Available Image processing, the heart of machine vision, has proven itself to be an essential part of the industries today. Its application has opened new doorways, making more concepts in manufacturing processes viable. This paper presents an application of machine vision in designing a module with the ability to extract drills and route coordinates from an un-mounted or mounted printed circuit board (PCB. The algorithm comprises pre-capturing processes, image segmentation and filtering, edge and contour detection, coordinate extraction, and G-code creation. OpenCV libraries and Qt IDE are the main tools used. Throughout some testing and experiments, it is concluded that the algorithm is able to deliver acceptable results. The drilling and routing coordinate extraction algorithm can extract in average 90% and 82% of the whole drills and routes available on the scanned PCB in a total processing time of less than 3 seconds. This is achievable through proper lighting condition, good PCB surface condition and good webcam quality. 

  14. 机械制造自动化的机器视觉技术应用%Machinery Manufacturing Automation Application of Machine Vision Technique

    Institute of Scientific and Technical Information of China (English)

    支月蓉

    2015-01-01

    In machinery manufacturing industry,video technology and infrared imaging technology have been widely applied. The machine vision technology is a technology which uses computer technology to simulate visual system of human beings and to interpret images instead of supervisor.Depending on its advantages such as quick processing speed,large information processing, full functions and so on,machine vision technology gets developed along with the process of facilitating automation level.%在机械制造行业,视频技术和红外成像技术得到了广泛的应用.机器视觉技术是由计算机技术来模拟人的视觉系统,代替原有的监控人员进行图像理解.凭借其运行速度较快,信息处理量大,以及功能齐全等诸多优点,机器视觉技术在推动自动化水平前进的同时自身也得到了良好的发展.

  15. A Machine Vision System for Ball Grid Array Package Inspection

    Institute of Scientific and Technical Information of China (English)

    XIA Nian-jiong; CAO Qi-xin; LEE Jey

    2005-01-01

    An optical inspection method of the Ball Grid Array package (BGA) was proposed by using a machine vision system. The developed machine vision system could get main critical factors for BGA quality evaluation, such as the height of solder ball, diameter, pitch and coplanarity. The experiment has proved that this system is available for BGA failure detection.

  16. A Machine Vision System for Automatically Grading Hardwood Lumber - (Proceedings)

    Science.gov (United States)

    Richard W. Conners; Tai-Hoon Cho; Chong T. Ng; Thomas H. Drayer; Joe G. Tront; Philip A. Araman; Robert L. Brisbon

    1990-01-01

    Any automatic system for grading hardwood lumber can conceptually be divided into two components. One of these is a machine vision system for locating and identifying grading defects. The other is an automatic grading program that accepts as input the output of the machine vision system and, based on these data, determines the grade of a board. The progress that has...

  17. Machine-vision based optofluidic cell sorting

    DEFF Research Database (Denmark)

    Glückstad, Jesper; Bañas, Andrew

    In contemporary life science there is an increasing emphasis on sorting rare disease-indicating cells within small dilute quantities such as in the confines of optofluidic lab-on-chip devices. Our approach to this is based on the use of optical forces to isolate red blood cells detected by advanc...... the available light and creating 2D or 3D beam distributions aimed at the positions of the detected cells. Furthermore, the beam shaping freedom provided by GPC can allow optimizations in the beam’s propagation and its interaction with the laser catapulted and sorted cells....... machine vision1. This approach is gentler, less invasive and more economical compared to conventional FACS-systems. As cells are less responsive to plastic or glass objects commonly used in the optical manipulation literature2, and since laser safety would be an issue in clinical use, we develop efficient...

  18. Machine learning, computer vision, and probabilistic models in jet physics

    CERN Document Server

    CERN. Geneva; NACHMAN, Ben

    2015-01-01

    In this talk we present recent developments in the application of machine learning, computer vision, and probabilistic models to the analysis and interpretation of LHC events. First, we will introduce the concept of jet-images and computer vision techniques for jet tagging. Jet images enabled the connection between jet substructure and tagging with the fields of computer vision and image processing for the first time, improving the performance to identify highly boosted W bosons with respect to state-of-the-art methods, and providing a new way to visualize the discriminant features of different classes of jets, adding a new capability to understand the physics within jets and to design more powerful jet tagging methods. Second, we will present Fuzzy jets: a new paradigm for jet clustering using machine learning methods. Fuzzy jets view jet clustering as an unsupervised learning task and incorporate a probabilistic assignment of particles to jets to learn new features of the jet structure. In particular, we wi...

  19. Machine vision for airport runway identification

    Science.gov (United States)

    Schubert, Matthew; Moore, Andrew J.; Dolph, Chester; Woodell, Glenn

    2015-03-01

    For rigid objects and fixed scenes, current machine vision technology is capable of identifying imagery rapidly and with specificity over a modest range of camera viewpoints and scene illumination. We applied that capability to the problem of runway identification using video of sixteen runway approaches at nine locations, subject to two simplifying assumptions. First, by using approach video from just one of the several possible seasonal variations (no snow cover and full foliage), we artificially removed one source of scene variation in this study. Secondly, by not using approach video at dawn and dusk, we limited the study to two illumination variants (day and night). We did allow scene variation due to atmospheric turbidity by using approach video from rainy and foggy days in some daytime approaches. With suitable ensemble statistics to account for temporal continuity in video, we observed high location specificity (<90% Bayesian posterior probability). We also tested repeatability, i.e., identification of a given runway across multiple videos, and observed robust repeatability only if illumination (day vs. night) was the same and approach visibility was good. Both specificity and repeatability degraded in poor weather conditions. The results of this simplified study show that geolocation via real-time comparison of cockpit image sensor video to a database of runway approach imagery is feasible, as long as the database contains imagery from about the same time of day (complete daylight and nighttime, excluding dawn and dusk) and the weather is clear at the time of the flight.

  20. Industrial Inspection with Open Eyes: Advance with Machine Vision Technology

    Energy Technology Data Exchange (ETDEWEB)

    Liu, Zheng; Ukida, H.; Niel, Kurt; Ramuhalli, Pradeep

    2015-10-01

    Machine vision systems have evolved significantly with the technology advances to tackle the challenges from modern manufacturing industry. A wide range of industrial inspection applications for quality control are benefiting from visual information captured by different types of cameras variously configured in a machine vision system. This chapter screens the state of the art in machine vision technologies in the light of hardware, software tools, and major algorithm advances for industrial inspection. The inspection beyond visual spectrum offers a significant complementary to the visual inspection. The combination with multiple technologies makes it possible for the inspection to achieve a better performance and efficiency in varied applications. The diversity of the applications demonstrates the great potential of machine vision systems for industry.

  1. Machine Vision Systems for Processing Hardwood Lumber and Logs

    Science.gov (United States)

    Philip A. Araman; Daniel L. Schmoldt; Tai-Hoon Cho; Dongping Zhu; Richard W. Conners; D. Earl Kline

    1992-01-01

    Machine vision and automated processing systems are under development at Virginia Tech University with support and cooperation from the USDA Forest Service. Our goals are to help U.S. hardwood producers automate, reduce costs, increase product volume and value recovery, and market higher value, more accurately graded and described products. Any vision system is...

  2. Learning surface molecular structures via machine vision

    Science.gov (United States)

    Ziatdinov, Maxim; Maksov, Artem; Kalinin, Sergei V.

    2017-08-01

    Recent advances in high resolution scanning transmission electron and scanning probe microscopies have allowed researchers to perform measurements of materials structural parameters and functional properties in real space with a picometre precision. In many technologically relevant atomic and/or molecular systems, however, the information of interest is distributed spatially in a non-uniform manner and may have a complex multi-dimensional nature. One of the critical issues, therefore, lies in being able to accurately identify (`read out') all the individual building blocks in different atomic/molecular architectures, as well as more complex patterns that these blocks may form, on a scale of hundreds and thousands of individual atomic/molecular units. Here we employ machine vision to read and recognize complex molecular assemblies on surfaces. Specifically, we combine Markov random field model and convolutional neural networks to classify structural and rotational states of all individual building blocks in molecular assembly on the metallic surface visualized in high-resolution scanning tunneling microscopy measurements. We show how the obtained full decoding of the system allows us to directly construct a pair density function—a centerpiece in analysis of disorder-property relationship paradigm—as well as to analyze spatial correlations between multiple order parameters at the nanoscale, and elucidate reaction pathway involving molecular conformation changes. The method represents a significant shift in our way of analyzing atomic and/or molecular resolved microscopic images and can be applied to variety of other microscopic measurements of structural, electronic, and magnetic orders in different condensed matter systems.

  3. Applications of AI, machine vision and robotics

    CERN Document Server

    Boyer, Kim; Bunke, H

    1995-01-01

    This text features a broad array of research efforts in computer vision including low level processing, perceptual organization, object recognition and active vision. The volume's nine papers specifically report on topics such as sensor confidence, low level feature extraction schemes, non-parametric multi-scale curve smoothing, integration of geometric and non-geometric attributes for object recognition, design criteria for a four degree-of-freedom robot head, a real-time vision system based on control of visual attention and a behavior-based active eye vision system. The scope of the book pr

  4. Standard machine vision systems used in different industrial applications

    Science.gov (United States)

    Bruehl, Wolfgang

    1993-12-01

    Fully standardized machine vision systems won't require task specific hard- or software development. This allows short project realization times at minimized cost. This paper describes two very different applications which were realized only by menu-guided configuration of the QueCheck standard machine vision system. The first is an in-line survey of oilpump castings necessary to protect the following working machine from being damaged by castings not according to the specified geometrical measures. The second application shows the replacement of time consuming manual particle size analysis of fertilizer pellets, by a continuous analysis with a vision system. At the same time the data of the vision system can be used to optimize particle size during production.

  5. Characterization of oats (Avena sativa L.) cultivars using machine vision.

    Science.gov (United States)

    Sumathi, S; Balamurugan, P

    2013-10-15

    Machine vision or image analysis is an important tool in the study of morphology of any materials. This technique has been used successfully to differentiate the eleven oats cultivars based on morphological characters. The geometry of seeds was measured through image analyzer and the variation was observed and recorded. From the recorded data, the cluster analysis was carried out and it revealed that the cultivars could be grouped into two main clusters based on similarity in the measured parameters. Cultivar Sabzar, UPO 212, OL 9 and OL 88 formed one main cluster. The another main cluster includes cv. Kent, OS 6, UPO 94, HFO 114, OS 7, HJ 8 and JHO 822 with many sub clusters. Among the cultivars HJ 8 and JHO 822 has more similarity in all measured parameters than other cultivars. Thus morphological characterization through seed image analysis was found useful to discriminate the cultivars.

  6. Trends and developments in industrial machine vision: 2013

    Science.gov (United States)

    Niel, Kurt; Heinzl, Christoph

    2014-03-01

    When following current advancements and implementations in the field of machine vision there seems to be no borders for future developments: Calculating power constantly increases, and new ideas are spreading and previously challenging approaches are introduced in to mass market. Within the past decades these advances have had dramatic impacts on our lives. Consumer electronics, e.g. computers or telephones, which once occupied large volumes, now fit in the palm of a hand. To note just a few examples e.g. face recognition was adopted by the consumer market, 3D capturing became cheap, due to the huge community SW-coding got easier using sophisticated development platforms. However, still there is a remaining gap between consumer and industrial applications. While the first ones have to be entertaining, the second have to be reliable. Recent studies (e.g. VDMA [1], Germany) show a moderately increasing market for machine vision in industry. Asking industry regarding their needs the main challenges for industrial machine vision are simple usage and reliability for the process, quick support, full automation, self/easy adjustment at changing process parameters, "forget it in the line". Furthermore a big challenge is to support quality control: Nowadays the operator has to accurately define the tested features for checking the probes. There is an upcoming development also to let automated machine vision applications find out essential parameters in a more abstract level (top down). In this work we focus on three current and future topics for industrial machine vision: Metrology supporting automation, quality control (inline/atline/offline) as well as visualization and analysis of datasets with steadily growing sizes. Finally the general trend of the pixel orientated towards object orientated evaluation is addressed. We do not directly address the field of robotics taking advances from machine vision. This is actually a fast changing area which is worth an own

  7. Direction Identification System of Garlic Clove Based on Machine Vision

    Directory of Open Access Journals (Sweden)

    Gao Chi

    2013-05-01

    Full Text Available In order to fulfill the requirements of seeding direction of garlic cloves, the paper proposed a research method of garlic clove direction identification based on machine vision, it expounded the theory of garlic clove direction identification, stated the arithmetic of it, designed the direction identification device of it, then developed the control system of garlic clove direction identification based on machine vision, at last tested the garlic clove direction identification, and the result of the experiment certificated that the rate of garlic clove direction identification could reach to more than 97%, and it demonstrated that the research is of high feasibility and technological values.

  8. Machine Vision For Industrial Control:The Unsung Opportunity

    Science.gov (United States)

    Falkman, Gerald A.; Murray, Lawrence A.; Cooper, James E.

    1984-05-01

    Vision modules have primarily been developed to relieve those pressures newly brought into existence by Inspection (QUALITY) and Robotic (PRODUCTIVITY) mandates. Industrial Control pressure stems on the other hand from the older first industrial revolution mandate of throughput. Satisfying such pressure calls for speed in both imaging and decision making. Vision companies have, however, put speed on a backburner or ignore it entirely because most modules are computer/software based which limits their speed potential. Increasingly, the keynote being struck at machine vision seminars is that "Visual and Computational Speed Must Be Increased and Dramatically!" There are modular hardwired-logic systems that are fast but, all too often, they are not very bright. Such units: Measure the fill factor of bottles as they spin by, Read labels on cans, Count stacked plastic cups or Monitor the width of parts streaming past the camera. Many are only a bit more complex than a photodetector. Once in place, most of these units are incapable of simple upgrading to a new task and are Vision's analog to the robot industry's pick and place (RIA TYPE E) robot. Vision thus finds itself amidst the same quandries that once beset the Robot Industry of America when it tried to define a robot, excluded dumb ones, and was left with only slow machines whose unit volume potential is shatteringly low. This paper develops an approach to meeting the need of a vision system that cuts a swath into the terra incognita of intelligent, high-speed vision processing. Main attention is directed to vision for industrial control. Some presently untapped vision application areas that will be serviced include: Electronics, Food, Sports, Pharmaceuticals, Machine Tools and Arc Welding.

  9. Building Artificial Vision Systems with Machine Learning

    Energy Technology Data Exchange (ETDEWEB)

    LeCun, Yann [New York University

    2011-02-23

    Three questions pose the next challenge for Artificial Intelligence (AI), robotics, and neuroscience. How do we learn perception (e.g. vision)? How do we learn representations of the perceptual world? How do we learn visual categories from just a few examples?

  10. Machine vision-based high-resolution weed mapping and patch-sprayer performance simulation

    NARCIS (Netherlands)

    Tang, L.; Tian, L.F.; Steward, B.L.

    1999-01-01

    An experimental machine vision-based patch-sprayer was developed. This sprayer was primarily designed to do real-time weed density estimation and variable herbicide application rate control. However, the sprayer also had the capability to do high-resolution weed mapping if proper mapping techniques

  11. Machine vision for a selective broccoli harvesting robot

    NARCIS (Netherlands)

    Blok, Pieter M.; Barth, Ruud; Berg, Van Den Wim

    2016-01-01

    The selective hand-harvest of fresh market broccoli is labor-intensive and comprises about 35% of the total production costs. This research was conducted to determine whether machine vision can be used to detect broccoli heads, as a first step in the development of a fully autonomous selective harve

  12. Design and construction of automatic sorting station with machine vision

    Directory of Open Access Journals (Sweden)

    Oscar D. Velasco-Delgado

    2014-01-01

    Full Text Available This article presents the design, construction and testing of an automatic product sorting system in belt conveyor with machine vision that integrates Free and Open Source Software technology and Allen Bradley commercial equipment. Requirements are defined to determine features such as: mechanics of manufacturing station, an app of product sorting with machine vision and for automation system. For the app of machine vision a library is used for optical digital image processing Open CV, for the mechanical design of the manufacturing station is used the CAD tool Solid Edge and for the design and implementation of automation ISA standards are used along with an automation engineering project methodology integrating a PLC, an inverter, a Panel View and a DeviceNet Network. Performance tests are shown by classifying bottles and PVC pieces in four established types, the behavior of the integrated system is checked so as the efficiency of the same. The processing time on machine vision is 0.290 s on average for a piece of PVC, a capacity of 206 accessories per minute, for bottles was obtained a processing time of 0.267 s, a capacity of 224 bottles per minute. A maximum mechanical performance is obtained with 32 products per minute (1920 products/hour with the conveyor to 22 cm/s and 40 cm of distance between products obtaining an average error of 0.8%.

  13. Machine-Vision Systems Selection for Agricultural Vehicles: A Guide

    Directory of Open Access Journals (Sweden)

    Gonzalo Pajares

    2016-11-01

    Full Text Available Machine vision systems are becoming increasingly common onboard agricultural vehicles (autonomous and non-autonomous for different tasks. This paper provides guidelines for selecting machine-vision systems for optimum performance, considering the adverse conditions on these outdoor environments with high variability on the illumination, irregular terrain conditions or different plant growth states, among others. In this regard, three main topics have been conveniently addressed for the best selection: (a spectral bands (visible and infrared; (b imaging sensors and optical systems (including intrinsic parameters and (c geometric visual system arrangement (considering extrinsic parameters and stereovision systems. A general overview, with detailed description and technical support, is provided for each topic with illustrative examples focused on specific applications in agriculture, although they could be applied in different contexts other than agricultural. A case study is provided as a result of research in the RHEA (Robot Fleets for Highly Effective Agriculture and Forestry Management project for effective weed control in maize fields (wide-rows crops, funded by the European Union, where the machine vision system onboard the autonomous vehicles was the most important part of the full perception system, where machine vision was the most relevant. Details and results about crop row detection, weed patches identification, autonomous vehicle guidance and obstacle detection are provided together with a review of methods and approaches on these topics.

  14. Close range photogrammetry and machine vision

    CERN Document Server

    Atkinson, KB

    1996-01-01

    This book presents the methodology, algorithms, techniques and equipment necessary to achieve real time digital photogrammetric solutions, together with contemporary examples of close range photogrammetry.

  15. Research on Manufacturing Technology Based on Machine Vision

    Institute of Scientific and Technical Information of China (English)

    HU Zhanqi; ZHENG Kuijing

    2006-01-01

    The concept of machine vision based manufacturing technology is proposed first, and the key algorithms used in two-dimensional and three-dimensional machining are discussed in detail. Machining information can be derived from the binary images and gray picture after processing and transforming the picture. Contour and the parallel cutting method about two-dimensional machining are proposed. Polygon approximating algorithm is used to cutting the profile of the workpiece. Fill Scanning algorithm used to machining inner part of a pocket. The improved Shape From Shading method with adaptive pre-processing is adopted to reconstruct the three-dimensional model. Layer cutting method is adopted for three-dimensional machining. The tool path is then gotten from the model, and NC code is formed subsequently. The model can be machined conveniently by the lathe, milling machine or engraver. Some examples are given to demonstrate the results of ImageCAM system, which is developed by the author to implement the algorithms previously mentioned.

  16. Research and application of visual location technology for solder paste printing based on machine vision

    Institute of Scientific and Technical Information of China (English)

    Luosi WEI; Zongxia JIAO

    2009-01-01

    A location system is very important for solder paste printing in the process of surface mount technology (SMT). Using machine vision technology to complete the location mission is new and very efficient. This paper presents an integrated visual location system for solder paste printing based on machine vision. The working principle of solder paste printing is introduced and then the design and implementation of the visual location system are described. In the system, two key techniques are completed by secondary development based on VisionPro.One is accurate image location solved by the pattern-based location algorithms of PatMax. The other one is camera calibration that is achieved by image warping technology through the checkerboard plate. Moreover, the system can provide good performances such as high image locating accuracy with 1/40 sub-pixels, high anti-jamming, and high-speed location of objects whose appearance is rotated, scaled, and/or stretched.

  17. Two dimensional convolute integers for machine vision and image recognition

    Science.gov (United States)

    Edwards, Thomas R.

    1988-01-01

    Machine vision and image recognition require sophisticated image processing prior to the application of Artificial Intelligence. Two Dimensional Convolute Integer Technology is an innovative mathematical approach for addressing machine vision and image recognition. This new technology generates a family of digital operators for addressing optical images and related two dimensional data sets. The operators are regression generated, integer valued, zero phase shifting, convoluting, frequency sensitive, two dimensional low pass, high pass and band pass filters that are mathematically equivalent to surface fitted partial derivatives. These operators are applied non-recursively either as classical convolutions (replacement point values), interstitial point generators (bandwidth broadening or resolution enhancement), or as missing value calculators (compensation for dead array element values). These operators show frequency sensitive feature selection scale invariant properties. Such tasks as boundary/edge enhancement and noise or small size pixel disturbance removal can readily be accomplished. For feature selection tight band pass operators are essential. Results from test cases are given.

  18. Measurement System of Quartz Mound's Vertical Status Based on Machine Vision Technique%基于机器视觉技术的石英砣垂直状态测量系统

    Institute of Scientific and Technical Information of China (English)

    邓彬; 于洪明; 王明昱; 李硕

    2014-01-01

    This paper introduces an auto inspection system of quartz mound's vertical status based on machine vision technique. The system of image acquisition, processing and controlling is composed of image sensors of array CCD (type:MINTRON 220X) and image acquisition card ( type: OKMC10A) . The system overcomes disadvantages of inefficiency by personal inspection and the low-level of precision. The accuracy of measurement, production efficiency and automaticity are improved greatly by this system.%介绍了基于机器视觉技术的石英砣垂直状态自动检测系统。以面阵CCD 型号为MINTRON 220 X 及OKMC10 A采集卡为核心器件构成的图像采集、处理及控制系统,克服了人工视觉测量效率低、精度不高等缺点,大大提高了企业的生产效率和自动化程度。

  19. Collection of Group Characteristics of Pleurotus Eryngii Using Machine Vision

    Science.gov (United States)

    Wang, Yunsheng; Wan, Changzhao; Yang, Juan; Chen, Jianlin; Yuan, Tao; Zhao, Jingyin

    An information collection system which was used to group characteristics of pleurotus eryngii was introduced. The group characteristics of pleurotus eryngii were quantified using machine vision in order to inspect and control the pleurotus eryngii house environment by an automated system. Its main contents include the following: collection of pleurotus eryngii image; image processing and pattern recognition. Finally, by analysing pleurotus eryngii image, the systems for group characteristics of pleurotus eryngii are proved to be greatly effective.

  20. Machine vision automated visual inspection theory, practice and applications

    CERN Document Server

    Beyerer, Jürgen; Frese, Christian

    2016-01-01

    The book offers a thorough introduction to machine vision. It is organized in two parts. The first part covers the image acquisition, which is the crucial component of most automated visual inspection systems. All important methods are described in great detail and are presented with a reasoned structure. The second part deals with the modeling and processing of image signals and pays particular regard to methods, which are relevant for automated visual inspection.

  1. Software architecture for time-constrained machine vision applications

    Science.gov (United States)

    Usamentiaga, Rubén; Molleda, Julio; García, Daniel F.; Bulnes, Francisco G.

    2013-01-01

    Real-time image and video processing applications require skilled architects, and recent trends in the hardware platform make the design and implementation of these applications increasingly complex. Many frameworks and libraries have been proposed or commercialized to simplify the design and tuning of real-time image processing applications. However, they tend to lack flexibility, because they are normally oriented toward particular types of applications, or they impose specific data processing models such as the pipeline. Other issues include large memory footprints, difficulty for reuse, and inefficient execution on multicore processors. We present a novel software architecture for time-constrained machine vision applications that addresses these issues. The architecture is divided into three layers. The platform abstraction layer provides a high-level application programming interface for the rest of the architecture. The messaging layer provides a message-passing interface based on a dynamic publish/subscribe pattern. A topic-based filtering in which messages are published to topics is used to route the messages from the publishers to the subscribers interested in a particular type of message. The application layer provides a repository for reusable application modules designed for machine vision applications. These modules, which include acquisition, visualization, communication, user interface, and data processing, take advantage of the power of well-known libraries such as OpenCV, Intel IPP, or CUDA. Finally, the proposed architecture is applied to a real machine vision application: a jam detector for steel pickling lines.

  2. Practical guide to machine vision software an introduction with LabVIEW

    CERN Document Server

    Kwon, Kye-Si

    2014-01-01

    For both students and engineers in R&D, this book explains machine vision in a concise, hands-on way, using the Vision Development Module of the LabView software by National Instruments. Following a short introduction to the basics of machine vision and the technical procedures of image acquisition, the book goes on to guide readers in the use of the various software functions of LabView's machine vision module. It covers typical machine vision tasks, including particle analysis, edge detection, pattern and shape matching, dimension measurements as well as optical character recognition, enabli

  3. Measurement Error with Different Computer Vision Techniques

    Science.gov (United States)

    Icasio-Hernández, O.; Curiel-Razo, Y. I.; Almaraz-Cabral, C. C.; Rojas-Ramirez, S. R.; González-Barbosa, J. J.

    2017-09-01

    The goal of this work is to offer a comparative of measurement error for different computer vision techniques for 3D reconstruction and allow a metrological discrimination based on our evaluation results. The present work implements four 3D reconstruction techniques: passive stereoscopy, active stereoscopy, shape from contour and fringe profilometry to find the measurement error and its uncertainty using different gauges. We measured several dimensional and geometric known standards. We compared the results for the techniques, average errors, standard deviations, and uncertainties obtaining a guide to identify the tolerances that each technique can achieve and choose the best.

  4. MEASUREMENT ERROR WITH DIFFERENT COMPUTER VISION TECHNIQUES

    Directory of Open Access Journals (Sweden)

    O. Icasio-Hernández

    2017-09-01

    Full Text Available The goal of this work is to offer a comparative of measurement error for different computer vision techniques for 3D reconstruction and allow a metrological discrimination based on our evaluation results. The present work implements four 3D reconstruction techniques: passive stereoscopy, active stereoscopy, shape from contour and fringe profilometry to find the measurement error and its uncertainty using different gauges. We measured several dimensional and geometric known standards. We compared the results for the techniques, average errors, standard deviations, and uncertainties obtaining a guide to identify the tolerances that each technique can achieve and choose the best.

  5. Machine Vision Applied to Navigation of Confined Spaces

    Science.gov (United States)

    Briscoe, Jeri M.; Broderick, David J.; Howard, Ricky; Corder, Eric L.

    2004-01-01

    The reliability of space related assets has been emphasized after the second loss of a Space Shuttle. The intricate nature of the hardware being inspected often requires a complete disassembly to perform a thorough inspection which can be difficult as well as costly. Furthermore, it is imperative that the hardware under inspection not be altered in any other manner than that which is intended. In these cases the use of machine vision can allow for inspection with greater frequency using less intrusive methods. Such systems can provide feedback to guide, not only manually controlled instrumentation, but autonomous robotic platforms as well. This paper serves to detail a method using machine vision to provide such sensing capabilities in a compact package. A single camera is used in conjunction with a projected reference grid to ascertain precise distance measurements. The design of the sensor focuses on the use of conventional components in an unconventional manner with the goal of providing a solution for systems that do not require or cannot accommodate more complex vision systems.

  6. Multimedia extensions to prototyping software for machine vision

    Science.gov (United States)

    Batchelor, Bruce G.; Griffiths, Eric C.; Hack, Ralf; Jones, Andrew C.

    1996-10-01

    PIP (prolog image processing) is a prototyping tool, intended to assists designers of intelligent industrial machine vision systems. This article concentrates on the multi-media extensions to PIP, including: 1) on-line HELP, which allows the user to satisfy PIP goals from within the HELP facility, 2) lighting advisor, which gives advice to a vision engineer about which lighting/viewing arrangement is appropriate to use in a given situation, 3) device control, for operating a robot work cell, 4) speech input and (simple) natural language understanding, 5) speech synthesis, 6) remote operation of PIP via a local area network, and 7) remote operation of PIP via a local area network. At the time of writing, on-line access to PIP, via the Internet, is being developed.

  7. Machine Vision-Based Measurement Systems for Fruit and Vegetable Quality Control in Postharvest.

    Science.gov (United States)

    Blasco, José; Munera, Sandra; Aleixos, Nuria; Cubero, Sergio; Molto, Enrique

    2017-03-14

    Individual items of any agricultural commodity are different from each other in terms of colour, shape or size. Furthermore, as they are living thing, they change their quality attributes over time, thereby making the development of accurate automatic inspection machines a challenging task. Machine vision-based systems and new optical technologies make it feasible to create non-destructive control and monitoring tools for quality assessment to ensure adequate accomplishment of food standards. Such systems are much faster than any manual non-destructive examination of fruit and vegetable quality, thus allowing the whole production to be inspected with objective and repeatable criteria. Moreover, current technology makes it possible to inspect the fruit in spectral ranges beyond the sensibility of the human eye, for instance in the ultraviolet and near-infrared regions. Machine vision-based applications require the use of multiple technologies and knowledge, ranging from those related to image acquisition (illumination, cameras, etc.) to the development of algorithms for spectral image analysis. Machine vision-based systems for inspecting fruit and vegetables are targeted towards different purposes, from in-line sorting into commercial categories to the detection of contaminants or the distribution of specific chemical compounds on the product's surface. This chapter summarises the current state of the art in these techniques, starting with systems based on colour images for the inspection of conventional colour, shape or external defects and then goes on to consider recent developments in spectral image analysis for internal quality assessment or contaminant detection.

  8. Machine vision system for automated detection of stained pistachio nuts

    Science.gov (United States)

    Pearson, Tom C.

    1995-01-01

    A machine vision system was developed to separate stained pistachio nuts, which comprise of about 5% of the California crop, from unstained nuts. The system may be used to reduce labor involved with manual grading or to remove aflatoxin contaminated product from low grade process streams. The system was tested on two different pistachio process streams: the bi- chromatic color sorter reject stream and the small nut shelling stock stream. The system had a minimum overall error rate of 14% for the bi-chromatic sorter reject stream and 15% for the small shelling stock stream.

  9. Development of machine vision system for PHWR fuel pellet inspection

    Energy Technology Data Exchange (ETDEWEB)

    Kamalesh Kumar, B.; Reddy, K.S.; Lakshminarayana, A.; Sastry, V.S.; Ramana Rao, A.V. [Nuclear Fuel Complex, Hyderabad, Andhra Pradesh (India); Joshi, M.; Deshpande, P.; Navathe, C.P.; Jayaraj, R.N. [Raja Ramanna Centre for Advanced Technology, Indore, Madhya Pradesh (India)

    2008-07-01

    Nuclear Fuel Complex, a constituent of Department of Atomic Energy; India is responsible for manufacturing nuclear fuel in India . Over a million Uranium-di-oxide pellets fabricated per annum need visual inspection . In order to overcome the limitations of human based visual inspection, NFC has undertaken the development of machine vision system. The development involved designing various subsystems viz. mechanical and control subsystem for handling and rotation of fuel pellets, lighting subsystem for illumination, image acquisition system, and image processing system and integration. This paper brings out details of various subsystems and results obtained from the trials conducted. (author)

  10. Accurate measurement method for tube's endpoints based on machine vision

    Science.gov (United States)

    Liu, Shaoli; Jin, Peng; Liu, Jianhua; Wang, Xiao; Sun, Peng

    2017-01-01

    Tubes are used widely in aerospace vehicles, and their accurate assembly can directly affect the assembling reliability and the quality of products. It is important to measure the processed tube's endpoints and then fix any geometric errors correspondingly. However, the traditional tube inspection method is time-consuming and complex operations. Therefore, a new measurement method for a tube's endpoints based on machine vision is proposed. First, reflected light on tube's surface can be removed by using photometric linearization. Then, based on the optimization model for the tube's endpoint measurements and the principle of stereo matching, the global coordinates and the relative distance of the tube's endpoint are obtained. To confirm the feasibility, 11 tubes are processed to remove the reflected light and then the endpoint's positions of tubes are measured. The experiment results show that the measurement repeatability accuracy is 0.167 mm, and the absolute accuracy is 0.328 mm. The measurement takes less than 1 min. The proposed method based on machine vision can measure the tube's endpoints without any surface treatment or any tools and can realize on line measurement.

  11. Accurate Measurement Method for Tube's Endpoints Based on Machine Vision

    Institute of Scientific and Technical Information of China (English)

    LIU Shaoli; JIN Peng; LIU Jianhua; WANG Xiao; SUN Peng

    2017-01-01

    Tubes are used widely in aerospace vehicles,and their accurate assembly can directly affect the assembling reliability and the quality of products.It is important to measure the processed tube's endpoints and then fix any geometric errors correspondingly.However,the traditional tube inspection method is time-consuming and complex operations.Therefore,a new measurement method for a tube's endpoints based on machine vision is proposed.First,reflected light on tube's surface can be removed by using photometric linearization.Then,based on the optimization model for the tube's endpoint measurements and the principle of stereo matching,the global coordinates and the relative distance of the tube's endpoint are obtained.To confirm the feasibility,11 tubes are processed to remove the reflected light and then the endpoint's positions of tubes are measured.The experiment results show that the measurement repeatability accuracy is 0.167 mm,and the absolute accuracy is 0.328 mm.The measurement takes less than 1 min.The proposed method based on machine vision can measure the tube's endpoints without any surface treatment or any tools and can realize on line measurement.

  12. Accurate measurement method for tube's endpoints based on machine vision

    Science.gov (United States)

    Liu, Shaoli; Jin, Peng; Liu, Jianhua; Wang, Xiao; Sun, Peng

    2016-08-01

    Tubes are used widely in aerospace vehicles, and their accurate assembly can directly affect the assembling reliability and the quality of products. It is important to measure the processed tube's endpoints and then fix any geometric errors correspondingly. However, the traditional tube inspection method is time-consuming and complex operations. Therefore, a new measurement method for a tube's endpoints based on machine vision is proposed. First, reflected light on tube's surface can be removed by using photometric linearization. Then, based on the optimization model for the tube's endpoint measurements and the principle of stereo matching, the global coordinates and the relative distance of the tube's endpoint are obtained. To confirm the feasibility, 11 tubes are processed to remove the reflected light and then the endpoint's positions of tubes are measured. The experiment results show that the measurement repeatability accuracy is 0.167 mm, and the absolute accuracy is 0.328 mm. The measurement takes less than 1 min. The proposed method based on machine vision can measure the tube's endpoints without any surface treatment or any tools and can realize on line measurement.

  13. Vision-Based People Detection System for Heavy Machine Applications

    Directory of Open Access Journals (Sweden)

    Vincent Fremont

    2016-01-01

    Full Text Available This paper presents a vision-based people detection system for improving safety in heavy machines. We propose a perception system composed of a monocular fisheye camera and a LiDAR. Fisheye cameras have the advantage of a wide field-of-view, but the strong distortions that they create must be handled at the detection stage. Since people detection in fisheye images has not been well studied, we focus on investigating and quantifying the impact that strong radial distortions have on the appearance of people, and we propose approaches for handling this specificity, adapted from state-of-the-art people detection approaches. These adaptive approaches nevertheless have the drawback of high computational cost and complexity. Consequently, we also present a framework for harnessing the LiDAR modality in order to enhance the detection algorithm for different camera positions. A sequential LiDAR-based fusion architecture is used, which addresses directly the problem of reducing false detections and computational cost in an exclusively vision-based system. A heavy machine dataset was built, and different experiments were carried out to evaluate the performance of the system. The results are promising, in terms of both processing speed and performance.

  14. Real-time machine vision FPGA implementation for microfluidic monitoring on Lab-on-Chips.

    Science.gov (United States)

    Sotiropoulou, Calliope-Louisa; Voudouris, Liberis; Gentsos, Christos; Demiris, Athanasios M; Vassiliadis, Nikolaos; Nikolaidis, Spyridon

    2014-04-01

    A machine vision implementation on a field-programmable gate array (FPGA) device for real-time microfluidic monitoring on Lab-On-Chips is presented in this paper. The machine vision system is designed to follow continuous or plug flows, for which the menisci of the fluids are always visible. The system discriminates between the front or "head" of the flow and the back or "tail" and is able to follow flows with a maximum speed of 20 mm/sec in circular channels of a diameter of 200 μm (corresponding to approx. 60 μl/sec ). It is designed to be part of a complete Point-of-Care system, which will be portable and operate in non-ideal laboratory conditions. Thus, it is able to cope with noise due to lighting conditions and small LoC displacements during the experiment execution. The machine vision system can be used for a variety of LoC devices, without the need for fiducial markers (such as redundancy patterns) for its operation. The underlying application requirements called for a complete hardware implementation. The architecture uses a variety of techniques to improve performance and minimize memory access requirements. The system input is 8 bit grayscale uncompressed video of up to 1 Mpixel resolution. The system uses an operating frequency of 170 Mhz and achieves a computational time of 13.97 ms (worst case), which leads to a throughput of 71.6 fps for 1 Mpixel video resolution.

  15. Machine learning techniques in optical communication

    DEFF Research Database (Denmark)

    Zibar, Darko; Piels, Molly; Jones, Rasmus Thomas

    2015-01-01

    Techniques from the machine learning community are reviewed and employed for laser characterization, signal detection in the presence of nonlinear phase noise, and nonlinearity mitigation. Bayesian filtering and expectation maximization are employed within nonlinear state-space framework...

  16. Machine learning techniques in optical communication

    DEFF Research Database (Denmark)

    Zibar, Darko; Piels, Molly; Jones, Rasmus Thomas

    2016-01-01

    Machine learning techniques relevant for nonlinearity mitigation, carrier recovery, and nanoscale device characterization are reviewed and employed. Markov Chain Monte Carlo in combination with Bayesian filtering is employed within the nonlinear state-space framework and demonstrated for parameter...

  17. Special techniques in ultra-precision machining

    Science.gov (United States)

    Li, Li; Min, Xu; Chen, Dong; Wang, JunHua

    2007-12-01

    As the development of ultra-precision machining, the SPDT (single point diamond turning) was applied for the manufacture of a variety of optical components for its high precision , versatility and lower manufacturing cost. Whereas, the improvement of ultra-precision machining is not only related to the most topnotch equipments in the world but also closely linked to the special techniques in the ultra-precision Machining. Therefore, the industrialization and marketization of the ultra-precision machining will not be realized without these special techniques. This paper introduces the principle, trait and application of some important special techniques which can match the SPDT efficaciously, they are FTS, STS, SSS, ACT, VQ, LADT and UADT techniques.

  18. A Machine Vision System for Automatically Grading Hardwood Lumber - (Industrial Metrology)

    Science.gov (United States)

    Richard W. Conners; Tai-Hoon Cho; Chong T. Ng; Thomas T. Drayer; Philip A. Araman; Robert L. Brisbon

    1992-01-01

    Any automatic system for grading hardwood lumber can conceptually be divided into two components. One of these is a machine vision system for locating and identifying grading defects. The other is an automatic grading program that accepts as input the output of the machine vision system and, based on these data, determines the grade of a board. The progress that has...

  19. Using Multiple FPGA Architectures for Real-time Processing of Low-level Machine Vision Functions

    Science.gov (United States)

    Thomas H. Drayer; William E. King; Philip A. Araman; Joseph G. Tront; Richard W. Conners

    1995-01-01

    In this paper, we investigate the use of multiple Field Programmable Gate Array (FPGA) architectures for real-time machine vision processing. The use of FPGAs for low-level processing represents an excellent tradeoff between software and special purpose hardware implementations. A library of modules that implement common low-level machine vision operations is presented...

  20. FUSION OF MULTI FOCUSED IMAGES USING HDWT FOR MACHINE VISION

    Directory of Open Access Journals (Sweden)

    S. Arumuga Perumal

    2011-10-01

    Full Text Available During image acquisition in machine vision, due to limited depth of field of lens, it is possible to take clear image of the objects in the scene which are in focus only. The remaining objects in the scene will be out of focus. A possible solution to bring clear images of all objects in the scene is image fusion. Image fusion is a process of combining multiple images to form the composite image with extended information content. This paper uses three band expansive higher density discrete wavelet transform to fuse two numbers of images focusing different objects in the same scene and also proposes three methods for image fusion. Experimental results on multi focused image fusion are presented in terms of root mean square, peak signal to noise ratio and quality index to illustrate the proposed fusion methods.

  1. Brake Pedal Displacement Measuring System based on Machine Vision

    Directory of Open Access Journals (Sweden)

    Chang Wang

    2013-10-01

    Full Text Available Displacement of brake pedal was an important characteristic of driving behavior. This paper proposed a displacement measure algorithm based on machine vision. Image of brake pedal was captured by camera from left side, and images were processed in industry computer. Firstly, average smooth algorithm and wavelet transform algorithm were used to smooth the original image consecutively. Then, edge extracting method which combined Roberts’s operator with wavelet analysis was used to identify the edge of brake pedal. At last, least square method was adopted to recognize the characteristic line of brake pedal’s displacement. The experimental results demonstrated that the proposed method takes the advantages of Roberts’s operator and wavelet transform, it can obtain better measurement result as well as linear displacement sensors

  2. Machine vision inspection of rice seed based on Hough transform

    Institute of Scientific and Technical Information of China (English)

    成芳; 应义斌

    2004-01-01

    A machine vision system was developed to inspect the quality of rice seeds. Five varieties of Jinyou402, Shanyou10, Zhongyou207, Jiayou and IIyou were evaluated. The images of both sides of rice seed with black background and white background were acquired with the image processing system for identifying external features of rice seeds. Five image sets consisting of 600 original images each were obtained. Then a digital image processing algorithm based on Hough transform was developed to inspect the rice seeds with incompletely closed glumes. The algorithm was implemented with all image sets using a Matlab 6.5 procedure. The results showed that the algorithm achieved an average accuracy of 96% for normal seeds, 92% for seeds with fine fissure and 87% for seeds with incompletely closed glumes. The algorithm was proved to be applicable to different seed varieties and insensitive to the color of the background.

  3. Machine vision inspection of rice seed based on Hough transform

    Institute of Scientific and Technical Information of China (English)

    成芳; 应义斌

    2004-01-01

    A machine vision system was developed to inspect the quality of rice seeds. Five varieties of Jinyou402,Shanyou 10, Zhongyou207, Jiayou and Ilyou were evaluated. The images of both sides of rice seed with black background and white background were acquired with the image processing system for identifying external features of rice seeds. Five image sets consisting of 600 original images each were obtained. Then a digital image processing algorithm based on Hough transform was developed to inspect the rice seeds with incompletely closed glumes. The algorithm was implemented with all image sets using a Matlab 6.5 procedure. The results showed that the algorithm achieved an average accuracy of 96% for normal seeds, 92% for seeds with fine fissure and 87% for seeds with incompletely closed glumes. The algorithm was proved to be applicable to different seed varieties and insensitive to the color of the background.

  4. Application of Machine Vision to Vehicle Automatic Collision Warning Algorithm

    Institute of Scientific and Technical Information of China (English)

    WANG Jiang-feng; GAO Feng; XU Guo-yan; YAO Sheng-zhuo

    2008-01-01

    Using the new technologies such as information technology, communication technology and electronic control technology, vehicle collision warning system(CWS) can acquire road condition, adjacent vehicle march condition as well as its dynamics performance continuously, then it can forecast the oncoming potential collision and give a warning. Based on the analysis of driver's driving behavior, algorithm's warning norms are determined. Based on warning norms adopting machine vision method, the cooperation collision warning algorithm(CWA) model with multi-input and multi-output is established which is used in supporting vehicle CWS. The CWA is tested using the actual data and the result shows that this algorithm can identify and carry out warning for vehicle collision efficiently, which has important meaning for improving the vehicle travel safety.

  5. Nontraditional manufacturing technique-Nano machining technique based on SPM

    Institute of Scientific and Technical Information of China (English)

    DONG; Shen; YAN; Yongda; SUN; Tao; LIANG; Yingchun; CHENG

    2004-01-01

    Nano machining based on SPM is a novel, nontraditional advanced manufacturing technique. There are three main machining methods based on SPM, i.e.single atom manipulation, surface modification using physical or chemical actions and mechanical scratching. The current development of this technique is summarized. Based on the analysis of mechanical scratching mechanism, a 5 μm micro inflation hole is fabricated on the surface of inertial confinement fusion (ICF) target. The processing technique is optimized. The machining properties of brittle material, single crystal Ge, are investigated. A micro machining system combining SPM and a high accuracy stage is developed. Some 2D and 3D microstructures are fabricated using the system. This method has broad applications in the field of nano machining.

  6. Potential application of machine vision technology to saffron (Crocus sativus L.) quality characterization.

    Science.gov (United States)

    Kiani, Sajad; Minaei, Saeid

    2016-12-01

    Saffron quality characterization is an important issue in the food industry and of interest to the consumers. This paper proposes an expert system based on the application of machine vision technology for characterization of saffron and shows how it can be employed in practical usage. There is a correlation between saffron color and its geographic location of production and some chemical attributes which could be properly used for characterization of saffron quality and freshness. This may be accomplished by employing image processing techniques coupled with multivariate data analysis for quantification of saffron properties. Expert algorithms can be made available for prediction of saffron characteristics such as color as well as for product classification.

  7. The Employment Effects of High-Technology: A Case Study of Machine Vision. Research Report No. 86-19.

    Science.gov (United States)

    Chen, Kan; Stafford, Frank P.

    A case study of machine vision was conducted to identify and analyze the employment effects of high technology in general. (Machine vision is the automatic acquisition and analysis of an image to obtain desired information for use in controlling an industrial activity, such as the visual sensor system that gives eyes to a robot.) Machine vision as…

  8. Principles and techniques for designing precision machines

    Energy Technology Data Exchange (ETDEWEB)

    Hale, L C

    1999-02-01

    This thesis is written to advance the reader's knowledge of precision-engineering principles and their application to designing machines that achieve both sufficient precision and minimum cost. It provides the concepts and tools necessary for the engineer to create new precision machine designs. Four case studies demonstrate the principles and showcase approaches and solutions to specific problems that generally have wider applications. These come from projects at the Lawrence Livermore National Laboratory in which the author participated: the Large Optics Diamond Turning Machine, Accuracy Enhancement of High- Productivity Machine Tools, the National Ignition Facility, and Extreme Ultraviolet Lithography. Although broad in scope, the topics go into sufficient depth to be useful to practicing precision engineers and often fulfill more academic ambitions. The thesis begins with a chapter that presents significant principles and fundamental knowledge from the Precision Engineering literature. Following this is a chapter that presents engineering design techniques that are general and not specific to precision machines. All subsequent chapters cover specific aspects of precision machine design. The first of these is Structural Design, guidelines and analysis techniques for achieving independently stiff machine structures. The next chapter addresses dynamic stiffness by presenting several techniques for Deterministic Damping, damping designs that can be analyzed and optimized with predictive results. Several chapters present a main thrust of the thesis, Exact-Constraint Design. A main contribution is a generalized modeling approach developed through the course of creating several unique designs. The final chapter is the primary case study of the thesis, the Conceptual Design of a Horizontal Machining Center.

  9. A Review of Machine-Vision-Based Analysis of Wireless Capsule Endoscopy Video

    Directory of Open Access Journals (Sweden)

    Yingju Chen

    2012-01-01

    Full Text Available Wireless capsule endoscopy (WCE enables a physician to diagnose a patient's digestive system without surgical procedures. However, it takes 1-2 hours for a gastroenterologist to examine the video. To speed up the review process, a number of analysis techniques based on machine vision have been proposed by computer science researchers. In order to train a machine to understand the semantics of an image, the image contents need to be translated into numerical form first. The numerical form of the image is known as image abstraction. The process of selecting relevant image features is often determined by the modality of medical images and the nature of the diagnoses. For example, there are radiographic projection-based images (e.g., X-rays and PET scans, tomography-based images (e.g., MRT and CT scans, and photography-based images (e.g., endoscopy, dermatology, and microscopic histology. Each modality imposes unique image-dependent restrictions for automatic and medically meaningful image abstraction processes. In this paper, we review the current development of machine-vision-based analysis of WCE video, focusing on the research that identifies specific gastrointestinal (GI pathology and methods of shot boundary detection.

  10. Stereoscopic Machine-Vision System Using Projected Circles

    Science.gov (United States)

    Mackey, Jeffrey R.

    2010-01-01

    A machine-vision system capable of detecting obstacles large enough to damage or trap a robotic vehicle is undergoing development. The system includes (1) a pattern generator that projects concentric circles of laser light forward onto the terrain, (2) a stereoscopic pair of cameras that are aimed forward to acquire images of the circles, (3) a frame grabber and digitizer for acquiring image data from the cameras, and (4) a single-board computer that processes the data. The system is being developed as a prototype of machine- vision systems to enable robotic vehicles ( rovers ) on remote planets to avoid craters, large rocks, and other terrain features that could capture or damage the vehicles. Potential terrestrial applications of systems like this one could include terrain mapping, collision avoidance, navigation of robotic vehicles, mining, and robotic rescue. This system is based partly on the same principles as those of a prior stereoscopic machine-vision system in which the cameras acquire images of a single stripe of laser light that is swept forward across the terrain. However, this system is designed to afford improvements over some of the undesirable features of the prior system, including the need for a pan-and-tilt mechanism to aim the laser to generate the swept stripe, ambiguities in interpretation of the single-stripe image, the time needed to sweep the stripe across the terrain and process the data from many images acquired during that time, and difficulty of calibration because of the narrowness of the stripe. In this system, the pattern generator does not contain any moving parts and need not be mounted on a pan-and-tilt mechanism: the pattern of concentric circles is projected steadily in the forward direction. The system calibrates itself by use of data acquired during projection of the concentric-circle pattern onto a known target representing flat ground. The calibration- target image data are stored in the computer memory for use as a

  11. MACHINE LEARNING TECHNIQUES USED IN BIG DATA

    Directory of Open Access Journals (Sweden)

    STEFANIA LOREDANA NITA

    2016-07-01

    Full Text Available The classical tools used in data analysis are not enough in order to benefit of all advantages of big data. The amount of information is too large for a complete investigation, and the possible connections and relations between data could be missed, because it is difficult or even impossible to verify all assumption over the information. Machine learning is a great solution in order to find concealed correlations or relationships between data, because it runs at scale machine and works very well with large data sets. The more data we have, the more the machine learning algorithm is useful, because it “learns” from the existing data and applies the found rules on new entries. In this paper, we present some machine learning algorithms and techniques used in big data.

  12. Development a Vision Based Seam Tracking System for None Destructive Testing Machines

    Directory of Open Access Journals (Sweden)

    Nasser moradi

    2013-04-01

    Full Text Available The automatic weld seam tracking is an important challenge in None Destructive Testing (NDT systems for welded pipe inspection. In this Study, a machine vision based seam tracker, is developed and implemented, instead of old electro-mechanical system. A novel algorithm based on the weld image centered is presented, to reduce Environment conditions and improve the seam tracking accuracy. The weld seam images are taken by a camera arranged ahead of the machine and the centered is extracted as a parameter to detect the weld position, and offset between this point and central axis is computed and used as control parameter of servomotors. Adaptive multi step segmentation t technique is employed to increase the probable of real edge of the welds and improve the line fitting accuracy. This new approach offers some important technical advantages over the existing solutions to weld seam detection: Its based on natural light and does not need any auxiliary light. The adaptive threshold segmentation technique applied, decrease Environmental lighting condition. Its accurate and stable in real time NDT testing machines. After a series of experiments in real industrial environment, it is demonstrated that accuracy of this method can improve the quality of NDT machines. The average tracking error is 1.5 pixels approximately 0.25mm..

  13. 3-D measuring of engine camshaft based on machine vision

    Science.gov (United States)

    Qiu, Jianxin; Tan, Liang; Xu, Xiaodong

    2008-12-01

    The non-touch 3D measuring based on machine vision is introduced into camshaft precise measuring. Currently, because CCD 3-dimensional measuring can't meet requirements for camshaft's measuring precision, it's necessary to improve its measuring precision. In this paper, we put forward a method to improve the measuring method. A Multi-Character Match method based on the Polygonal Non-regular model is advanced with the theory of Corner Extraction and Corner Matching .This method has solved the problem of the matching difficulty and a low precision. In the measuring process, the use of the Coded marked Point method and Self-Character Match method can bring on this problem. The 3D measuring experiment on camshaft, which based on the Multi-Character Match method of the Polygonal Non-regular model, proves that the normal average measuring precision is increased to a new level less than 0.04mm in the point-clouds photo merge. This measuring method can effectively increase the 3D measuring precision of the binocular CCD.

  14. Prolog-based prototyping software for machine vision

    Science.gov (United States)

    Batchelor, Bruce G.; Hack, Ralf; Jones, Andrew C.

    1996-10-01

    Prolog image processing (PIP) is a multi-media prototyping tool, intended to assist designers of intelligent industrial machine vision systems. This is the latest in a series of prolog-based systems that have been implemented at Cardiff, specifically for this purpose. The software package provides fully integrated facilities for both interactive and programmed image processing, 'smart' documentation, guidance about which lighting/viewing set-up to use, speech/natural language input and speech output. It can also be used to control a range of electro-mechanical devices, such as lamps, cameras, lenses, pneumatic positioning mechanisms, robots, etc., via a low-cost hardware interfacing module. The software runs on a standard computer, with no predecessors in that the image processing is carried out entirely in software. This article concentrates on the design and implementation of the PIP system, and presents programs for two demonstration applications: (a) recognizing a non-picture playing card; (b) recognizing a well laid table place setting.

  15. Broiler weight estimation based on machine vision and artificial neural network.

    Science.gov (United States)

    Amraei, S; Abdanan Mehdizadeh, S; Salari, S

    2017-04-01

    1. Machine vision and artificial neural network (ANN) procedures were used to estimate live body weight of broiler chickens in 30 1-d-old broiler chickens reared for 42 d. 2. Imaging was performed two times daily. To localise chickens within the pen, an ellipse fitting algorithm was used and the chickens' head and tail removed using the Chan-Vese method. 3. The correlations between the body weight and 6 physical extracted features indicated that there were strong correlations between body weight and the 5 features including area, perimeter, convex area, major and minor axis length. 5. According to statistical analysis there was no significant difference between morning and afternoon data over 42 d. 6. In an attempt to improve the accuracy of live weight approximation different ANN techniques, including Bayesian regulation, Levenberg-Marquardt, Scaled conjugate gradient and gradient descent were used. Bayesian regulation with R(2) value of 0.98 was the best network for prediction of broiler weight. 7. The accuracy of the machine vision technique was examined and most errors were less than 50 g.

  16. Soft Computing Techniques in Vision Science

    CERN Document Server

    Yang, Yeon-Mo

    2012-01-01

    This Special Edited Volume is a unique approach towards Computational solution for the upcoming field of study called Vision Science. From a scientific firmament Optics, Ophthalmology, and Optical Science has surpassed an Odyssey of optimizing configurations of Optical systems, Surveillance Cameras and other Nano optical devices with the metaphor of Nano Science and Technology. Still these systems are falling short of its computational aspect to achieve the pinnacle of human vision system. In this edited volume much attention has been given to address the coupling issues Computational Science and Vision Studies.  It is a comprehensive collection of research works addressing various related areas of Vision Science like Visual Perception and Visual system, Cognitive Psychology, Neuroscience, Psychophysics and Ophthalmology, linguistic relativity, color vision etc. This issue carries some latest developments in the form of research articles and presentations. The volume is rich of contents with technical tools ...

  17. Potato Size and Shape Detection Using Machine Vision

    Directory of Open Access Journals (Sweden)

    Liao Guiping

    2015-01-01

    Full Text Available To reduce the error and faster classification by mechanizing in classifying the potato shape and size through machine vision using the extraction of characters procedure to identify the size, and using the shape detection procedure to identify the shape. Test results in potato size detection revealed 40/191 = 0.210mm/pixel as length scale or calibration factor (40/M where 40 is the table tennis ball size (40mm and 191 as image pixels table tennis (M; measurement results revealed that between the algorithm results and the manual measurements, the absolute error was <3mm, while the relative error rate was <4%; and the measurement results based on the ellipse axis length can accurately calculate the actual long axis and short axis of potato. Potato shape detection revealed the analysis of 228 images composed of 114 positive and 114 negatives side, only 2 have been incorrectly classified, mainly because the Extracted ratio (R of the potato image of those two positive and negative images are near 0.67, respectively 0.671887, 0.661063, 0.667604, and 0.67193. The comparison to establish a calibration system method using both basic rectangle and ellipse R ratio methods to detect the potato size and shape, revealed that the basic rectangle method has better effect in the case of fixed place. Moreover, the ellipse axis method was observed to be more stable with an error rate of 7%. Therefore it is recommended that the ellipse axis method should be used to detect the shape of potato for differentiation into round, long cylindrical, and oval shapes, with the accuracy level of 98.8%.

  18. [Development of a new position-recognition system for robotic radiosurgery systems using machine vision].

    Science.gov (United States)

    Mohri, Issai; Umezu, Yoshiyuki; Fukunaga, Junnichi; Tane, Hiroyuki; Nagata, Hironori; Hirashima, Hideaki; Nakamura, Katsumasa; Hirata, Hideki

    2014-08-01

    CyberKnife(®) provides continuous guidance through radiography, allowing instantaneous X-ray images to be obtained; it is also equipped with 6D adjustment for patient setup. Its disadvantage is that registration is carried out just before irradiation, making it impossible to perform stereo-radiography during irradiation. In addition, patient movement cannot be detected during irradiation. In this study, we describe a new registration system that we term "Machine Vision," which subjects the patient to no additional radiation exposure for registration purposes, can be set up promptly, and allows real-time registration during irradiation. Our technique offers distinct advantages over CyberKnife by enabling a safer and more precise mode of treatment. "Machine Vision," which we have designed and fabricated, is an automatic registration system that employs three charge coupled device cameras oriented in different directions that allow us to obtain a characteristic depiction of the shape of both sides of the fetal fissure and external ears in a human head phantom. We examined the degree of precision of this registration system and concluded it to be suitable as an alternative method of registration without radiation exposure when displacement is less than 1.0 mm in radiotherapy. It has potential for application to CyberKnife in clinical treatment.

  19. Nondestructive and rapid detection of potato black heart based on machine vision technology

    Science.gov (United States)

    Tian, Fang; Peng, Yankun; Wei, Wensong

    2016-05-01

    Potatoes are one of the major food crops in the world. Potato black heart is a kind of defect that the surface is intact while the tissues in skin become black. This kind of potato has lost the edibleness, but it's difficult to be detected with conventional methods. A nondestructive detection system based on the machine vision technology was proposed in this study to distinguish the normal and black heart of potatoes according to the different transmittance of them. The detection system was equipped with a monochrome CCD camera, LED light sources for transmitted illumination and a computer. Firstly, the transmission images of normal and black heart potatoes were taken by the detection system. Then the images were processed by algorithm written with VC++. As the transmitted light intensity was influenced by the radial dimension of the potato samples, the relationship between the grayscale value and the potato radial dimension was acquired by analyzing the grayscale value changing rule of the transmission image. Then proper judging condition was confirmed to distinguish the normal and black heart of potatoes after image preprocessing. The results showed that the nondestructive system built coupled with the processing methods was accessible for the detection of potato black heart at a considerable accuracy rate. The transmission detection technique based on machine vision is nondestructive and feasible to realize the detection of potato black heart.

  20. Machine learning techniques and drug design.

    Science.gov (United States)

    Gertrudes, J C; Maltarollo, V G; Silva, R A; Oliveira, P R; Honório, K M; da Silva, A B F

    2012-01-01

    The interest in the application of machine learning techniques (MLT) as drug design tools is growing in the last decades. The reason for this is related to the fact that the drug design is very complex and requires the use of hybrid techniques. A brief review of some MLT such as self-organizing maps, multilayer perceptron, bayesian neural networks, counter-propagation neural network and support vector machines is described in this paper. A comparison between the performance of the described methods and some classical statistical methods (such as partial least squares and multiple linear regression) shows that MLT have significant advantages. Nowadays, the number of studies in medicinal chemistry that employ these techniques has considerably increased, in particular the use of support vector machines. The state of the art and the future trends of MLT applications encompass the use of these techniques to construct more reliable QSAR models. The models obtained from MLT can be used in virtual screening studies as well as filters to develop/discovery new chemicals. An important challenge in the drug design field is the prediction of pharmacokinetic and toxicity properties, which can avoid failures in the clinical phases. Therefore, this review provides a critical point of view on the main MLT and shows their potential ability as a valuable tool in drug design.

  1. Machine learning techniques in dialogue act recognition

    Directory of Open Access Journals (Sweden)

    Mark Fišel

    2007-05-01

    Full Text Available This report addresses dialogue acts, their existing applications and techniques of automatically recognizing them, in Estonia as well as elsewhere. Three main applications are described: in dialogue systems to determine the intention of the speaker, in dialogue systems with machine translation to resolve ambiguities in the possible translation variants and in speech recognition to reduce word recognition error rate. Several recognition techniques are described on the surface level: how they work and how they are trained. A summary of the corresponding representation methods is provided for each technique. The paper also includes examples of applying the techniques to dialogue act recognition.The author comes to the conclusion that using the current evaluation metric it is impossible to compare dialogue act recognition techniques when these are applied to different dialogue act tag sets. Dialogue acts remain an open research area, with space and need for developing new recognition techniques and methods of evaluation.

  2. Anti-shake and coordinate interpolation techniques in machine vision electronic whiteboard system application%机器视觉电子白板系统的防抖与坐标插值技术

    Institute of Scientific and Technical Information of China (English)

    周祖微; 刘森; 王忆文; 李辉

    2012-01-01

    在基于机器视觉的电子白板系统应用中,为了消除各种因素导致的触控点抖动,提出了一种改进的均值滤波的防抖方法.为了突破硬件设备的限制提高系统工作的流畅性,采用了一种基于曲线拟合的坐标插值方法来提高系统实时性并平滑处理触控点的运动轨迹.实验结果表明:触控点的抖动情况得到了消除,在摄像头最高工作频率60fps的情况下,系统能以每秒输出180个触控点坐标的速度实时工作,在不增加硬件成本的情况下提高了系统实时性.%In the electronic whiteboard system based on machine vision, an improved mean filter was proposed to eliminate touching-point jitter. In order to enhance the working fluency without hardware restrictions, a coordinate interpolation based on curve-fitting was adopted to improve the real-time performance of the whole system and smooth the trajectory of moving touching-point. The experimental results show that: on one hand, touching-point jitter can be eliminated. On the other hand, the system can output 180 touching-point coordinates per second when the camera works at its highest speed of 60 frame per second. The real-time performance of the whole system gets effectively improved without any new hardware cost.

  3. Machine Vision for Relative Spacecraft Navigation During Approach to Docking

    Science.gov (United States)

    Chien, Chiun-Hong; Baker, Kenneth

    2011-01-01

    This paper describes a machine vision system for relative spacecraft navigation during the terminal phase of approach to docking that: 1) matches high contrast image features of the target vehicle, as seen by a camera that is bore-sighted to the docking adapter on the chase vehicle, to the corresponding features in a 3d model of the docking adapter on the target vehicle and 2) is robust to on-orbit lighting. An implementation is provided for the case of the Space Shuttle Orbiter docking to the International Space Station (ISS) with quantitative test results using a full scale, medium fidelity mock-up of the ISS docking adapter mounted on a 6-DOF motion platform at the NASA Marshall Spaceflight Center Flight Robotics Laboratory and qualitative test results using recorded video from the Orbiter Docking System Camera (ODSC) during multiple orbiter to ISS docking missions. The Natural Feature Image Registration (NFIR) system consists of two modules: 1) Tracking which tracks the target object from image to image and estimates the position and orientation (pose) of the docking camera relative to the target object and 2) Acquisition which recognizes the target object if it is in the docking camera Field-of-View and provides an approximate pose that is used to initialize tracking. Detected image edges are matched to the 3d model edges whose predicted location, based on the pose estimate and its first time derivative from the previous frame, is closest to the detected edge1 . Mismatches are eliminated using a rigid motion constraint. The remaining 2d image to 3d model matches are used to make a least squares estimate of the change in relative pose from the previous image to the current image. The changes in position and in attitude are used as data for two Kalman filters whose outputs are smoothed estimate of position and velocity plus attitude and attitude rate that are then used to predict the location of the 3d model features in the next image.

  4. Machine Learning and Computer Vision System for Phenotype Data Acquisition and Analysis in Plants

    Directory of Open Access Journals (Sweden)

    Pedro J. Navarro

    2016-05-01

    Full Text Available Phenomics is a technology-driven approach with promising future to obtain unbiased data of biological systems. Image acquisition is relatively simple. However data handling and analysis are not as developed compared to the sampling capacities. We present a system based on machine learning (ML algorithms and computer vision intended to solve the automatic phenotype data analysis in plant material. We developed a growth-chamber able to accommodate species of various sizes. Night image acquisition requires near infrared lightning. For the ML process, we tested three different algorithms: k-nearest neighbour (kNN, Naive Bayes Classifier (NBC, and Support Vector Machine. Each ML algorithm was executed with different kernel functions and they were trained with raw data and two types of data normalisation. Different metrics were computed to determine the optimal configuration of the machine learning algorithms. We obtained a performance of 99.31% in kNN for RGB images and a 99.34% in SVM for NIR. Our results show that ML techniques can speed up phenomic data analysis. Furthermore, both RGB and NIR images can be segmented successfully but may require different ML algorithms for segmentation.

  5. Machine Learning and Computer Vision System for Phenotype Data Acquisition and Analysis in Plants.

    Science.gov (United States)

    Navarro, Pedro J; Pérez, Fernando; Weiss, Julia; Egea-Cortines, Marcos

    2016-05-05

    Phenomics is a technology-driven approach with promising future to obtain unbiased data of biological systems. Image acquisition is relatively simple. However data handling and analysis are not as developed compared to the sampling capacities. We present a system based on machine learning (ML) algorithms and computer vision intended to solve the automatic phenotype data analysis in plant material. We developed a growth-chamber able to accommodate species of various sizes. Night image acquisition requires near infrared lightning. For the ML process, we tested three different algorithms: k-nearest neighbour (kNN), Naive Bayes Classifier (NBC), and Support Vector Machine. Each ML algorithm was executed with different kernel functions and they were trained with raw data and two types of data normalisation. Different metrics were computed to determine the optimal configuration of the machine learning algorithms. We obtained a performance of 99.31% in kNN for RGB images and a 99.34% in SVM for NIR. Our results show that ML techniques can speed up phenomic data analysis. Furthermore, both RGB and NIR images can be segmented successfully but may require different ML algorithms for segmentation.

  6. Vision Trainer Teaches Focusing Techniques at Home

    Science.gov (United States)

    2015-01-01

    Based on work Stanford Research Institute did for Ames Research Center, Joseph Trachtman developed a vision trainer to treat visual focusing problems in the 1980s. In 2014, Trachtman, operating out of Seattle, released a home version of the device called the Zone-Trac. The inventor has found the biofeedback process used by the technology induces an alpha-wave brain state, causing increased hand-eye coordination and reaction times, among other effects

  7. Optical correlator techniques applied to robotic vision

    Science.gov (United States)

    Hine, Butler P., III; Reid, Max B.; Downie, John D.

    1991-01-01

    Vision processing is one of the most computationally intensive tasks required of an autonomous robot. The data flow from a single typical imaging sensor is roughly 60 Mbits/sec, which can easily overload current on-board processors. Optical correlator-based processing can be used to perform many of the functions required of a general robotic vision system, such as object recognition, tracking, and orientation determination, and can perform these functions fast enough to keep pace with the incoming sensor data. We describe a hybrid digital electronic/analog optical robotic vision processing system developed at Ames Research Center to test concepts and algorithms for autonomous construction, inspection, and maintenance of space-based habitats. We discuss the system architecture design and implementation, its performance characteristics, and our future plans. In particular, we compare the performance of the system to a more conventional all digital electronic system developed concurrently. The hybrid system consistently outperforms the digital electronic one in both speed and robustness.

  8. Ethical, environmental and social issues for machine vision in manufacturing industry

    Science.gov (United States)

    Batchelor, Bruce G.; Whelan, Paul F.

    1995-10-01

    Some of the ethical, environmental and social issues relating to the design and use of machine vision systems in manufacturing industry are highlighted. The authors' aim is to emphasize some of the more important issues, and raise general awareness of the need to consider the potential advantages and hazards of machine vision technology. However, in a short article like this, it is impossible to cover the subject comprehensively. This paper should therefore be seen as a discussion document, which it is hoped will provoke more detailed consideration of these very important issues. It follows from an article presented at last year's workshop. Five major topics are discussed: (1) The impact of machine vision systems on the environment; (2) The implications of machine vision for product and factory safety, the health and well-being of employees; (3) The importance of intellectual integrity in a field requiring a careful balance of advanced ideas and technologies; (4) Commercial and managerial integrity; and (5) The impact of machine visions technology on employment prospects, particularly for people with low skill levels.

  9. Musca domestica inspired machine vision system with hyperacuity

    Science.gov (United States)

    Riley, Dylan T.; Harman, William M.; Tomberlin, Eric; Barrett, Steven F.; Wilcox, Michael; Wright, Cameron H. G.

    2005-05-01

    Musca domestica, the common house fly, has a simple yet powerful and accessible vision system. Cajal indicated in 1885 the fly's vision system is the same as in the human retina. The house fly has some intriguing vision system features such as fast, analog, parallel operation. Furthermore, it has the ability to detect movement and objects at far better resolution than predicted by photoreceptor spacing, termed hyperacuity. We are investigating the mechanisms behind these features and incorporating them into next generation vision systems. We have developed a prototype sensor that employs a fly inspired arrangement of photodetectors sharing a common lens. The Gaussian shaped acceptance profile of each sensor coupled with overlapped sensor field of views provide the necessary configuration for obtaining hyperacuity data. The sensor is able to detect object movement with far greater resolution than that predicted by photoreceptor spacing. We have exhaustively tested and characterized the sensor to determine its practical resolution limit. Our tests coupled with theory from Bucklew and Saleh (1985) indicate that the limit to the hyperacuity response may only be related to target contrast. We have also implemented an array of these prototype sensors which will allow for two - dimensional position location. These high resolution, low contrast capable sensors are being developed for use as a vision system for an autonomous robot and the next generation of smart wheel chairs. However, they are easily adapted for biological endoscopy, downhole monitoring in oil wells, and other applications.

  10. Hand gesture recognition system based in computer vision and machine learning

    OpenAIRE

    Trigueiros, Paulo; Ribeiro, António Fernando; Reis, L.P.

    2015-01-01

    "Lecture notes in computational vision and biomechanics series, ISSN 2212-9391, vol. 19" Hand gesture recognition is a natural way of human computer interaction and an area of very active research in computer vision and machine learning. This is an area with many different possible applications, giving users a simpler and more natural way to communicate with robots/systems interfaces, without the need for extra devices. So, the primary goal of gesture recognition research applied to Hum...

  11. A Machine Vision System for Quality Inspection of Pine Nuts

    Directory of Open Access Journals (Sweden)

    Ikramullah Khosa

    2016-09-01

    Full Text Available Computers and artificial intelligence have penetrated in the food industry since last decade, for intellectual automatic processing and packaging in general, and in assisting for quality inspection of the food itself in particular. The food quality assessment task becomes more challenging when it is about harmless internal examination of the ingredient, and even more when its size is also minute. In this article, a method for automatic detection, extraction and classification of raw food item is presented using x-ray image data of pine nuts. Image processing techniques are employed in developing an efficient method for automatic detection and then extraction of individual ingredient, from the source x-ray image which comprises bunch of nuts in a single frame. For data representation, statistical texture analysis is carried out and attributes are calculated from each of the sample image on the global level as features. In addition co-occurrence matrices are computed from images with four different offsets, and hence more features are extracted by using them. To find fewer meaningful characteristics, all the calculated features are organized in several combinations and then tested. Seventy percent of image data is used for training and 15% each for cross-validation and test purposes. Binary classification is performed using two state-of-the-art non-linear classifiers: Artificial Neural Network (ANN and Support Vector Machines (SVM. Performance is evaluated in terms of classification accuracy, specificity and sensitivity. ANN classifier showed 87.6% accuracy with correct recognition rate of healthy nuts and unhealthy nuts as 94% and 62% respectively. SVM classifier produced the similar accuracy achieving 86.3% specificity and 89.2% sensitivity rate. The results obtained are unique itself in terms of ingredient and promising relatively. It is also found that feature set size can be reduced up to 57% by compromising 3.5% accuracy, in combination with

  12. Virtual Machine Monitor Indigenous Memory Reclamation Technique

    Directory of Open Access Journals (Sweden)

    Muhammad Shams Ul Haq

    2016-04-01

    Full Text Available Sandboxing is a mechanism to monitor and control the execution of malicious or untrusted program. Memory overhead incurred by sandbox solutions is one of bottleneck for sandboxing most of applications in a system. Memory reclamation techniques proposed for traditional full virtualization do not suit sandbox environment due to lack of full scale guest operating system in sandbox. In this paper, we propose memory reclamation technique for sandboxed applications. The proposed technique indigenously works in virtual machine monitor layer without installing any driver in VMX non root mode and without new communication channel with host kernel. Proposed Page reclamation algorithm is a simple modified form of Least recently used page reclamation and Working set page reclamation algorithms. For efficiently collecting working set of application, we use a hardware virtualization extension, page Modification logging introduced by Intel. We implemented proposed technique with one of open source sandboxes to show effectiveness of proposed memory reclamation method. Experimental results show that proposed technique successfully reclaim up to 11% memory from sandboxed applications with negligible CPU overheads

  13. Computer vision and machine learning with RGB-D sensors

    CERN Document Server

    Shao, Ling; Kohli, Pushmeet

    2014-01-01

    This book presents an interdisciplinary selection of cutting-edge research on RGB-D based computer vision. Features: discusses the calibration of color and depth cameras, the reduction of noise on depth maps and methods for capturing human performance in 3D; reviews a selection of applications which use RGB-D information to reconstruct human figures, evaluate energy consumption and obtain accurate action classification; presents an approach for 3D object retrieval and for the reconstruction of gas flow from multiple Kinect cameras; describes an RGB-D computer vision system designed to assist t

  14. The systematic development of a machine vision based milking robot.

    NARCIS (Netherlands)

    Gouws, J.

    1993-01-01

    Agriculture involves unique interactions between man, machines, and various elements from nature. Therefore the implementation of advanced technology in agriculture holds different challenges than in other sectors of the economy. This dissertation stems from research into the application of advanced

  15. Machine Vision Automation for Ground Control Tele-Robotics Project

    Data.gov (United States)

    National Aeronautics and Space Administration — This project seeks to advance ground based tele-robotic capabilities with the development of natural feature target tracking technology with the use of machine...

  16. Measuring Leaf Motion of Tomato by Machine Vision

    NARCIS (Netherlands)

    Henten, van E.J.; Marx, G.E.H.; Hofstee, J.W.; Hemming, J.; Sarlikioti, V.

    2012-01-01

    For a better understanding of growth and development of tomato plants in three dimensional space, tomato plants were monitored using a computer vision system. It is commonly known that leaves of tomato plants do not have a fixed position and orientation during the day; they move in response to chang

  17. Quality indexing by machine vision during fermentation in black tea manufacturing

    Science.gov (United States)

    Borah, S.; Bhuyan, M.

    2003-04-01

    Although the organoleptic method of tea testing has been traditionally used for quality monitoring, an alternative way by machine vision may be advantageous. Although, the three main quality descriptors estimate the overall quality of made-tea, viz., strength, briskness and brightness of tea liquor, the exact colour detection in fermenting process leads to a good quality-monitoring tool. The use of digital image processing technique for this purpose is reported to play an effective role towards the production of good quality tea though it is not the only quality determining parameter. In this paper, it has been tried to compare the contribution of the chemical constituents towards the final product with the visual appearance in the processing stage by imaging. The use of machine intelligence supports the process somewhat invariantly in comparison to the human decision and colorimetric approach. The captured images are processed for colour matching with a standard image database using HSI colour model. The application of colour dissimilarity and perceptron learning for the standard images and the test images is ensured. Moreover, the performance of the system is being tried to correlate with the decision made by the organoleptic panel assigned for the tea testing and chemical test results on the final product. However, it should be noted that the optimized result could be achieved only when the other quality parameters such as withering, flavour (aroma) detection, drying status etc. are properly maintained.

  18. Optimization of machining techniques – A retrospective and literature review

    Indian Academy of Sciences (India)

    Aman Aggarwal; Hari Singh

    2005-12-01

    In this paper an attempt is made to review the literature on optimizing machining parameters in turning processes. Various conventional techniques employed for machining optimization include geometric programming, geometric plus linear programming, goal programming, sequential unconstrained minimizationtechnique, dynamic programming etc. The latest techniques for optimization include fuzzy logic, scatter search technique, genetic algorithm, Taguchi technique and response surface methodology.

  19. On-line estimation of laser-drilled hole depth using a machine vision method.

    Science.gov (United States)

    Ho, Chao-Ching; He, Jun-Jia; Liao, Te-Ying

    2012-01-01

    The paper presents a novel method for monitoring and estimating the depth of a laser-drilled hole using machine vision. Through on-line image acquisition and analysis in laser machining processes, we could simultaneously obtain correlations between the machining processes and analyzed images. Based on the machine vision method, the depths of laser-machined holes could be estimated in real time. Therefore, a low cost on-line inspection system is developed to increase productivity. All of the processing work was performed in air under standard atmospheric conditions and gas assist was used. A correlation between the cumulative size of the laser-induced plasma region and the depth of the hole is presented. The result indicates that the estimated depths of the laser-drilled holes were a linear function of the cumulative plasma size, with a high degree of confidence. This research provides a novel machine vision-based method for estimating the depths of laser-drilled holes in real time.

  20. On-Line Estimation of Laser-Drilled Hole Depth Using a Machine Vision Method

    Directory of Open Access Journals (Sweden)

    Te-Ying Liao

    2012-07-01

    Full Text Available The paper presents a novel method for monitoring and estimating the depth of a laser-drilled hole using machine vision. Through on-line image acquisition and analysis in laser machining processes, we could simultaneously obtain correlations between the machining processes and analyzed images. Based on the machine vision method, the depths of laser-machined holes could be estimated in real time. Therefore, a low cost on-line inspection system is developed to increase productivity. All of the processing work was performed in air under standard atmospheric conditions and gas assist was used. A correlation between the cumulative size of the laser-induced plasma region and the depth of the hole is presented. The result indicates that the estimated depths of the laser-drilled holes were a linear function of the cumulative plasma size, with a high degree of confidence. This research provides a novel machine vision-based method for estimating the depths of laser-drilled holes in real time.

  1. 75 FR 71146 - In the Matter of Certain Machine Vision Software, Machine Vision Systems, and Products Containing...

    Science.gov (United States)

    2010-11-22

    ..., California; Techno Soft Systemnics, Inc. (``Techno Soft'') of Japan; Fuji Machine Manufacturing Co., Ltd. of... the investigation as to Amistar based on a consent order and settlement agreement, and as to...

  2. A Multiple Sensor Machine Vision System for Automatic Hardwood Feature Detection

    Science.gov (United States)

    D. Earl Kline; Richard W. Conners; Daniel L. Schmoldt; Philip A. Araman; Robert L. Brisbin

    1993-01-01

    A multiple sensor machine vision prototype is being developed to scan full size hardwood lumber at industrial speeds for automatically detecting features such as knots holes, wane, stain, splits, checks, and color. The prototype integrates a multiple sensor imaging system, a materials handling system, a computer system, and application software. The prototype provides...

  3. Gall mite inspection on dormant black currant buds using machine vision

    DEFF Research Database (Denmark)

    Nielsen, M. R.; Stigaard Laursen, Morten; Jonassen, M. S.

    2013-01-01

    This paper presents a novel machine vision-based approach detecting and mapping gall mite infection in dormant buds on black currant bushes. A vehicle was fitted with four cameras and RTK-GPS. Results compared automatic detection to human decisions based on the images, and by mapping the results ...

  4. Machine vision analysis for industrial beet color change kinetics and total soluble solid content

    Science.gov (United States)

    A machine vision system (MVS) for the measurement of color change kinetics in crushed industrial beet to evaluate the total soluble solid content (°Brix) was developed in this study. It is expected that higher the °Brix faster the color change and modeling this color change kinetics helps in assessi...

  5. 3D Machine Vision and Additive Manufacturing: Concurrent Product and Process Development

    Science.gov (United States)

    Ilyas, Ismet P.

    2013-06-01

    The manufacturing environment rapidly changes in turbulence fashion. Digital manufacturing (DM) plays a significant role and one of the key strategies in setting up vision and strategic planning toward the knowledge based manufacturing. An approach of combining 3D machine vision (3D-MV) and an Additive Manufacturing (AM) may finally be finding its niche in manufacturing. This paper briefly overviews the integration of the 3D machine vision and AM in concurrent product and process development, the challenges and opportunities, the implementation of the 3D-MV and AM at POLMAN Bandung in accelerating product design and process development, and discusses a direct deployment of this approach on a real case from our industrial partners that have placed this as one of the very important and strategic approach in research as well as product/prototype development. The strategic aspects and needs of this combination approach in research, design and development are main concerns of the presentation.

  6. Multivariate Analysis Techniques for Optimal Vision System Design

    DEFF Research Database (Denmark)

    Sharifzadeh, Sara

    used in this thesis are described. The methodological strategies are outlined including sparse regression and pre-processing based on feature selection and extraction methods, supervised versus unsupervised analysis and linear versus non-linear approaches. One supervised feature selection algorithm......The present thesis considers optimization of the spectral vision systems used for quality inspection of food items. The relationship between food quality, vision based techniques and spectral signature are described. The vision instruments for food analysis as well as datasets of the food items...... (SSPCA) and DCT based characterization of the spectral diffused reflectance images for wavelength selection and discrimination. These methods together with some other state-of-the-art statistical and mathematical analysis techniques are applied on datasets of different food items; meat, diaries, fruits...

  7. X-ray-based machine vision system for distal locking of intramedullary nails.

    Science.gov (United States)

    Juneho, F; Bouazza-Marouf, K; Kerr, D; Taylor, A J; Taylor, G J S

    2007-05-01

    In surgical procedures for femoral shaft fracture treatment, current techniques for locking the distal end of intramedullary nails, using two screws, rely heavily on the use of two-dimensional X-ray images to guide three-dimensional bone drilling processes. Therefore, a large number of X-ray images are required, as the surgeon uses his/her skills and experience to locate the distal hole axes on the intramedullary nail. The long-term effects of X-ray radiation and their relation to different types of cancer still remain uncertain. Therefore, there is a need to develop a surgical technique that can limit the use of X-rays during the distal locking procedure. A robotic-assisted orthopaedic surgery system has been developed at Loughborough University to assist orthopaedic surgeons by reducing the irradiation involved in such operations. The system simplifies the current approach as it uses only two near-orthogonal X-ray images to determine the drilling trajectory of the distal locking holes, thereby considerably reducing irradiation to both the surgeon and patient. Furthermore, the system uses robust machine vision features to reduce the surgeon's interaction with the system, thus reducing the overall operating time. Laboratory test results have shown that the proposed system is very robust in the presence of variable noise and contrast in the X-ray images.

  8. Automatic detection and counting of cattle in UAV imagery based on machine vision technology (Conference Presentation)

    Science.gov (United States)

    Rahnemoonfar, Maryam; Foster, Jamie; Starek, Michael J.

    2017-05-01

    Beef production is the main agricultural industry in Texas, and livestock are managed in pasture and rangeland which are usually huge in size, and are not easily accessible by vehicles. The current research method for livestock location identification and counting is visual observation which is very time consuming and costly. For animals on large tracts of land, manned aircraft may be necessary to count animals which is noisy and disturbs the animals, and may introduce a source of error in counts. Such manual approaches are expensive, slow and labor intensive. In this paper we study the combination of small unmanned aerial vehicle (sUAV) and machine vision technology as a valuable solution to manual animal surveying. A fixed-wing UAV fitted with GPS and digital RGB camera for photogrammetry was flown at the Welder Wildlife Foundation in Sinton, TX. Over 600 acres were flown with four UAS flights and individual photographs used to develop orthomosaic imagery. To detect animals in UAV imagery, a fully automatic technique was developed based on spatial and spectral characteristics of objects. This automatic technique can even detect small animals that are partially occluded by bushes. Experimental results in comparison to ground-truth show the effectiveness of our algorithm.

  9. Robot vision: obstacle-avoidance techniques for unmanned aerial vehicles

    NARCIS (Netherlands)

    Carloni, Raffaella; Lippiello, Vincenzo; D'auria, Massimo; Fumagalli, Matteo; Mersha, Abeje Y.; Stramigioli, Stefano; Sicilano, Bruno

    2013-01-01

    In this article, a vision-based technique for obstacle avoidance and target identification is combined with haptic feedback to develop a new teleoperated navigation system for underactuated aerial vehicles in unknown environments. A three-dimensional (3-D) map of the surrounding environment is built

  10. Performance evaluation of image enhancement techniques on night vision imagery

    NARCIS (Netherlands)

    Dijk, J.; Bijl, P.; Eekeren, W.M. van

    2010-01-01

    Recently new techniques for night-vision cameras are developed. Digital image-intensifiers are becoming available on the market. Also, so-called EMCCD (electro-magnified) cameras are developed, which can also record imagery in dim conditions. In this paper we present data recorded with both types of

  11. Robot vision: obstacle-avoidance techniques for unmanned aerial vehicles

    NARCIS (Netherlands)

    Carloni, Raffaella; Lippiello, Vincenzo; D'auria, Massimo; Fumagalli, Matteo; Mersha, A.Y.; Stramigioli, Stefano; Sicilano, Bruno

    2013-01-01

    In this article, a vision-based technique for obstacle avoidance and target identification is combined with haptic feedback to develop a new teleoperated navigation system for underactuated aerial vehicles in unknown environments. A three-dimensional (3-D) map of the surrounding environment is built

  12. A New Color Constancy Model for Machine Vision

    Institute of Scientific and Technical Information of China (English)

    TAO Linmi; XU Guangyou

    2001-01-01

    Both physiological and psychological evidences suggest that the human visual system analyze images in neural subsystems tuned to different attributes of the stimulus. Color module and lightness module are such subsystems. Under this general result, a new physical model of trichromatic system has been developed to deal with the color constancy of computer vision. A normal color image is split into two images: the gray scale image and the equal lightness color image for the two modules. Relatively, a two-dimensional descriptor is applied to describe the property of surface reflectance in the equal lightness color image. This description of surface spectral reflectance has the property of color constancy. Image segmentation experiments based on color property of object show that the presented model is effective.

  13. SAD-based stereo vision machine on a System-on-Programmable-Chip (SoPC).

    Science.gov (United States)

    Zhang, Xiang; Chen, Zhangwei

    2013-03-04

    This paper, proposes a novel solution for a stereo vision machine based on the System-on-Programmable-Chip (SoPC) architecture. The SOPC technology provides great convenience for accessing many hardware devices such as DDRII, SSRAM, Flash, etc., by IP reuse. The system hardware is implemented in a single FPGA chip involving a 32-bit Nios II microprocessor, which is a configurable soft IP core in charge of managing the image buffer and users' configuration data. The Sum of Absolute Differences (SAD) algorithm is used for dense disparity map computation. The circuits of the algorithmic module are modeled by the Matlab-based DSP Builder. With a set of configuration interfaces, the machine can process many different sizes of stereo pair images. The maximum image size is up to 512 K pixels. This machine is designed to focus on real time stereo vision applications. The stereo vision machine offers good performance and high efficiency in real time. Considering a hardware FPGA clock of 90 MHz, 23 frames of 640 × 480 disparity maps can be obtained in one second with 5 × 5 matching window and maximum 64 disparity pixels.

  14. SAD-Based Stereo Vision Machine on a System-on-Programmable-Chip (SoPC

    Directory of Open Access Journals (Sweden)

    Zhangwei Chen

    2013-03-01

    Full Text Available This paper, proposes a novel solution for a stereo vision machine based on the System-on-Programmable-Chip (SoPC architecture. The SOPC technology provides great convenience for accessing many hardware devices such as DDRII, SSRAM, Flash, etc., by IP reuse. The system hardware is implemented in a single FPGA chip involving a 32-bit Nios II microprocessor, which is a configurable soft IP core in charge of managing the image buffer and users’ configuration data. The Sum of Absolute Differences (SAD algorithm is used for dense disparity map computation. The circuits of the algorithmic module are modeled by the Matlab-based DSP Builder. With a set of configuration interfaces, the machine can process many different sizes of stereo pair images. The maximum image size is up to 512 K pixels. This machine is designed to focus on real time stereo vision applications. The stereo vision machine offers good performance and high efficiency in real time. Considering a hardware FPGA clock of 90 MHz, 23 frames of 640 × 480 disparity maps can be obtained in one second with 5 × 5 matching window and maximum 64 disparity pixels.

  15. The relevance vector machine technique for channel equalization application

    OpenAIRE

    Chen, S.; Gunn, S.R.; Harris, C J

    2001-01-01

    The recently introduced relevance vector machine (RVM) technique is applied to communication channel equalization. It is demonstrated that the RVM equalizer can closely match the optimal performance of the Bayesian equalizer, with a much sparser kernel representation than that is achievable by the state-of-art support vector machine (SVM) technique.

  16. Improvement of molecular techniques: A multidisciplinar vision

    Directory of Open Access Journals (Sweden)

    Bruno do Amaral Crispim

    2016-08-01

    Full Text Available The advances in molecular technologies since the discovery of the PCR (Polymerase Chain Reaction and their association with the use of molecular markers, allowed a rapid progress in the development of technologies and equipment able to generate and analyze data on a large scale, revolutionizing research that until recently was only based on single marker, like the analysis of Single Nucleotide Polymorphism (SNP, and nowadays with the genomic era is already possible in a few hours genotyping millions or even thousands of SNPs. This evolution has allowed improvements in research to the knowledge of genomes creating expectations and real possibilities of application of these techniques in various fields, from medicine to animal production. These new technologies of molecular analysis of DNA variability determining points of interest in chromosomes, which are technically called as molecular markers. These markers can be used in various applications, including paternity test, construction of genetic maps, mapping of quantitative inheritance of characteristics, isolation of genes, marker-assisted selection and characterization of the genetic diversity of different species. The improvement of sequencing and bioinformatics technologies were crucial to studies with characteristics of interest using high-density genetic information. The SNP genotyping panels stimulated researches in the human area, especially in studies of cancer and exoma, and also in agribusiness, aiming the search for superior genotypes for domestic plants and animals. The differential use of the panels is the possibility to seek complex characteristics, once the wide distribution of markers favors through the linkage disequilibrium, the identification of genomic regions associated with expression phenotypes in study. Therefore, this advance has become essential for greater accuracy and speed in molecular diagnostics, increasing the accuracy in the selection of individuals with

  17. Machine vision based on the concept of contrast sensitivity of the human eye

    Science.gov (United States)

    Bezzubik, Vitali; Belashenkov, Nikolai; Vdovin, Gleb

    2014-09-01

    The model of contrast sensitivity function (CSF) of machine vision system, based on the CSF of the human visual system is proposed. By analogy with the human eye, we employ the concept of ganglion cell receptive field to the artificial light-sensitive elements. By further following this concept, we introduced quantative metrics of local and global contrast of digital image. We suggested that the contrast sensitivity threshold forms an iso-line in the parameter space contrast - spatial frequency. The model, implemented in a computer vision system, has been compared to the results of contrast sensitivity research, conducted directly with the human visual system, and demonstrated a good match.

  18. BENCHMARKING MACHINE LEARNING TECHNIQUES FOR SOFTWARE DEFECT DETECTION

    Directory of Open Access Journals (Sweden)

    Saiqa Aleem

    2015-06-01

    Full Text Available Machine Learning approaches are good in solving problems that have less information. In most cases, the software domain problems characterize as a process of learning that depend on the various circumstances and changes accordingly. A predictive model is constructed by using machine learning approaches and classified them into defective and non-defective modules. Machine learning techniques help developers to retrieve useful information after the classification and enable them to analyse data from different perspectives. Machine learning techniques are proven to be useful in terms of software bug prediction. This study used public available data sets of software modules and provides comparative performance analysis of different machine learning techniques for software bug prediction. Results showed most of the machine learning methods performed well on software bug datasets.

  19. Micro machining techniques commonly used in manufacturing field

    Directory of Open Access Journals (Sweden)

    Adem Çiçek

    2011-06-01

    Full Text Available Developing technology and the need for high-precision parts in manufacturing industry has revealed the micro-machining. Machine tools and work pieces are miniaturized through micro-machining, materials and power consumption reduced to a minimum level. High productiveness in the use of resources and time can be obtained through this rapidly growing industry around the world. In this paper, different micro-machining techniques have been classified revising the investigations recently performed in the field of micro-machining and discussed their contributions to manufacturing process.

  20. A review of RGB-LED based mixed-color illumination system for machine vision and microscopy

    Science.gov (United States)

    Hou, Lexin; Wang, Hexin; Xu, Min

    2016-09-01

    The theory and application of RGB-LED based mixed-color illumination system for use in machine vision and optical microscopy systems are presented. For machine vision system, relationship of various color sources and output image sharpness is discussed. From the viewpoint of gray scale images, evaluation and optimization methods of optimal illumination for machine vision are concluded. The image quality under monochromatic and mixed color illumination is compared. For optical microscopy system, demand of light source is introduced and design thoughts of RGB-LED based mixed-color illumination system are concluded. The problems need to be solved in this field are pointed out.

  1. DIAGNOSIS OF DIABETIC RETINOPATHY USING MACHINE LEARNING TECHNIQUES

    Directory of Open Access Journals (Sweden)

    R. Priya

    2013-07-01

    Full Text Available Diabetic retinopathy (DR is an eye disease caused by the complication of diabetes and we should detect it early for effective treatment. As diabetes progresses, the vision of a patient may start to deteriorate and lead to diabetic retinopathy. As a result, two groups were identified, namely non-proliferative diabetic retinopathy (NPDR and proliferative diabetic retinopathy (PDR. In this paper, to diagnose diabetic retinopathy, three models like Probabilistic Neural network (PNN, Bayesian Classification and Support vector machine (SVM are described and their performances are compared. The amount of the disease spread in the retina can be identified by extracting the features of the retina. The features like blood vessels, haemmoraghes of NPDR image and exudates of PDR image are extracted from the raw images using the image processing techniques and fed to the classifier for classification. A total of 350 fundus images were used, out of which 100 were used for training and 250 images were used for testing. Experimental results show that PNN has an accuracy of 89.6 % Bayes Classifier has an accuracy of 94.4% and SVM has an accuracy of 97.6%. This infers that the SVM model outperforms all other models. Also our system is also run on 130 images available from “DIARETDB0: Evaluation Database and Methodology for Diabetic Retinopathy” and the results show that PNN has an accuracy of 87.69% Bayes Classifier has an accuracy of 90.76% and SVM has an accuracy of 95.38%.

  2. A two-level real-time vision machine combining coarse and fine grained parallelism

    DEFF Research Database (Denmark)

    Jensen, Lars Baunegaard With; Kjær-Nielsen, Anders; Pauwels, Karl;

    2010-01-01

    In this paper, we describe a real-time vision machine having a stereo camera as input generating visual information on two different levels of abstraction. The system provides visual low-level and mid-level information in terms of dense stereo and optical flow, egomotion, indicating areas...... a factor 90 and a reduction of latency of a factor 26 compared to processing on a single CPU--core. Since the vision machine provides generic visual information it can be used in many contexts. Currently it is used in a driver assistance context as well as in two robotic applications....... with independently moving objects as well as a condensed geometric description of the scene. The system operates at more than 20 Hz using a hybrid architecture consisting of one dual--GPU card and one quad-core CPU. The different processing stages of visual information have rather different characteristics...

  3. Quantifying nonhomogeneous colors in agricultural materials. Part II: comparison of machine vision and sensory panel evaluations.

    Science.gov (United States)

    Balaban, M O; Aparicio, J; Zotarelli, M; Sims, C

    2008-11-01

    The average colors of mangos and apples were measured using machine vision. A method to quantify the perception of nonhomogeneous colors by sensory panelists was developed. Three colors out of several reference colors and their perceived percentage of the total sample area were selected by untrained panelists. Differences between the average colors perceived by panelists and those from the machine vision were reported as DeltaE values (color difference error). Effects of nonhomogeneity of color, and using real samples or their images in the sensory panels on DeltaE were evaluated. In general, samples with more nonuniform colors had higher DeltaE values, suggesting that panelists had more difficulty in evaluating more nonhomogeneous colors. There was no significant difference in DeltaE values between the real fruits and their screen image, therefore images can be used to evaluate color instead of the real samples.

  4. Enhanced Machine Vision System for Ripe Fruit Detection Based on Robotic Harvesting

    Directory of Open Access Journals (Sweden)

    R. Thendral

    2015-04-01

    Full Text Available The proposed work intends to provide an efficient algorithm for the instruction of an automatic robot arm to choose the ripe fruits on the tree. Steps involved in this study are recognizing and locating the ripe fruits from the leaf and branch portions by using an efficient machine vision algorithm. Initially, discrete wavelet transform is used for better preserving of edges and fine details in the given input image. Then RGB, HSV, L*a*b* and YIQ color spaces were studied to segment the ripe fruits from the surrounding objects. Finally, the results showed that ‘I’ component of the YIQ color space has the best criterion for recognizing the fruit from the foliage. The fruit segmentation based on machine vision has an occlusion problem. In this proposed method these problems are also examined.

  5. Principles of image processing in machine vision systems for the color analysis of minerals

    Science.gov (United States)

    Petukhova, Daria B.; Gorbunova, Elena V.; Chertov, Aleksandr N.; Korotaev, Valery V.

    2014-09-01

    At the moment color sorting method is one of promising methods of mineral raw materials enrichment. This method is based on registration of color differences between images of analyzed objects. As is generally known the problem with delimitation of close color tints when sorting low-contrast minerals is one of the main disadvantages of color sorting method. It is can be related with wrong choice of a color model and incomplete image processing in machine vision system for realizing color sorting algorithm. Another problem is a necessity of image processing features reconfiguration when changing the type of analyzed minerals. This is due to the fact that optical properties of mineral samples vary from one mineral deposit to another. Therefore searching for values of image processing features is non-trivial task. And this task doesn't always have an acceptable solution. In addition there are no uniform guidelines for determining criteria of mineral samples separation. It is assumed that the process of image processing features reconfiguration had to be made by machine learning. But in practice it's carried out by adjusting the operating parameters which are satisfactory for one specific enrichment task. This approach usually leads to the fact that machine vision system unable to estimate rapidly the concentration rate of analyzed mineral ore by using color sorting method. This paper presents the results of research aimed at addressing mentioned shortcomings in image processing organization for machine vision systems which are used to color sorting of mineral samples. The principles of color analysis for low-contrast minerals by using machine vision systems are also studied. In addition, a special processing algorithm for color images of mineral samples is developed. Mentioned algorithm allows you to determine automatically the criteria of mineral samples separation based on an analysis of representative mineral samples. Experimental studies of the proposed algorithm

  6. Online tomato sorting based on shape, maturity, size, and surface defects using machine vision

    OpenAIRE

    ARJENAKI, Omid Omidi; MOGHADDAM, Parviz Ahmadi; MOTLAGH, Asad Moddares

    2013-01-01

    Online sorting of tomatoes according to their features is an important postharvest procedure. The purpose of this research was to develop an efficient machine vision-based experimental sorting system for tomatoes. Relevant sorting parameters included shape (oblong and circular), size (small and large), maturity (color), and defects. The variables defining shape, maturity, and size of the tomatoes were eccentricity, average of color components, and 2-D pixel area, respectively. Tomato defects ...

  7. Using an FPGA-Based Processing Platform in an Industrial Machine Vision System

    OpenAIRE

    King, William E

    1998-01-01

    This thesis describes the development of a commercial machine vision system as a case study for utilizing the Modular Reprogrammable Real-time Processing Hardware (MORRPH) board. The commercial system described in this thesis is based on a prototype system that was developed as a test-bed for developing the necessary concepts and algorithms. The prototype system utilized color linescan cameras, custom framegrabbers, and standard PCs to color-sort red oak parts (staves). When a furniture ma...

  8. Qualitative classification of milled rice grains using computer vision and metaheuristic techniques.

    Science.gov (United States)

    Zareiforoush, Hemad; Minaei, Saeid; Alizadeh, Mohammad Reza; Banakar, Ahmad

    2016-01-01

    Qualitative grading of milled rice grains was carried out in this study using a machine vision system combined with some metaheuristic classification approaches. Images of four different classes of milled rice including Low-processed sound grains (LPS), Low-processed broken grains (LPB), High-processed sound grains (HPS), and High-processed broken grains (HPB), representing quality grades of the product, were acquired using a computer vision system. Four different metaheuristic classification techniques including artificial neural networks, support vector machines, decision trees and Bayesian Networks were utilized to classify milled rice samples. Results of validation process indicated that artificial neural network with 12-5*4 topology had the highest classification accuracy (98.72 %). Next, support vector machine with Universal Pearson VII kernel function (98.48 %), decision tree with REP algorithm (97.50 %), and Bayesian Network with Hill Climber search algorithm (96.89 %) had the higher accuracy, respectively. Results presented in this paper can be utilized for developing an efficient system for fully automated classification and sorting of milled rice grains.

  9. Intelligent Machine Vision Based Modeling and Positioning System in Sand Casting Process

    Directory of Open Access Journals (Sweden)

    Shahid Ikramullah Butt

    2017-01-01

    Full Text Available Advanced vision solutions enable manufacturers in the technology sector to reconcile both competitive and regulatory concerns and address the need for immaculate fault detection and quality assurance. The modern manufacturing has completely shifted from the manual inspections to the machine assisted vision inspection methodology. Furthermore, the research outcomes in industrial automation have revolutionized the whole product development strategy. The purpose of this research paper is to introduce a new scheme of automation in the sand casting process by means of machine vision based technology for mold positioning. Automation has been achieved by developing a novel system in which casting molds of different sizes, having different pouring cup location and radius, position themselves in front of the induction furnace such that the center of pouring cup comes directly beneath the pouring point of furnace. The coordinates of the center of pouring cup are found by using computer vision algorithms. The output is then transferred to a microcontroller which controls the alignment mechanism on which the mold is placed at the optimum location.

  10. METHODOLOGY OF TECHNIQUE PREPARATION FOR LOW VISION JAVELIN THROWERS

    Directory of Open Access Journals (Sweden)

    Milan Matić

    2013-07-01

    Full Text Available Javelin throwing discipline for disabled people has been expanding couple of years back. In addition, world’s records have been improving year after year. The esential part in preparation of low vision javelin throwers is mastering the technique elements, crucial for acquiring better results. Method of theoretical analysis, decriptive and comparative methods of survey were applied. Relevant knowledge in the area of low vision javelin throwers was analyzed and systematized, and then interpretated theoretically and applied on the top javelin thrower, which served as a base for the inovative apporoach in methodology and praxis with disabled people. Due to visual impairment, the coordination and balance are challenged. This limitation practically makes the difference in methodology, explained in this article. Apart from the goals focused on improving the condition and results on competitions, more specialized goals should be considered, e.g. improving of orientation, balance and socialization process for the people who have low vision. Special approach used in the technique preparation brought the significant improvement in techique of our famous Paralympian Grlica Miloš. In addition to the technique improvement he acquired better results on the big competitions and a few worldwide valuable prizes were won. The area of ’sport for disabled people’ is not enough present in the praxis of sport’s workers. More articles and scientific surveys on this topic are needed for further work and results improvement with these kind of sportsmen.

  11. Machine Vision System Design Method%机器视觉系统的设计方法

    Institute of Scientific and Technical Information of China (English)

    王运哲; 白雁兵; 张博

    2011-01-01

    文章主要介绍了机器视觉系统的概念和发展历程,介绍了机器视觉的组成和基本原理,从工业摄像机、镜头、光源、图像采集卡几方面详细阐述了机器视觉系统的设计要点、分类、选型。%The article introduces the conception and the developmental process of machine vision system, the component and fundamental theory of machine vision, expatiates on main points of designing machine vision system, classifying, choosing type, enumerates the most of manufacturers in the field of machine vision system in china.

  12. Machine learning approximation techniques using dual trees

    OpenAIRE

    Ergashbaev, Denis

    2015-01-01

    This master thesis explores a dual-tree framework as applied to a particular class of machine learning problems that are collectively referred to as generalized n-body problems. It builds a new algorithm on top of it and improves existing Boosted OGE classifier.

  13. Volume Measurement in Solid Objects Using Artificial Vision Technique

    Science.gov (United States)

    Cordova-Fraga, T.; Martinez-Espinosa, J. C.; Bernal, J.; Huerta-Franco, R.; Sosa-Aquino, M.; Vargas-Luna, M.

    2004-09-01

    A simple system using artificial vision technique for measuring the volume of solid objects is described. The system is based on the acquisition of an image sequence of the object while it is rotating on an automated mechanism controlled by a PC. Volumes of different objects such as a sphere, a cylinder and also a carrot were measured. The proposed algorithm was developed in environment LabView 6.1. This technique can be very useful when it is applied to measure the human body for evaluating its body composition.

  14. Extending Driving Vision Based on Image Mosaic Technique

    Directory of Open Access Journals (Sweden)

    Chen Deng

    2017-01-01

    Full Text Available Car cameras have been used extensively to assist driving by make driving visible. However, due to the limitation of the Angle of View (AoV, the dead zone still exists, which is a primary origin of car accidents. In this paper, we introduce a system to extend the vision of drivers to 360 degrees. Our system consists of four wide-angle cameras, which are mounted at different sides of a car. Although the AoV of each camera is within 180 degrees, relying on the image mosaic technique, our system can seamlessly integrate 4-channel videos into a panorama video. The panorama video enable drivers to observe everywhere around a car as far as three meters from a top view. We performed experiments in a laboratory environment. Preliminary results show that our system can eliminate vision dead zone completely. Additionally, the real-time performance of our system can satisfy requirements for practical use.

  15. Modelling tick abundance using machine learning techniques and satellite imagery

    DEFF Research Database (Denmark)

    Kjær, Lene Jung; Korslund, L.; Kjelland, V.

    satellite images to run Boosted Regression Tree machine learning algorithms to predict overall distribution (presence/absence of ticks) and relative tick abundance of nymphs and larvae in southern Scandinavia. For nymphs, the predicted abundance had a positive correlation with observed abundance...... the predicted distribution of larvae was mostly even throughout Denmark, it was primarily around the coastlines in Norway and Sweden. Abundance was fairly low overall except in some fragmented patches corresponding to forested habitats in the region. Machine learning techniques allow us to predict for larger...... the collected ticks for pathogens and using the same machine learning techniques to develop prevalence maps of the ScandTick region....

  16. Comparison of Machine Learning Techniques for Target Detection

    NARCIS (Netherlands)

    Vink, J.P.; Haan, G. de

    2013-01-01

    This paper focuses on machine learning techniques for real-time detection. Although many supervised learning techniques have been described in the literature, no technique always performs best. Several comparative studies are available, but have not always been performedcarefully, leading to invalid

  17. Comparison of Machine Learning Techniques for Target Detection

    NARCIS (Netherlands)

    Vink, J.P.; Haan, G. de

    2013-01-01

    This paper focuses on machine learning techniques for real-time detection. Although many supervised learning techniques have been described in the literature, no technique always performs best. Several comparative studies are available, but have not always been performedcarefully, leading to invalid

  18. Extreme Learning Machine and Moving Least Square Regression Based Solar Panel Vision Inspection

    Directory of Open Access Journals (Sweden)

    Heng Liu

    2017-01-01

    Full Text Available In recent years, learning based machine intelligence has aroused a lot of attention across science and engineering. Particularly in the field of automatic industry inspection, the machine learning based vision inspection plays a more and more important role in defect identification and feature extraction. Through learning from image samples, many features of industry objects, such as shapes, positions, and orientations angles, can be obtained and then can be well utilized to determine whether there is defect or not. However, the robustness and the quickness are not easily achieved in such inspection way. In this work, for solar panel vision inspection, we present an extreme learning machine (ELM and moving least square regression based approach to identify solder joint defect and detect the panel position. Firstly, histogram peaks distribution (HPD and fractional calculus are applied for image preprocessing. Then an ELM-based defective solder joints identification is discussed in detail. Finally, moving least square regression (MLSR algorithm is introduced for solar panel position determination. Experimental results and comparisons show that the proposed ELM and MLSR based inspection method is efficient not only in detection accuracy but also in processing speed.

  19. Jet-Images: Computer Vision Inspired Techniques for Jet Tagging

    CERN Document Server

    Cogan, Josh; Strauss, Emanuel; Schwarztman, Ariel

    2014-01-01

    We introduce a novel approach to jet tagging and classification through the use of techniques inspired by computer vision. Drawing parallels to the problem of facial recognition in images, we define a jet-image using calorimeter towers as the elements of the image and establish jet-image preprocessing methods. For the jet-image processing step, we develop a discriminant for classifying the jet-images derived using Fisher discriminant analysis. The effectiveness of the technique is shown within the context of identifying boosted hadronic W boson decays with respect to a background of quark- and gluon- initiated jets. Using Monte Carlo simulation, we demonstrate that the performance of this technique introduces additional discriminating power over other substructure approaches, and gives significant insight into the internal structure of jets.

  20. Jet-images: computer vision inspired techniques for jet tagging

    Energy Technology Data Exchange (ETDEWEB)

    Cogan, Josh; Kagan, Michael; Strauss, Emanuel; Schwarztman, Ariel [SLAC National Accelerator Laboratory,Menlo Park, CA 94028 (United States)

    2015-02-18

    We introduce a novel approach to jet tagging and classification through the use of techniques inspired by computer vision. Drawing parallels to the problem of facial recognition in images, we define a jet-image using calorimeter towers as the elements of the image and establish jet-image preprocessing methods. For the jet-image processing step, we develop a discriminant for classifying the jet-images derived using Fisher discriminant analysis. The effectiveness of the technique is shown within the context of identifying boosted hadronic W boson decays with respect to a background of quark- and gluon-initiated jets. Using Monte Carlo simulation, we demonstrate that the performance of this technique introduces additional discriminating power over other substructure approaches, and gives significant insight into the internal structure of jets.

  1. Computer Vision and Machine Learning for Autonomous Characterization of AM Powder Feedstocks

    Science.gov (United States)

    DeCost, Brian L.; Jain, Harshvardhan; Rollett, Anthony D.; Holm, Elizabeth A.

    2017-03-01

    By applying computer vision and machine learning methods, we develop a system to characterize powder feedstock materials for metal additive manufacturing (AM). Feature detection and description algorithms are applied to create a microstructural scale image representation that can be used to cluster, compare, and analyze powder micrographs. When applied to eight commercial feedstock powders, the system classifies powder images into the correct material systems with greater than 95% accuracy. The system also identifies both representative and atypical powder images. These results suggest the possibility of measuring variations in powders as a function of processing history, relating microstructural features of powders to properties relevant to their performance in AM processes, and defining objective material standards based on visual images. A significant advantage of the computer vision approach is that it is autonomous, objective, and repeatable.

  2. Computer Vision and Machine Learning for Autonomous Characterization of AM Powder Feedstocks

    Science.gov (United States)

    DeCost, Brian L.; Jain, Harshvardhan; Rollett, Anthony D.; Holm, Elizabeth A.

    2016-12-01

    By applying computer vision and machine learning methods, we develop a system to characterize powder feedstock materials for metal additive manufacturing (AM). Feature detection and description algorithms are applied to create a microstructural scale image representation that can be used to cluster, compare, and analyze powder micrographs. When applied to eight commercial feedstock powders, the system classifies powder images into the correct material systems with greater than 95% accuracy. The system also identifies both representative and atypical powder images. These results suggest the possibility of measuring variations in powders as a function of processing history, relating microstructural features of powders to properties relevant to their performance in AM processes, and defining objective material standards based on visual images. A significant advantage of the computer vision approach is that it is autonomous, objective, and repeatable.

  3. A bio-inspired apposition compound eye machine vision sensor system.

    Science.gov (United States)

    Davis, J D; Barrett, S F; Wright, C H G; Wilcox, M

    2009-12-01

    The Wyoming Information, Signal Processing, and Robotics Laboratory is developing a wide variety of bio-inspired vision sensors. We are interested in exploring the vision system of various insects and adapting some of their features toward the development of specialized vision sensors. We do not attempt to supplant traditional digital imaging techniques but rather develop sensor systems tailor made for the application at hand. We envision that many applications may require a hybrid approach using conventional digital imaging techniques enhanced with bio-inspired analogue sensors. In this specific project, we investigated the apposition compound eye and its characteristics commonly found in diurnal insects and certain species of arthropods. We developed and characterized an array of apposition compound eye-type sensors and tested them on an autonomous robotic vehicle. The robot exhibits the ability to follow a pre-defined target and avoid specified obstacles using a simple control algorithm.

  4. A bio-inspired apposition compound eye machine vision sensor system

    Energy Technology Data Exchange (ETDEWEB)

    Davis, J D [Applied Research Laboratories, University of Texas, 10000 Burnet Rd, Austin, TX 78757 (United States); Barrett, S F; Wright, C H G [Electrical and Computer Engineering, University of Wyoming, Dept 3295 1000 E. University Ave, Laramie, WY 82071 (United States); Wilcox, M, E-mail: steveb@uwyo.ed [Department of Biology, United States Air Force Academy, CO 80840 (United States)

    2009-12-15

    The Wyoming Information, Signal Processing, and Robotics Laboratory is developing a wide variety of bio-inspired vision sensors. We are interested in exploring the vision system of various insects and adapting some of their features toward the development of specialized vision sensors. We do not attempt to supplant traditional digital imaging techniques but rather develop sensor systems tailor made for the application at hand. We envision that many applications may require a hybrid approach using conventional digital imaging techniques enhanced with bio-inspired analogue sensors. In this specific project, we investigated the apposition compound eye and its characteristics commonly found in diurnal insects and certain species of arthropods. We developed and characterized an array of apposition compound eye-type sensors and tested them on an autonomous robotic vehicle. The robot exhibits the ability to follow a pre-defined target and avoid specified obstacles using a simple control algorithm.

  5. Beef identification in industrial slaughterhouses using machine vision techniques

    Directory of Open Access Journals (Sweden)

    J. F. Velez

    2013-10-01

    Full Text Available Accurate individual animal identification provides the producers with useful information to take management decisions about an individual animal or about the complete herd. This identification task is also important to ensure the integrity of the food chain. Consequently, many consumers are turning their attention to issues of quality in animal food production methods. This work describes an implemented solution for individual beef identification, taking in the time from cattle shipment arrival at the slaughterhouse until the animals are slaughtered and cut up. Our beef identification approach is image-based and the pursued goals are the correct automatic extraction and matching between some numeric information extracted from the beef ear-tag and the corresponding one from the Bovine Identification Document (BID. The achieved correct identification results by our method are near 90%, by considering the practical working conditions of slaughterhouses (i.e. problems with dirt and bad illumination conditions. Moreover, the presence of multiple machinery in industrial slaughterhouses make it difficult the use of Radio Frequency Identification (RFID beef tags due to the high risks of interferences between RFID and the other technologies in the workplace. The solution presented is hardware/software since it includes a specialized hardware system that was also developed. Our approach considers the current EU legislation for beef traceability and it reduces the economic cost of individual beef identification with respect to RFID transponders. The system implemented has been in use satisfactorily for more than three years in one of the largest industrial slaughterhouses in Spain.

  6. Study on the Fruit Grading Recognition System Based on Machine Vision

    Directory of Open Access Journals (Sweden)

    Huan Ma

    2015-07-01

    Full Text Available The study proposed that the current development of fruit industry requires the fast and efficient methods to test the varieties of fruits, which can combine the image processing and computer machine vision technology together to be applied in the field of fruit varieties detection, so as to be consistent with this new trend. At present, the determination of these traits were mainly depended on visual grading and manual measurement, which existed the problems such as: slow speed, low accuracy and poor objectivity and so on.

  7. On-line welding quality inspection system for steel pipe based on machine vision

    Science.gov (United States)

    Yang, Yang

    2017-05-01

    In recent years, high frequency welding has been widely used in production because of its advantages of simplicity, reliability and high quality. In the production process, how to effectively control the weld penetration welding, ensure full penetration, weld uniform, so as to ensure the welding quality is to solve the problem of the present stage, it is an important research field in the field of welding technology. In this paper, based on the study of some methods of welding inspection, a set of on-line welding quality inspection system based on machine vision is designed.

  8. Tensor Voting A Perceptual Organization Approach to Computer Vision and Machine Learning

    CERN Document Server

    Mordohai, Philippos

    2006-01-01

    This lecture presents research on a general framework for perceptual organization that was conducted mainly at the Institute for Robotics and Intelligent Systems of the University of Southern California. It is not written as a historical recount of the work, since the sequence of the presentation is not in chronological order. It aims at presenting an approach to a wide range of problems in computer vision and machine learning that is data-driven, local and requires a minimal number of assumptions. The tensor voting framework combines these properties and provides a unified perceptual organiza

  9. Computer vision techniques for the diagnosis of skin cancer

    CERN Document Server

    Celebi, M

    2014-01-01

    The goal of this volume is to summarize the state-of-the-art in the utilization of computer vision techniques in the diagnosis of skin cancer. Malignant melanoma is one of the most rapidly increasing cancers in the world. Early diagnosis is particularly important since melanoma can be cured with a simple excision if detected early. In recent years, dermoscopy has proved valuable in visualizing the morphological structures in pigmented lesions. However, it has also been shown that dermoscopy is difficult to learn and subjective. Newer technologies such as infrared imaging, multispectral imaging, and confocal microscopy, have recently come to the forefront in providing greater diagnostic accuracy. These imaging technologies presented in this book can serve as an adjunct to physicians and  provide automated skin cancer screening. Although computerized techniques cannot as yet provide a definitive diagnosis, they can be used to improve biopsy decision-making as well as early melanoma detection, especially for pa...

  10. Template matching techniques in computer vision theory and practice

    CERN Document Server

    Brunelli, Roberto

    2009-01-01

    The detection and recognition of objects in images is a key research topic in the computer vision community.  Within this area, face recognition and interpretation has attracted increasing attention owing to the possibility of unveiling human perception mechanisms, and for the development of practical biometric systems. This book and the accompanying website, focus on template matching, a subset of object recognition techniques of wide applicability, which has proved to be particularly effective for face recognition applications. Using examples from face processing tasks throughout the book to illustrate more general object recognition approaches, Roberto Brunelli: examines the basics of digital image formation, highlighting points critical to the task of template matching;presents basic and  advanced template matching techniques, targeting grey-level images, shapes and point sets;discusses recent pattern classification paradigms from a template matching perspective;illustrates the development of a real fac...

  11. Vision-Based Perception and Classification of Mosquitoes Using Support Vector Machine

    Directory of Open Access Journals (Sweden)

    Masataka Fuchida

    2017-01-01

    Full Text Available The need for a novel automated mosquito perception and classification method is becoming increasingly essential in recent years, with steeply increasing number of mosquito-borne diseases and associated casualties. There exist remote sensing and GIS-based methods for mapping potential mosquito inhabitants and locations that are prone to mosquito-borne diseases, but these methods generally do not account for species-wise identification of mosquitoes in closed-perimeter regions. Traditional methods for mosquito classification involve highly manual processes requiring tedious sample collection and supervised laboratory analysis. In this research work, we present the design and experimental validation of an automated vision-based mosquito classification module that can deploy in closed-perimeter mosquito inhabitants. The module is capable of identifying mosquitoes from other bugs such as bees and flies by extracting the morphological features, followed by support vector machine-based classification. In addition, this paper presents the results of three variants of support vector machine classifier in the context of mosquito classification problem. This vision-based approach to the mosquito classification problem presents an efficient alternative to the conventional methods for mosquito surveillance, mapping and sample image collection. Experimental results involving classification between mosquitoes and a predefined set of other bugs using multiple classification strategies demonstrate the efficacy and validity of the proposed approach with a maximum recall of 98%.

  12. Machine Vision Analysis of Characteristics and Image Information Base Construction for Hybrid Rice Seed

    Institute of Scientific and Technical Information of China (English)

    2005-01-01

    Incompletely closed glumes, germination on panicle and disease ara thrae important factors causing poor seed quality of hybrid rice. To determine how many and which categories should be classified to meet the demand for seed in rice production, the effects of various degrees of incompletely closed glumes, germination on panicle and disease on germination percentage at the harvest and after storage for six months were studied by standard germination percentage test. Six categories of seeds with germ (germinated seeds), severe disease, incompletely closed glumes, spot disease, fine fissure and normal seeds were inspected and then treated separately. Images of the five hybrid rice seed (Jinyou 402, Shanyou 10, Zhongyou 27, Jiayou 99 and Ⅱ you 3207) were acquired with a self-developed machine vision system. Each image could be processed to get the feature values of seed region such as length, width, ratio of length to width, araa, solidity and hue. Then all the images of normal seeds were calculated to draw the feature value ranges of each hybrid rice variety. Finally, an image information base that stores typical images and related feature values of each variety was established. This image information base can help us to identify the classification limit of characteristics, and provide the reference of the threshold selection. The management of large numbers of pictures and the addition of new varieties have been supported. The research laid a foundation for extracting image features of hybrid rice seed, which is a key approach to futura quality inspection with machine vision.

  13. Computer vision techniques for rotorcraft low-altitude flight

    Science.gov (United States)

    Sridhar, Banavar; Cheng, Victor H. L.

    1988-01-01

    A description is given of research that applies techniques from computer vision to automation of rotorcraft navigation. The effort emphasizes the development of a methodology for detecting the ranges to obstacles in the region of interest based on the maximum utilization of passive sensors. The range map derived from the obstacle detection approach can be used as obstacle data for the obstacle avoidance in an automataic guidance system and as advisory display to the pilot. The lack of suitable flight imagery data, however, presents a problem in the verification of concepts for obstacle detection. This problem is being addressed by the development of an adequate flight database and by preprocessing of currently available flight imagery. Some comments are made on future work and how research in this area relates to the guidance of other autonomous vehicles.

  14. Computer vision techniques for rotorcraft low-altitude flight

    Science.gov (United States)

    Sridhar, Banavar; Cheng, Victor H. L.

    1988-01-01

    A description is given of research that applies techniques from computer vision to automation of rotorcraft navigation. The effort emphasizes the development of a methodology for detecting the ranges to obstacles in the region of interest based on the maximum utilization of passive sensors. The range map derived from the obstacle detection approach can be used as obstacle data for the obstacle avoidance in an automataic guidance system and as advisory display to the pilot. The lack of suitable flight imagery data, however, presents a problem in the verification of concepts for obstacle detection. This problem is being addressed by the development of an adequate flight database and by preprocessing of currently available flight imagery. Some comments are made on future work and how research in this area relates to the guidance of other autonomous vehicles.

  15. Analysing CMS transfers using Machine Learning techniques

    CERN Document Server

    Diotalevi, Tommaso

    2016-01-01

    LHC experiments transfer more than 10 PB/week between all grid sites using the FTS transfer service. In particular, CMS manages almost 5 PB/week of FTS transfers with PhEDEx (Physics Experiment Data Export). FTS sends metrics about each transfer (e.g. transfer rate, duration, size) to a central HDFS storage at CERN. The work done during these three months, here as a Summer Student, involved the usage of ML techniques, using a CMS framework called DCAFPilot, to process this new data and generate predictions of transfer latencies on all links between Grid sites. This analysis will provide, as a future service, the necessary information in order to proactively identify and maybe fix latency issued transfer over the WLCG.

  16. Machine Learning Techniques in Optimal Design

    Science.gov (United States)

    Cerbone, Giuseppe

    1992-01-01

    Many important applications can be formalized as constrained optimization tasks. For example, we are studying the engineering domain of two-dimensional (2-D) structural design. In this task, the goal is to design a structure of minimum weight that bears a set of loads. A solution to a design problem in which there is a single load (L) and two stationary support points (S1 and S2) consists of four members, E1, E2, E3, and E4 that connect the load to the support points is discussed. In principle, optimal solutions to problems of this kind can be found by numerical optimization techniques. However, in practice [Vanderplaats, 1984] these methods are slow and they can produce different local solutions whose quality (ratio to the global optimum) varies with the choice of starting points. Hence, their applicability to real-world problems is severely restricted. To overcome these limitations, we propose to augment numerical optimization by first performing a symbolic compilation stage to produce: (a) objective functions that are faster to evaluate and that depend less on the choice of the starting point and (b) selection rules that associate problem instances to a set of recommended solutions. These goals are accomplished by successive specializations of the problem class and of the associated objective functions. In the end, this process reduces the problem to a collection of independent functions that are fast to evaluate, that can be differentiated symbolically, and that represent smaller regions of the overall search space. However, the specialization process can produce a large number of sub-problems. This is overcome by deriving inductively selection rules which associate problems to small sets of specialized independent sub-problems. Each set of candidate solutions is chosen to minimize a cost function which expresses the tradeoff between the quality of the solution that can be obtained from the sub-problem and the time it takes to produce it. The overall solution

  17. Memory Based Machine Intelligence Techniques in VLSI hardware

    OpenAIRE

    James, Alex Pappachen

    2012-01-01

    We briefly introduce the memory based approaches to emulate machine intelligence in VLSI hardware, describing the challenges and advantages. Implementation of artificial intelligence techniques in VLSI hardware is a practical and difficult problem. Deep architectures, hierarchical temporal memories and memory networks are some of the contemporary approaches in this area of research. The techniques attempt to emulate low level intelligence tasks and aim at providing scalable solutions to high ...

  18. Memory Based Machine Intelligence Techniques in VLSI hardware

    CERN Document Server

    James, Alex Pappachen

    2012-01-01

    We briefly introduce the memory based approaches to emulate machine intelligence in VLSI hardware, describing the challenges and advantages. Implementation of artificial intelligence techniques in VLSI hardware is a practical and difficult problem. Deep architectures, hierarchical temporal memories and memory networks are some of the contemporary approaches in this area of research. The techniques attempt to emulate low level intelligence tasks and aim at providing scalable solutions to high level intelligence problems such as sparse coding and contextual processing.

  19. Fast and intuitive programming of adaptive laser cutting of lace enabled by machine vision

    Science.gov (United States)

    Vaamonde, Iago; Souto-López, Álvaro; García-Díaz, Antón

    2015-07-01

    A machine vision system has been developed, validated, and integrated in a commercial laser robot cell. It permits an offline graphical programming of laser cutting of lace. The user interface allows loading CAD designs and aligning them with images of lace pieces. Different thread widths are discriminated to generate proper cutting program templates. During online operation, the system aligns CAD models of pieces and lace images, pre-checks quality of lace cuts and adapts laser parameters to thread widths. For pieces detected with the required quality, the program template is adjusted by transforming the coordinates of every trajectory point. A low-cost lace feeding system was also developed for demonstration of full process automation.

  20. Image processing with genetic algorithm in a raisin sorting system based on machine vision

    Science.gov (United States)

    Abbasgholipour, Mahdi; Alasti, Behzad Mohammadi; Abbasgholipour, Vahdi; Derakhshan, Ali; Abbasgholipour, Mohammad; Rahmatfam, Sharmin; Rahmatfam, Sheyda; Habibifar, Rahim

    2012-04-01

    This study was undertaken to develop machine vision-based raisin detection technology. Supervised color image segmentation using a Permutation-coded Genetic Algorithm (GA) identifying regions in Hue-Saturation-Intensity (HSI) color space (GAHSI) for desired and undesired raisin detection was successfully implemented. Images were captured to explore the possibility of using GAHSI to locate desired raisin and undesired raisin regions in color space simultaneously. In this research, images were processed separately using three segmentation method, K-Means clustering in L*a*b* color space and GAHSI for single image, GA for single image in Red-Green-Blue (RGB) color space (GARGB). The GAHSI results provided evidence for the existence and separability of such regions. When compared with cluster analysis-based segmentation results, the GAHSI method showed no significant difference.

  1. Development of an automatic weld surface appearance inspection system using machine vision

    Institute of Scientific and Technical Information of China (English)

    Lin Sanbao; Fu Xibin; Fan Chenglei; Yang Chunli; Luo Lu

    2009-01-01

    In this paper, an automatic inspection system for weld surface appearance using machine vision has been developed to recognize weld surface defects such as porosities, cracks, etc. It can replace conventional manual visual inspection method, which is tedious, time-consuming, subjective, experience-depended, and sometimes biased. The system consists of a CCD camera, a self-designed annular light source, a sensor controller, a frame grabbing card, a computer and so on. After acquiring weld surface appearance images using CCD, the images are preprocessed using median filtering and a series of image enhancement algorithms. Then a dynamic threshold and morphology algorithms are applied to segment defect object. Finally, defect features information is obtained by eight neighborhoods boundary chain code algorithm. Experimental results show that the developed system is capable of inspecting most surface defects such as porosities, cracks with high reliability and accuracy.

  2. A real-time surface inspection system for precision steel balls based on machine vision

    Science.gov (United States)

    Chen, Yi-Ji; Tsai, Jhy-Cherng; Hsu, Ya-Chen

    2016-07-01

    Precision steel balls are one of the most fundament components for motion and power transmission parts and they are widely used in industrial machinery and the automotive industry. As precision balls are crucial for the quality of these products, there is an urgent need to develop a fast and robust system for inspecting defects of precision steel balls. In this paper, a real-time system for inspecting surface defects of precision steel balls is developed based on machine vision. The developed system integrates a dual-lighting system, an unfolding mechanism and inspection algorithms for real-time signal processing and defect detection. The developed system is tested under feeding speeds of 4 pcs s-1 with a detection rate of 99.94% and an error rate of 0.10%. The minimum detectable surface flaw area is 0.01 mm2, which meets the requirement for inspecting ISO grade 100 precision steel balls.

  3. Development of a machine vision system for a real-time precision sprayer

    Science.gov (United States)

    Bossu, Jérémie; Gée, Christelle; Truchetet, Frédéric

    2007-01-01

    In the context of precision agriculture, we have developed a machine vision system for a real time precision sprayer. From a monochrome CCD camera located in front of the tractor, the discrimination between crop and weeds is obtained with an image processing based on spatial information using a Gabor filter. This method allows to detect the periodic signals from the non periodic one and it enables to enhance the crop rows whereas weeds have patchy distribution. Thus, weed patches were clearly identified by a blob-coloring method. Finally, we use a pinhole model to transform the weed patch coordinates image in world coordinates in order to activate the right electro-pneumatic valve of the sprayer at the right moment.

  4. Machine vision method for online surface inspection of easy open can ends

    Science.gov (United States)

    Mariño, Perfecto; Pastoriza, Vicente; Santamaría, Miguel

    2006-10-01

    Easy open can end manufacturing process in the food canning sector currently makes use of a manual, non-destructive testing procedure to guarantee can end repair coating quality. This surface inspection is based on a visual inspection made by human inspectors. Due to the high production rate (100 to 500 ends per minute) only a small part of each lot is verified (statistical sampling), then an automatic, online, inspection system, based on machine vision, has been developed to improve this quality control. The inspection system uses a fuzzy model to make the acceptance/rejection decision for each can end from the information obtained by the vision sensor. In this work, the inspection method is presented. This surface inspection system checks the total production, classifies the ends in agreement with an expert human inspector, supplies interpretability to the operators in order to find out the failure causes and reduce mean time to repair during failures, and allows to modify the minimum can end repair coating quality.

  5. Infrared machine vision system for the automatic detection of olive fruit quality.

    Science.gov (United States)

    Guzmán, Elena; Baeten, Vincent; Pierna, Juan Antonio Fernández; García-Mesa, José A

    2013-11-15

    External quality is an important factor in the extraction of olive oil and the marketing of olive fruits. The appearance and presence of external damage are factors that influence the quality of the oil extracted and the perception of consumers, determining the level of acceptance prior to purchase in the case of table olives. The aim of this paper is to report on artificial vision techniques developed for the online estimation of olive quality and to assess the effectiveness of these techniques in evaluating quality based on detecting external defects. This method of classifying olives according to the presence of defects is based on an infrared (IR) vision system. Images of defects were acquired using a digital monochrome camera with band-pass filters on near-infrared (NIR). The original images were processed using segmentation algorithms, edge detection and pixel value intensity to classify the whole fruit. The detection of the defect involved a pixel classification procedure based on nonparametric models of the healthy and defective areas of olives. Classification tests were performed on olives to assess the effectiveness of the proposed method. This research showed that the IR vision system is a useful technology for the automatic assessment of olives that has the potential for use in offline inspection and for online sorting for defects and the presence of surface damage, easily distinguishing those that do not meet minimum quality requirements. Crown Copyright © 2013 Published by Elsevier B.V. All rights reserved.

  6. A machine vision system for micro-EDM based on linux

    Science.gov (United States)

    Guo, Rui; Zhao, Wansheng; Li, Gang; Li, Zhiyong; Zhang, Yong

    2006-11-01

    Due to the high precision and good surface quality that it can give, Electrical Discharge Machining (EDM) is potentially an important process for the fabrication of micro-tools and micro-components. However, a number of issues remain unsolved before micro-EDM becomes a reliable process with repeatable results. To deal with the difficulties in micro electrodes on-line fabrication and tool wear compensation, a micro-EDM machine vision system is developed with a Charge Coupled Device (CCD) camera, with an optical resolution of 1.61μm and an overall magnification of 113~729. Based on the Linux operating system, an image capturing program is developed with the V4L2 API, and an image processing program is exploited by using OpenCV. The contour of micro electrodes can be extracted by means of the Canny edge detector. Through the system calibration, the micro electrodes diameter can be measured on-line. Experiments have been carried out to prove its performance, and the reasons of measurement error are also analyzed.

  7. Research on automatic inspection system for defects on precise optical surface based on machine vision

    Institute of Scientific and Technical Information of China (English)

    WANG Xue; XIE Zhi-jiang

    2006-01-01

    In manufacture of precise optical products, it is important to inspect and classify the potential defects existing on the products' surfaces after precise machining in order to obtain high quality in both functionality and aesthetics. The existing methods for detecting and classifying defects all are low accuracy or efficiency or high cost in inspection process. In this paper, a new inspection system based on machine vision has been introduced, which uses automatic focusing and image mosaic technologies to rapidly acquire distinct surface image, and employs Case-Based Reasoning(CBR)method in defects classification. A modificatory fuzzy similarity algorithm in CBR has been adopted for more quick and robust need of pattern recognition in practice inspection. Experiments show that the system can inspect surface diameter of 500mm in half an hour with resolving power of 0.8μm diameter according to digs or 0.5μm transverse width according to scratches. The proposed inspection principles and methods not only have meet manufacturing requirements of precise optical products, but also have great potential applications in other fields of precise surface inspection.

  8. Data mining practical machine learning tools and techniques

    CERN Document Server

    Witten, Ian H

    2005-01-01

    As with any burgeoning technology that enjoys commercial attention, the use of data mining is surrounded by a great deal of hype. Exaggerated reports tell of secrets that can be uncovered by setting algorithms loose on oceans of data. But there is no magic in machine learning, no hidden power, no alchemy. Instead there is an identifiable body of practical techniques that can extract useful information from raw data. This book describes these techniques and shows how they work. The book is a major revision of the first edition that appeared in 1999. While the basic core remains the same

  9. Computer vision techniques for rotorcraft low altitude flight

    Science.gov (United States)

    Sridhar, Banavar

    1990-01-01

    Rotorcraft operating in high-threat environments fly close to the earth's surface to utilize surrounding terrain, vegetation, or manmade objects to minimize the risk of being detected by an enemy. Increasing levels of concealment are achieved by adopting different tactics during low-altitude flight. Rotorcraft employ three tactics during low-altitude flight: low-level, contour, and nap-of-the-earth (NOE). The key feature distinguishing the NOE mode from the other two modes is that the whole rotorcraft, including the main rotor, is below tree-top whenever possible. This leads to the use of lateral maneuvers for avoiding obstacles, which in fact constitutes the means for concealment. The piloting of the rotorcraft is at best a very demanding task and the pilot will need help from onboard automation tools in order to devote more time to mission-related activities. The development of an automation tool which has the potential to detect obstacles in the rotorcraft flight path, warn the crew, and interact with the guidance system to avoid detected obstacles, presents challenging problems. Research is described which applies techniques from computer vision to automation of rotorcraft navigtion. The effort emphasizes the development of a methodology for detecting the ranges to obstacles in the region of interest based on the maximum utilization of passive sensors. The range map derived from the obstacle-detection approach can be used as obstacle data for the obstacle avoidance in an automatic guidance system and as advisory display to the pilot. The lack of suitable flight imagery data presents a problem in the verification of concepts for obstacle detection. This problem is being addressed by the development of an adequate flight database and by preprocessing of currently available flight imagery. The presentation concludes with some comments on future work and how research in this area relates to the guidance of other autonomous vehicles.

  10. An Automated Recognition of Fake or Destroyed Indian Currency Notes in Machine Vision

    Directory of Open Access Journals (Sweden)

    Sanjana

    2012-04-01

    Full Text Available Almost every country in the world face the problem of counterfeitcurrency notes, but in India the problem is acute as the country ishit hard by this evil practice. Fake notes in India in denominationsof Rs.100, 500 and 1000 are being flooded into the system. Inorder to deal with such type of problems, an automated recognitionof currency notes in introduced by with the help of featureextraction, classification based in SVM, Neural Nets, and heuristicapproach. This technique is also subjected with the computervision where all processing with the image is done by machine.The machine is fitted with a CDD camera which will scan theimage of the currency note considering the dimensions of thebanknote and software will process the image segments with thehelp of SVM and character recognition methods. ANN is alsointroduced in this paper to train the data and classify the segmentsusing its datasets. To implement this design we are dealing withMATLAB Tool.

  11. Decision Support System for Diabetes Mellitus through Machine Learning Techniques

    Directory of Open Access Journals (Sweden)

    Tarik A. Rashid

    2016-07-01

    Full Text Available recently, the diseases of diabetes mellitus have grown into extremely feared problems that can have damaging effects on the health condition of their sufferers globally. In this regard, several machine learning models have been used to predict and classify diabetes types. Nevertheless, most of these models attempted to solve two problems; categorizing patients in terms of diabetic types and forecasting blood surge rate of patients. This paper presents an automatic decision support system for diabetes mellitus through machine learning techniques by taking into account the above problems, plus, reflecting the skills of medical specialists who believe that there is a great relationship between patient’s symptoms with some chronic diseases and the blood sugar rate. Data sets are collected from Layla Qasim Clinical Center in Kurdistan Region, then, the data is cleaned and proposed using feature selection techniques such as Sequential Forward Selection and the Correlation Coefficient, finally, the refined data is fed into machine learning models for prediction, classification, and description purposes. This system enables physicians and doctors to provide diabetes mellitus (DM patients good health treatments and recommendations.

  12. Analysis of Machine Learning Techniques for Heart Failure Readmissions.

    Science.gov (United States)

    Mortazavi, Bobak J; Downing, Nicholas S; Bucholz, Emily M; Dharmarajan, Kumar; Manhapra, Ajay; Li, Shu-Xia; Negahban, Sahand N; Krumholz, Harlan M

    2016-11-01

    The current ability to predict readmissions in patients with heart failure is modest at best. It is unclear whether machine learning techniques that address higher dimensional, nonlinear relationships among variables would enhance prediction. We sought to compare the effectiveness of several machine learning algorithms for predicting readmissions. Using data from the Telemonitoring to Improve Heart Failure Outcomes trial, we compared the effectiveness of random forests, boosting, random forests combined hierarchically with support vector machines or logistic regression (LR), and Poisson regression against traditional LR to predict 30- and 180-day all-cause readmissions and readmissions because of heart failure. We randomly selected 50% of patients for a derivation set, and a validation set comprised the remaining patients, validated using 100 bootstrapped iterations. We compared C statistics for discrimination and distributions of observed outcomes in risk deciles for predictive range. In 30-day all-cause readmission prediction, the best performing machine learning model, random forests, provided a 17.8% improvement over LR (mean C statistics, 0.628 and 0.533, respectively). For readmissions because of heart failure, boosting improved the C statistic by 24.9% over LR (mean C statistic 0.678 and 0.543, respectively). For 30-day all-cause readmission, the observed readmission rates in the lowest and highest deciles of predicted risk with random forests (7.8% and 26.2%, respectively) showed a much wider separation than LR (14.2% and 16.4%, respectively). Machine learning methods improved the prediction of readmission after hospitalization for heart failure compared with LR and provided the greatest predictive range in observed readmission rates. © 2016 American Heart Association, Inc.

  13. Development of a model of machine hand eye coordination and program specifications for a topological machine vision system

    Science.gov (United States)

    1972-01-01

    A unified approach to computer vision and manipulation is developed which is called choreographic vision. In the model, objects to be viewed by a projected robot in the Viking missions to Mars are seen as objects to be manipulated within choreographic contexts controlled by a multimoded remote, supervisory control system on Earth. A new theory of context relations is introduced as a basis for choreographic programming languages. A topological vision model is developed for recognizing objects by shape and contour. This model is integrated with a projected vision system consisting of a multiaperture image dissector TV camera and a ranging laser system. System program specifications integrate eye-hand coordination and topological vision functions and an aerospace multiprocessor implementation is described.

  14. Tomato grading system using machine vision technology and neuro-fuzzy networks (ANFIS

    Directory of Open Access Journals (Sweden)

    H Izadi

    2016-04-01

    Full Text Available Introduction: The quality of agricultural products is associated with their color, size and health, grading of fruits is regarded as an important step in post-harvest processing. In most cases, manual sorting inspections depends on available manpower, time consuming and their accuracy could not be guaranteed. Machine Vision is known to be a useful tool for external features measurement (e.g. size, shape, color and defects and in recent century, Machine Vision technology has been used for shape sorting. The main purpose of this study was to develop new method for tomato grading and sorting using Neuro-fuzzy system (ANFIS and to compare the accuracies of the ANFIS predicted results with those suggested by a human expert. Materials and Methods: In this study, a total of 300 image of tomatoes (Rev ground was randomly harvested, classified in 3 ripeness stage, 3 sizes and 2 health. The grading and sorting mechanism consisted of a lighting chamber (cloudy sky, lighting source and a digital camera connected to a computer. The images were recorded in a special chamber with an indirect radiation (cloudy sky with four florescent lampson each sides and camera lens was entire to lighting chamber by a hole which was only entranced to outer and covered by a camera lens. Three types of features were extracted from final images; Shap, color and texture. To receive these features, we need to have images both in color and binary format in procedure shown in Figure 1. For the first group; characteristics of the images were analysis that could offer information an surface area (S.A., maximum diameter (Dmax, minimum diameter (Dmin and average diameters. Considering to the importance of the color in acceptance of food quality by consumers, the following classification was conducted to estimate the apparent color of the tomato; 1. Classified as red (red > 90% 2. Classified as red light (red or bold pink 60-90% 3. Classified as pink (red 30-60% 4. Classified as Turning

  15. Dropout Prediction in E-Learning Courses through the Combination of Machine Learning Techniques

    Science.gov (United States)

    Lykourentzou, Ioanna; Giannoukos, Ioannis; Nikolopoulos, Vassilis; Mpardis, George; Loumos, Vassili

    2009-01-01

    In this paper, a dropout prediction method for e-learning courses, based on three popular machine learning techniques and detailed student data, is proposed. The machine learning techniques used are feed-forward neural networks, support vector machines and probabilistic ensemble simplified fuzzy ARTMAP. Since a single technique may fail to…

  16. Dropout Prediction in E-Learning Courses through the Combination of Machine Learning Techniques

    Science.gov (United States)

    Lykourentzou, Ioanna; Giannoukos, Ioannis; Nikolopoulos, Vassilis; Mpardis, George; Loumos, Vassili

    2009-01-01

    In this paper, a dropout prediction method for e-learning courses, based on three popular machine learning techniques and detailed student data, is proposed. The machine learning techniques used are feed-forward neural networks, support vector machines and probabilistic ensemble simplified fuzzy ARTMAP. Since a single technique may fail to…

  17. A distortion-correction method for workshop machine vision measurement system

    Science.gov (United States)

    Chen, Ruwen; Huang, Ren; Zhang, Zhisheng; Shi, Jinfei; Chen, Zixin

    2008-12-01

    The application of machine vision measurement system is developing rapidly in industry for its non-contact, high speed, and automation characteristics. However, there are nonlinear distortions in the images which are vital to measuring precision, for the object dimensions are determined by the image properties. People are interested in this problem and put forward some physical model based correction methods which are widely applied in engineering. However, these methods are difficult to be realized in workshop for the images are non-repetitive interfered by the coupled dynamic factors, which means the real imaging is a stochastic process. A new nonlinear distortion correction method based on a VNAR model (Volterra series based nonlinear auto-regressive time series model) is proposed to describe the distorted image edge series. The model parameter vectors are achieved by the laws of data. The distortion-free edges are obtained after model filtering and the image dimensions are transformed to measuring dimensions. Experimental results show that the method is reliable and can be applied to engineering.

  18. Integrating Symbolic and Statistical Methods for Testing Intelligent Systems Applications to Machine Learning and Computer Vision

    Energy Technology Data Exchange (ETDEWEB)

    Jha, Sumit Kumar [University of Central Florida, Orlando; Pullum, Laura L [ORNL; Ramanathan, Arvind [ORNL

    2016-01-01

    Embedded intelligent systems ranging from tiny im- plantable biomedical devices to large swarms of autonomous un- manned aerial systems are becoming pervasive in our daily lives. While we depend on the flawless functioning of such intelligent systems, and often take their behavioral correctness and safety for granted, it is notoriously difficult to generate test cases that expose subtle errors in the implementations of machine learning algorithms. Hence, the validation of intelligent systems is usually achieved by studying their behavior on representative data sets, using methods such as cross-validation and bootstrapping.In this paper, we present a new testing methodology for studying the correctness of intelligent systems. Our approach uses symbolic decision procedures coupled with statistical hypothesis testing to. We also use our algorithm to analyze the robustness of a human detection algorithm built using the OpenCV open-source computer vision library. We show that the human detection implementation can fail to detect humans in perturbed video frames even when the perturbations are so small that the corresponding frames look identical to the naked eye.

  19. Colour Model for Outdoor Machine Vision for Tropical Regions and its Comparison with the CIE Model

    Energy Technology Data Exchange (ETDEWEB)

    Sahragard, Nasrolah; Ramli, Abdul Rahman B [Institute of Advanced Technology, Universiti Putra Malaysia 43400 Serdang, Selangor (Malaysia); Marhaban, Mohammad Hamiruce [Department of Electrical and Electronic Engineering, Faculty of Engineering, Universiti Putra Malaysia 43400 Serdang, Selangor (Malaysia); Mansor, Shattri B, E-mail: sahragard@yahoo.com [Department of Civil Engineering, Faculty of Engineering, Universiti Putra Malaysia 43400 Serdang, Selangor (Malaysia)

    2011-02-15

    Accurate modeling of daylight and surface reflectance are very useful for most outdoor machine vision applications specifically those which are based on color recognition. Existing daylight CIE model has drawbacks that limit its ability to predict the color of incident light. These limitations include lack of considering ambient light, effects of light reflected off the ground, and context specific information. Previously developed color model is only tested for a few geographical places in North America and its accountability is under question for other places in the world. Besides, existing surface reflectance models are not easily applied to outdoor images. A reflectance model with combined diffuse and specular reflection in normalized HSV color space could be used to predict color. In this paper, a new daylight color model showing the color of daylight for a broad range of sky conditions is developed which will suit weather conditions of tropical places such as Malaysia. A comparison of this daylight color model and daylight CIE model will be discussed. The colors of matte and specular surfaces have been estimated by use of the developed color model and surface reflection function in this paper. The results are shown to be highly reliable.

  20. A New High-Speed Foreign Fiber Detection System with Machine Vision

    Directory of Open Access Journals (Sweden)

    Zhiguo Chen

    2010-01-01

    Full Text Available A new high-speed foreign fiber detection system with machine vision is proposed for removing foreign fibers from raw cotton using optimal hardware components and appropriate algorithms designing. Starting from a specialized lens of 3-charged couple device (CCD camera, the system applied digital signal processor (DSP and field-programmable gate array (FPGA on image acquisition and processing illuminated by ultraviolet light, so as to identify transparent objects such as polyethylene and polypropylene fabric from cotton tuft flow by virtue of the fluorescent effect, until all foreign fibers that have been blown away safely by compressed air quality can be achieved. An image segmentation algorithm based on fast wavelet transform is proposed to identify block-like foreign fibers, and an improved canny detector is also developed to segment wire-like foreign fibers from raw cotton. The procedure naturally provides color image segmentation method with region growing algorithm for better adaptability. Experiments on a variety of images show that the proposed algorithms can effectively segment foreign fibers from test images under various circumstances.

  1. Histogram of Intensity Feature Extraction for Automatic Plastic Bottle Recycling System Using Machine Vision

    Directory of Open Access Journals (Sweden)

    Suzaimah Ramli

    2008-01-01

    Full Text Available Currently, many recycling activities adopt manual sorting for plastic recycling that relies on plant personnel who visually identify and pick plastic bottles as they travel along the conveyor belt. These bottles are then sorted into the respective containers. Manual sorting may not be a suitable option for recycling facilities of high throughput. It has also been noted that the high turnover among sorting line workers had caused difficulties in achieving consistency in the plastic separation process. As a result, an intelligent system for automated sorting is greatly needed to replace manual sorting system. The core components of machine vision for this intelligent sorting system is the image recognition and classification. In this research, the overall plastic bottle sorting system is described. Additionally, the feature extraction algorithm used is discussed in detail since it is the core component of the overall system that determines the success rate. The performance of the proposed feature extractions were evaluated in terms of classification accuracy and result obtained showed an accuracy of more than 80%.

  2. Machine Vision Based Measurement of Dynamic Contact Angles in Microchannel Flows

    Institute of Scientific and Technical Information of China (English)

    Valtteri Heiskanen; Kalle Marjanen; Pasi Kallio

    2008-01-01

    When characterizing flows in miniaturized channels, the determination of the dynamic contact angle is important. By measuring the dynamic contact angle, the flow properties of the flowing liquid and the effect of material properties on the flow can be characterized. A machine vision based system to measure the contact angle of front or rear menisci of a moving liquid plug is described in this article. In this research, transparent flow channels fabricated on thermoplastic polymer and scaled with an adhesive tape are used. The transparency of the channels enables image based monitoring and measurement of flow variables, including the dynamic contact angle. It is shown that the dynamic angle can be measured from a liquid flow in a channel using the image based measurement system. An image processing algorithm has been developed in a MATLAB environment. Im-ages are taken using a CCD camera and the channels are illuminated using a custom made ring light. Two fitting methods, a circle and two parabolas, are experimented and the results are compared in the measurement of the dynamic contact angles.

  3. Development of an evaluation technique for human-machine interface

    Energy Technology Data Exchange (ETDEWEB)

    Min, Dae Hwan; Koo, Sang Hui; Ahn, Won Yeong; Ryu, Yeong Shin [Korea Univ., Seoul (Korea, Republic of)

    1997-07-15

    The purpose of this study is two-fold : firstly to establish an evaluation technique for HMI(Human Machine Interface) in NPPs(Nuclear Power Plants) and secondly to develop an architecture of a support system which can be used for the evaluation of HMI. In order to establish an evaluation technique, this study conducted literature review on basic theories of cognitive science studies and summarized the cognitive characteristics of humans. This study also surveyed evaluation techniques of HMI in general, and reviewed studies on the evaluation of HMI in NPPs. On the basis of this survey, the study established a procedure for the evaluation of HMI in NPPs in Korea and laid a foundation for empirical verification.

  4. Machine vision assisted analysis of structure-localization relationships in a combinatorial library of prospective bioimaging probes

    OpenAIRE

    Shedden, Kerby; Li, Qian; Liu, Fangyi; Chang, Young Tae; Rosania, Gus R.

    2009-01-01

    With a combinatorial library of bioimaging probes, it is now possible to use machine vision to analyze the contribution of different building blocks of the molecules to their cell-associated visual signals. For athis purpose, cell-permeant, fluorescent styryl molecules were synthesized by condensation of 168 aldehyde with 8 pyridinium/quinolinium building blocks. Images of cells incubated with fluorescent molecules were acquired with a high content screening instrument. Chemical and image fea...

  5. Waveform interative techniques for device transient simulation on parallel machines

    Energy Technology Data Exchange (ETDEWEB)

    Lumsdaine, A. [Univ. of Notre Dame, IN (United States); Reichelt, M.W. [Massachusetts Institute of Technology, Cambridge, MA (United States)

    1993-12-31

    In this paper we describe our experiences with parallel implementations of several different waveform algorithms for performing transient simulation of semiconductor devices. Because of their inherent computation and communication structure, waveform methods are well suited to MIMD-type parallel machines having a high communication latency - such as a cluster of workstations. Experimental results using pWORDS, a parallel waveform-based device transient simulation program, in conjunction with PVM running on a cluster of eight workstations demonstrate that parallel waveform techniques are an efficient and faster alternative to standard simulation algorithms.

  6. A machine vision approach to seam tracking in real-time in PAW of large-diameter stainless steel tube

    Institute of Scientific and Technical Information of China (English)

    葛景国; 朱政强; 何德孚; 陈立功

    2004-01-01

    Manual monitoring and seam tracking through watching weld pool images in real-time, by naked eyes or by industrial TV, are experience-depended, subjective, labor intensive, and sometimes biased. So it is necessary to realize the automation of computer-aided seam tracking. A PAW (plasma arc welding) seam tracking system was developed, which senses the molten pool and the seam in one frame by a vision sensor, and then detects the seam deviation to adjust the work piece motion adaptively to the seam position sensed by vision sensor. A novel molten pool area image-processing algorithm based on machine vision was proposed. The algorithm processes each image at the speed of 20 frames/second in real-time to extract three feature variables to get the seam deviation. It is proved experimentally that the algorithm is very fast and effective. Issues related to the algorithm are also discussed.

  7. People Recognition for Loja ECU911 applying artificial vision techniques

    Directory of Open Access Journals (Sweden)

    Diego Cale

    2016-05-01

    Full Text Available This article presents a technological proposal based on artificial vision which aims to search people in an intelligent way by using IP video cameras. Currently, manual searching process is time and resource demanding in contrast to automated searching one, which means that it could be replaced. In order to obtain optimal results, three different techniques of artificial vision were analyzed (Eigenfaces, Fisherfaces, Local Binary Patterns Histograms. The selection process considered factors like lighting changes, image quality and changes in the angle of focus of the camera. Besides, a literature review was conducted to evaluate several points of view regarding artificial vision techniques.

  8. Application of generalized Hough transform for detecting sugar beet plant from weed using machine vision method

    Directory of Open Access Journals (Sweden)

    A Bakhshipour Ziaratgahi

    2017-05-01

    Full Text Available Introduction Sugar beet (Beta vulgaris L. as the second most important world’s sugar source after sugarcane is one of the major industrial crops. The presence of weeds in sugar beet fields, especially at early growth stages, results in a substantial decrease in the crop yield. It is very important to efficiently eliminate weeds at early growing stages. The first step of precision weed control is accurate detection of weeds location in the field. This operation can be performed by machine vision techniques. Hough transform is one of the shape feature extraction methods for object tracking in image processing which is basically used to identify lines or other geometrical shapes in an image. Generalized Hough transform (GHT is a modified version of the Hough transform used not only for geometrical forms, but also for detecting any arbitrary shape. This method is based on a pattern matching principle that uses a set of vectors of feature points (usually object edge points to a reference point to construct a pattern. By comparing this pattern with a set pattern, the desired shape is detected. The aim of this study was to identify the sugar beet plant from some common weeds in a field using the GHT. Materials and Methods Images required for this study were taken at the four-leaf stage of sugar beet as the beginning of the critical period of weed control. A shelter was used to avoid direct sunlight and prevent leaf shadows on each other. The obtained images were then introduced to the Image Processing Toolbox of MATLAB programming software for further processing. Green and Red color components were extracted from primary RGB images. In the first step, binary images were obtained by applying the optimal threshold on the G-R images. A comprehensive study of several sugar beet images revealed that there is a unique feature in sugar beet leaves which makes them differentiable from the weeds. The feature observed in all sugar beet plants at the four

  9. Modern machine learning techniques and their applications in cartoon animation research

    CERN Document Server

    Yu, Jun

    2013-01-01

    The integration of machine learning techniques and cartoon animation research is fast becoming a hot topic. This book helps readers learn the latest machine learning techniques, including patch alignment framework; spectral clustering, graph cuts, and convex relaxation; ensemble manifold learning; multiple kernel learning; multiview subspace learning; and multiview distance metric learning. It then presents the applications of these modern machine learning techniques in cartoon animation research. With these techniques, users can efficiently utilize the cartoon materials to generate animations

  10. Estimation of Alpine Skier Posture Using Machine Learning Techniques

    Directory of Open Access Journals (Sweden)

    Bojan Nemec

    2014-10-01

    Full Text Available High precision Global Navigation Satellite System (GNSS measurements are becoming more and more popular in alpine skiing due to the relatively undemanding setup and excellent performance. However, GNSS provides only single-point measurements that are defined with the antenna placed typically behind the skier’s neck. A key issue is how to estimate other more relevant parameters of the skier’s body, like the center of mass (COM and ski trajectories. Previously, these parameters were estimated by modeling the skier’s body with an inverted-pendulum model that oversimplified the skier’s body. In this study, we propose two machine learning methods that overcome this shortcoming and estimate COM and skis trajectories based on a more faithful approximation of the skier’s body with nine degrees-of-freedom. The first method utilizes a well-established approach of artificial neural networks, while the second method is based on a state-of-the-art statistical generalization method. Both methods were evaluated using the reference measurements obtained on a typical giant slalom course and compared with the inverted-pendulum method. Our results outperform the results of commonly used inverted-pendulum methods and demonstrate the applicability of machine learning techniques in biomechanical measurements of alpine skiing.

  11. Classification of Phishing Email Using Random Forest Machine Learning Technique

    Directory of Open Access Journals (Sweden)

    Andronicus A. Akinyelu

    2014-01-01

    Full Text Available Phishing is one of the major challenges faced by the world of e-commerce today. Thanks to phishing attacks, billions of dollars have been lost by many companies and individuals. In 2012, an online report put the loss due to phishing attack at about $1.5 billion. This global impact of phishing attacks will continue to be on the increase and thus requires more efficient phishing detection techniques to curb the menace. This paper investigates and reports the use of random forest machine learning algorithm in classification of phishing attacks, with the major objective of developing an improved phishing email classifier with better prediction accuracy and fewer numbers of features. From a dataset consisting of 2000 phishing and ham emails, a set of prominent phishing email features (identified from the literature were extracted and used by the machine learning algorithm with a resulting classification accuracy of 99.7% and low false negative (FN and false positive (FP rates.

  12. Improving Alzheimer's disease diagnosis with machine learning techniques.

    Science.gov (United States)

    Trambaiolli, Lucas R; Lorena, Ana C; Fraga, Francisco J; Kanda, Paulo A M; Anghinah, Renato; Nitrini, Ricardo

    2011-07-01

    There is not a specific test to diagnose Alzheimer's disease (AD). Its diagnosis should be based upon clinical history, neuropsychological and laboratory tests, neuroimaging and electroencephalography (EEG). Therefore, new approaches are necessary to enable earlier and more accurate diagnosis and to follow treatment results. In this study we used a Machine Learning (ML) technique, named Support Vector Machine (SVM), to search patterns in EEG epochs to differentiate AD patients from controls. As a result, we developed a quantitative EEG (qEEG) processing method for automatic differentiation of patients with AD from normal individuals, as a complement to the diagnosis of probable dementia. We studied EEGs from 19 normal subjects (14 females/5 males, mean age 71.6 years) and 16 probable mild to moderate symptoms AD patients (14 females/2 males, mean age 73.4 years. The results obtained from analysis of EEG epochs were accuracy 79.9% and sensitivity 83.2%. The analysis considering the diagnosis of each individual patient reached 87.0% accuracy and 91.7% sensitivity.

  13. Machine Learning Techniques in Diagnosis of Pulmonary Embolism

    Directory of Open Access Journals (Sweden)

    Goksu Berikol

    2016-04-01

    Full Text Available Aim: Pulmonary embolism (PE, is a high mortality disease which clinical suspicion and a variety of diagnostic laboratory and imagingresults have a high importance in diagnose. Anticoagulation and fybrinolytic treatments are hard to decide in some cases therefore early diagnose is important in emergency medicine.Material and Method: The study was designed retrospectively based on the records of the 201 patients who were presenting to Emergency Department with pulmonary complaints including dyspnea and chest pain between January 2010 and October 2013. Results: Machine learning techniques were used for calculating the success in diagnosing PE. The success rate of the classification tree method for detection of PE was 95%, which was higher than that of KNN classification (75% and Naive Bayes Classification (88.5%. Discussion: Classification tree and Bayesian method can be selected ones to diagnose or define possibility of pulmonary embolism in emergency centers with limited study tests and for the patients difficultly diagnosed.

  14. Machine vision approach to auto-generation of high resolution, continental-scale geomorphometric map from DEM

    Science.gov (United States)

    Jasiewicz, J.; Stepinski, T. F.

    2012-04-01

    Geomorphometric map (GM) is a map of landforms delineated exclusively on the basis of their morphology; it depicts a classification of landscape into its constituent elements. GM is a valuable tool for visual terrain analysis, but more importantly, it's a perfect terrain representation for its further algorithmic analysis. GMs themselves are auto-generated from DEM. We have developed a new technique for auto-generation of GMs that is based on the principle of machine vision. Such approach approximates more closely the mapping process of human analyst and results in an efficient generation of GMs having quality and utility superior to maps generated by a standard technique based on differential geometry. The core of the new technique is a notion of geomorphon. A geomorphon is a relief-invariant, orientation-invariant, and size-flexible abstracted elementary unit of terrain. It is calculated from DEM using simple ternary patterns defined on a neighborhood which size adapts to the character of local terrain. Geomorphons are both terrain attributes and landform types at the same time; they allow for a direct and highly efficient, single-step classification and mapping of landforms. There are 498 unique geomorphons but only a small fraction of them are found in typical natural terrain. The geomorphon-based mapping technique is implemented as a GRASS GIS extension written in ANSI C and will be available in the public domain. In order to showcase the capabilities of geomorphons we have calculated the GM for the entire conterminous United States from the 30m/pixel NED DEM. The map shows ten most abundant landforms: flat, peak, ridge, shoulder, spur, slope, hollow, footslope, valley, and pit; a lookup table was used to assign each of the remaining 488 infrequent forms to a morphologically closest mapped form. The result is a unique, never before seen, type of map that clearly shows multiple geomorphic features and indicates the underlying geologic processes. The auto

  15. Localization System for a Mobile Robot Using Computer Vision Techniques

    Directory of Open Access Journals (Sweden)

    Rony Cruz Ramírez

    2012-05-01

    Full Text Available Mobile Robotics is a subject with multiple fields of action hence studies in this area are of vital importance. This paper describes the development of localization system for a mobile robot using Computer Vision. A webcam is placed at a height where the navigation environment can be seen. A LEGO NXT kit is used to build a wheeled mobile robot of differential drive configuration. The software is programmed in C++ using the functions library Open CV 2.0. this software then soft handles the webcam, does the processing of captured images, the calculation of the location, controls and communicates via Bluetooth. Also it implements a kinematic position control and performs several experiments to verify the reliability of the localization system. The results of one such experiment are described here.

  16. From geospatial observations of ocean currents to causal predictors of spatio-economic activity using computer vision and machine learning

    Science.gov (United States)

    Popescu, Florin; Ayache, Stephane; Escalera, Sergio; Baró Solé, Xavier; Capponi, Cecile; Panciatici, Patrick; Guyon, Isabelle

    2016-04-01

    The big data transformation currently revolutionizing science and industry forges novel possibilities in multi-modal analysis scarcely imaginable only a decade ago. One of the important economic and industrial problems that stand to benefit from the recent expansion of data availability and computational prowess is the prediction of electricity demand and renewable energy generation. Both are correlates of human activity: spatiotemporal energy consumption patterns in society are a factor of both demand (weather dependent) and supply, which determine cost - a relation expected to strengthen along with increasing renewable energy dependence. One of the main drivers of European weather patterns is the activity of the Atlantic Ocean and in particular its dominant Northern Hemisphere current: the Gulf Stream. We choose this particular current as a test case in part due to larger amount of relevant data and scientific literature available for refinement of analysis techniques. This data richness is due not only to its economic importance but also to its size being clearly visible in radar and infrared satellite imagery, which makes it easier to detect using Computer Vision (CV). The power of CV techniques makes basic analysis thus developed scalable to other smaller and less known, but still influential, currents, which are not just curves on a map, but complex, evolving, moving branching trees in 3D projected onto a 2D image. We investigate means of extracting, from several image modalities (including recently available Copernicus radar and earlier Infrared satellites), a parameterized representation of the state of the Gulf Stream and its environment that is useful as feature space representation in a machine learning context, in this case with the EC's H2020-sponsored 'See.4C' project, in the context of which data scientists may find novel predictors of spatiotemporal energy flow. Although automated extractors of Gulf Stream position exist, they differ in methodology

  17. Short-term wind speed predictions with machine learning techniques

    Science.gov (United States)

    Ghorbani, M. A.; Khatibi, R.; FazeliFard, M. H.; Naghipour, L.; Makarynskyy, O.

    2016-02-01

    Hourly wind speed forecasting is presented by a modeling study with possible applications to practical problems including farming wind energy, aircraft safety and airport operations. Modeling techniques employed in this paper for such short-term predictions are based on the machine learning techniques of artificial neural networks (ANNs) and genetic expression programming (GEP). Recorded values of wind speed were used, which comprised 8 years of collected data at the Kersey site, Colorado, USA. The January data over the first 7 years (2005-2011) were used for model training; and the January data for 2012 were used for model testing. A number of model structures were investigated for the validation of the robustness of these two techniques. The prediction results were compared with those of a multiple linear regression (MLR) method and with the Persistence method developed for the data. The model performances were evaluated using the correlation coefficient, root mean square error, Nash-Sutcliffe efficiency coefficient and Akaike information criterion. The results indicate that forecasting wind speed is feasible using past records of wind speed alone, but the maximum lead time for the data was found to be 14 h. The results show that different techniques would lead to different results, where the choice between them is not easy. Thus, decision making has to be informed of these modeling results and decisions should be arrived at on the basis of an understanding of inherent uncertainties. The results show that both GEP and ANN are equally credible selections and even MLR should not be dismissed, as it has its uses.

  18. Tomato grading system using machine vision technology and neuro-fuzzy networks (ANFIS

    Directory of Open Access Journals (Sweden)

    H Izadi

    2016-04-01

    Full Text Available Introduction: The quality of agricultural products is associated with their color, size and health, grading of fruits is regarded as an important step in post-harvest processing. In most cases, manual sorting inspections depends on available manpower, time consuming and their accuracy could not be guaranteed. Machine Vision is known to be a useful tool for external features measurement (e.g. size, shape, color and defects and in recent century, Machine Vision technology has been used for shape sorting. The main purpose of this study was to develop new method for tomato grading and sorting using Neuro-fuzzy system (ANFIS and to compare the accuracies of the ANFIS predicted results with those suggested by a human expert. Materials and Methods: In this study, a total of 300 image of tomatoes (Rev ground was randomly harvested, classified in 3 ripeness stage, 3 sizes and 2 health. The grading and sorting mechanism consisted of a lighting chamber (cloudy sky, lighting source and a digital camera connected to a computer. The images were recorded in a special chamber with an indirect radiation (cloudy sky with four florescent lampson each sides and camera lens was entire to lighting chamber by a hole which was only entranced to outer and covered by a camera lens. Three types of features were extracted from final images; Shap, color and texture. To receive these features, we need to have images both in color and binary format in procedure shown in Figure 1. For the first group; characteristics of the images were analysis that could offer information an surface area (S.A., maximum diameter (Dmax, minimum diameter (Dmin and average diameters. Considering to the importance of the color in acceptance of food quality by consumers, the following classification was conducted to estimate the apparent color of the tomato; 1. Classified as red (red > 90% 2. Classified as red light (red or bold pink 60-90% 3. Classified as pink (red 30-60% 4. Classified as Turning

  19. 机器视觉的构造及应用%A Summary of the Construction and Application of Machine Vision

    Institute of Scientific and Technical Information of China (English)

    徐仲勋; 黄科程

    2015-01-01

    文章介绍了机器视觉在处理图像时的几种方法以及目标跟踪的原理,分析了机器视觉在农业、工业、医学等领域的实际应用情况,探讨了现阶段机器视觉在各个领域存在的一些问题和解决的方法,最后对机器视觉技术的应用和发展前景做了展望。%This paper introduces the concept of machine vision, structure, expounds the developing situation, such as light source, optical lens, camera, etc. This paper introduces the application of machine vision in the several methods of processing the image. Machine vision were analyzed in the agricultural, industrial, medical and other fields of application. Discusses the present some problems of machine vision in various fields and the solution method. Finally, the application of machine vision technology and development prospects were discussed.

  20. A dangerous cocktail: databases, information techniques and lack of visions

    DEFF Research Database (Denmark)

    Tarp, Sven

    2017-01-01

    fully to the new technologies and get rid of old habits and ways of thinking. The article provides some examples of how the current challenges can be approached in terms of databases, user interfaces and other tools and techniques to assist the compilation and presentation of online dictionaries......This contribution discusses challenges to lexicography created by the new computer, information and communication technologies and techniques. It argues that the current transition period is full of paradoxes and that the main problem seems to be the subjective factor, i.e. the ability to adapt...

  1. Application of Krylov Reduction Technique for a Machine Tool Multibody Modelling

    Directory of Open Access Journals (Sweden)

    M. Sulitka

    2014-02-01

    Full Text Available Quick calculation of machine tool dynamic response represents one of the major requirements for machine tool virtual modelling and virtual machining, aiming at simulating the machining process performance, quality, and precision of a workpiece. Enhanced time effectiveness in machine tool dynamic simulations may be achieved by employing model order reduction (MOR techniques of the full finite element (FE models. The paper provides a case study aimed at comparison of Krylov subspace base and mode truncation technique. Application of both of the reduction techniques for creating a machine tool multibody model is evaluated. The Krylov subspace reduction technique shows high quality in terms of both dynamic properties of the reduced multibody model and very low time demands at the same time.

  2. Using machine learning techniques to differentiate acute coronary syndrome

    Directory of Open Access Journals (Sweden)

    Sougand Setareh

    2015-02-01

    Full Text Available Backgroud: Acute coronary syndrome (ACS is an unstable and dynamic process that includes unstable angina, ST elevation myocardial infarction, and non-ST elevation myocardial infarction. Despite recent technological advances in early diognosis of ACS, differentiating between different types of coronary diseases in the early hours of admission is controversial. The present study was aimed to accurately differentiate between various coronary events, using machine learning techniques. Such methods, as a subset of artificial intelligence, include algorithms that allow computers to learn and play a major role in treatment decisions. Methods: 1902 patients diagnosed with ACS and admitted to hospital were selected according to Euro Heart Survey on ACS. Patients were classified based on decision tree J48. Bagging aggregation algorithms was implemented to increase the efficiency of algorithm. Results: The performance of classifiers was estimated and compared based on their accuracy computed from confusion matrix. The accuracy rates of decision tree and bagging algorithm were calculated to be 91.74% and 92.53%, respectively. Conclusion: The proposed methods used in this study proved to have the ability to identify various ACS. In addition, using matrix of confusion, an acceptable number of subjects with acute coronary syndrome were identified in each class.

  3. Machine learning techniques for energy optimization in mobile embedded systems

    Science.gov (United States)

    Donohoo, Brad Kyoshi

    Mobile smartphones and other portable battery operated embedded systems (PDAs, tablets) are pervasive computing devices that have emerged in recent years as essential instruments for communication, business, and social interactions. While performance, capabilities, and design are all important considerations when purchasing a mobile device, a long battery lifetime is one of the most desirable attributes. Battery technology and capacity has improved over the years, but it still cannot keep pace with the power consumption demands of today's mobile devices. This key limiter has led to a strong research emphasis on extending battery lifetime by minimizing energy consumption, primarily using software optimizations. This thesis presents two strategies that attempt to optimize mobile device energy consumption with negligible impact on user perception and quality of service (QoS). The first strategy proposes an application and user interaction aware middleware framework that takes advantage of user idle time between interaction events of the foreground application to optimize CPU and screen backlight energy consumption. The framework dynamically classifies mobile device applications based on their received interaction patterns, then invokes a number of different power management algorithms to adjust processor frequency and screen backlight levels accordingly. The second strategy proposes the usage of machine learning techniques to learn a user's mobile device usage pattern pertaining to spatiotemporal and device contexts, and then predict energy-optimal data and location interface configurations. By learning where and when a mobile device user uses certain power-hungry interfaces (3G, WiFi, and GPS), the techniques, which include variants of linear discriminant analysis, linear logistic regression, non-linear logistic regression, and k-nearest neighbor, are able to dynamically turn off unnecessary interfaces at runtime in order to save energy.

  4. Development of techniques to enhance man/machine communication

    Science.gov (United States)

    Targ, R.; Cole, P.; Puthoff, H.

    1974-01-01

    A four-state random stimulus generator, considered to function as an ESP teaching machine was used to investigate an approach to facilitating interactions between man and machines. A subject tries to guess in which of four states the machine is. The machine offers the user feedback and reinforcement as to the correctness of his choice. Using this machine, 148 volunteer subjects were screened under various protocols. Several whose learning slope and/or mean score departed significantly from chance expectation were identified. Direct physiological evidence of perception of remote stimuli not presented to any known sense of the percipient using electroencephalographic (EEG) output when a light was flashed in a distant room was also studied.

  5. Investigation into the use of smartphone as a machine vision device for engineering metrology and flaw detection, with focus on drilling

    Science.gov (United States)

    Razdan, Vikram; Bateman, Richard

    2015-05-01

    This study investigates the use of a Smartphone and its camera vision capabilities in Engineering metrology and flaw detection, with a view to develop a low cost alternative to Machine vision systems which are out of range for small scale manufacturers. A Smartphone has to provide a similar level of accuracy as Machine Vision devices like Smart cameras. The objective set out was to develop an App on an Android Smartphone, incorporating advanced Computer vision algorithms written in java code. The App could then be used for recording measurements of Twist Drill bits and hole geometry, and analysing the results for accuracy. A detailed literature review was carried out for in-depth study of Machine vision systems and their capabilities, including a comparison between the HTC One X Android Smartphone and the Teledyne Dalsa BOA Smart camera. A review of the existing metrology Apps in the market was also undertaken. In addition, the drilling operation was evaluated to establish key measurement parameters of a twist Drill bit, especially flank wear and diameter. The methodology covers software development of the Android App, including the use of image processing algorithms like Gaussian Blur, Sobel and Canny available from OpenCV software library, as well as designing and developing the experimental set-up for carrying out the measurements. The results obtained from the experimental set-up were analysed for geometry of Twist Drill bits and holes, including diametrical measurements and flaw detection. The results show that Smartphones like the HTC One X have the processing power and the camera capability to carry out metrological tasks, although dimensional accuracy achievable from the Smartphone App is below the level provided by Machine vision devices like Smart cameras. A Smartphone with mechanical attachments, capable of image processing and having a reasonable level of accuracy in dimensional measurement, has the potential to become a handy low-cost Machine vision

  6. Wire electric-discharge machining and other fabrication techniques

    Science.gov (United States)

    Morgan, W. H.

    1983-01-01

    Wire electric discharge machining and extrude honing were used to fabricate a two dimensional wing for cryogenic wind tunnel testing. Electric-discharge cutting is done with a moving wire electrode. The cut track is controlled by means of a punched-tape program and the cutting feed is regulated according to the progress of the work. Electric-discharge machining involves no contact with the work piece, and no mechanical force is exerted. Extrude hone is a process for honing finish-machined surfaces by the extrusion of an abrasive material (silly putty), which is forced through a restrictive fixture. The fabrication steps are described and production times are given.

  7. Measuring the modulation-transfer function of radiation-tolerant machine-vision system using the sum of harmonic components of different frequency

    Science.gov (United States)

    Perezyabov, Oleg A.; Maltseva, Nadezhda K.; Ilinski, Aleksandr V.

    2017-05-01

    There are a number of robotic systems that are used for nuclear power plant maintenance and it is important to ensure the necessary safety level. The machine-vision systems are applied for this purpose. There are special requirements for the image quality of these systems. To estimate the resolution of a video-system one should determine the impact of the system on the special test pattern. In this paper we describe the procedure of determining the number of the modulation transfer function values of the radiation-tolerant machine-vision systems using the test pattern, containing the sum of the harmonic functions of different frequency.

  8. SPAM CLASSIFICATION BASED ON SUPERVISED LEARNING USING MACHINE LEARNING TECHNIQUES

    Directory of Open Access Journals (Sweden)

    T. Hamsapriya

    2011-12-01

    Full Text Available E-mail is one of the most popular and frequently used ways of communication due to its worldwide accessibility, relatively fast message transfer, and low sending cost. The flaws in the e-mail protocols and the increasing amount of electronic business and financial transactions directly contribute to the increase in e-mail-based threats. Email spam is one of the major problems of the today’s Internet, bringing financial damage to companies and annoying individual users. Spam emails are invading users without their consent and filling their mail boxes. They consume more network capacity as well as time in checking and deleting spam mails. The vast majority of Internet users are outspoken in their disdain for spam, although enough of them respond to commercial offers that spam remains a viable source of income to spammers. While most of the users want to do right think to avoid and get rid of spam, they need clear and simple guidelines on how to behave. In spite of all the measures taken to eliminate spam, they are not yet eradicated. Also when the counter measures are over sensitive, even legitimate emails will be eliminated. Among the approaches developed to stop spam, filtering is the one of the most important technique. Many researches in spam filtering have been centered on the more sophisticated classifier-related issues. In recent days, Machine learning for spam classification is an important research issue. The effectiveness of the proposed work is explores and identifies the use of different learning algorithms for classifying spam messages from e-mail. A comparative analysis among the algorithms has also been presented.

  9. A Low Cost Vision Based Hybrid Fiducial Mark Tracking Technique for Mobile Industrial Robots

    OpenAIRE

    Mohammed Y Aalsalem; Wazir Zada Khan; Quratul Ain Arshad

    2012-01-01

    The field of robotic vision is developing rapidly. Robots can react intelligently and provide assistance to user activities through sentient computing. Since industrial applications pose complex requirements that cannot be handled by humans, an efficient low cost and robust technique is required for the tracking of mobile industrial robots. The existing sensor based techniques for mobile robot tracking are expensive and complex to deploy, configure and maintain. Also some of them demand dedic...

  10. Machine Learning

    CERN Document Server

    CERN. Geneva

    2017-01-01

    Machine learning, which builds on ideas in computer science, statistics, and optimization, focuses on developing algorithms to identify patterns and regularities in data, and using these learned patterns to make predictions on new observations. Boosted by its industrial and commercial applications, the field of machine learning is quickly evolving and expanding. Recent advances have seen great success in the realms of computer vision, natural language processing, and broadly in data science. Many of these techniques have already been applied in particle physics, for instance for particle identification, detector monitoring, and the optimization of computer resources. Modern machine learning approaches, such as deep learning, are only just beginning to be applied to the analysis of High Energy Physics data to approach more and more complex problems. These classes will review the framework behind machine learning and discuss recent developments in the field.

  11. Machine learning techniques applied to system characterization and equalization

    DEFF Research Database (Denmark)

    Zibar, Darko; Thrane, Jakob; Wass, Jesper

    2016-01-01

    Linear signal processing algorithms are effective in combating linear fibre channel impairments. We demonstrate the ability of machine learning algorithms to combat nonlinear fibre channel impairments and perform parameter extraction from directly detected signals.......Linear signal processing algorithms are effective in combating linear fibre channel impairments. We demonstrate the ability of machine learning algorithms to combat nonlinear fibre channel impairments and perform parameter extraction from directly detected signals....

  12. Survey of Mechatronic Techniques in Modern Machine Design

    Directory of Open Access Journals (Sweden)

    Devdas Shetty

    2012-01-01

    Full Text Available Increasing demands on the productivity of complex systems, such as manufacturing machines and their steadily growing technological importance will require the application of new methods in the product development process. A smart machine can make decisions about the process in real-time with plenty of adaptive controls. This paper shows the simulation based mechatronic model of a complex system with a better understanding of the dynamic behavior and interactions of the components. This offers improved possibilities of evaluating and optimizing the dynamic motion performance of the entire automated system in the early stages of the design process. Another effect is the growing influence of interactions between machine components on achievable machine dynamics and precision and quality of components. The examples cited in this paper, demonstrate the distinguishing feature of mechatronic systems through intensive integration. The case studies also show that it will no longer be sufficient to focus solely on the optimization of subsystems. Instead it will be necessary to strive for optimization of the complete system. The interactions between machine components, the influence of the control system and the machining process will have to be considered during the design process and the coordination of feed drives and frame structure components.

  13. MUMAL: Multivariate analysis in shotgun proteomics using machine learning techniques

    Directory of Open Access Journals (Sweden)

    Cerqueira Fabio R

    2012-10-01

    Full Text Available Abstract Background The shotgun strategy (liquid chromatography coupled with tandem mass spectrometry is widely applied for identification of proteins in complex mixtures. This method gives rise to thousands of spectra in a single run, which are interpreted by computational tools. Such tools normally use a protein database from which peptide sequences are extracted for matching with experimentally derived mass spectral data. After the database search, the correctness of obtained peptide-spectrum matches (PSMs needs to be evaluated also by algorithms, as a manual curation of these huge datasets would be impractical. The target-decoy database strategy is largely used to perform spectrum evaluation. Nonetheless, this method has been applied without considering sensitivity, i.e., only error estimation is taken into account. A recently proposed method termed MUDE treats the target-decoy analysis as an optimization problem, where sensitivity is maximized. This method demonstrates a significant increase in the retrieved number of PSMs for a fixed error rate. However, the MUDE model is constructed in such a way that linear decision boundaries are established to separate correct from incorrect PSMs. Besides, the described heuristic for solving the optimization problem has to be executed many times to achieve a significant augmentation in sensitivity. Results Here, we propose a new method, termed MUMAL, for PSM assessment that is based on machine learning techniques. Our method can establish nonlinear decision boundaries, leading to a higher chance to retrieve more true positives. Furthermore, we need few iterations to achieve high sensitivities, strikingly shortening the running time of the whole process. Experiments show that our method achieves a considerably higher number of PSMs compared with standard tools such as MUDE, PeptideProphet, and typical target-decoy approaches. Conclusion Our approach not only enhances the computational performance, and

  14. Comments on the Co-Emergence of Machine Techniques, Paper-and-Pencil Techniques, and Theoretical Reflection

    Science.gov (United States)

    Monaghan, John; Ozmantar, Mehmet Fatih

    2006-01-01

    We comment on the paper "The co-emergence of machine techniques, paper-and-pencil techniques, and theoretical reflection: A study of CAS use in secondary school algebra" by Carolyn Kieran and Paul Drijvers. We look at aspects of Kieran and Drijvers' analysis with regard to "task-technique-theory" in the light of a model of abstraction in context…

  15. Comments on the Co-Emergence of Machine Techniques, Paper-and-Pencil Techniques, and Theoretical Reflection

    Science.gov (United States)

    Monaghan, John; Ozmantar, Mehmet Fatih

    2006-01-01

    We comment on the paper "The co-emergence of machine techniques, paper-and-pencil techniques, and theoretical reflection: A study of CAS use in secondary school algebra" by Carolyn Kieran and Paul Drijvers. We look at aspects of Kieran and Drijvers' analysis with regard to "task-technique-theory" in the light of a model of abstraction in context…

  16. Machine learning in Python essential techniques for predictive analysis

    CERN Document Server

    Bowles, Michael

    2015-01-01

    Learn a simpler and more effective way to analyze data and predict outcomes with Python Machine Learning in Python shows you how to successfully analyze data using only two core machine learning algorithms, and how to apply them using Python. By focusing on two algorithm families that effectively predict outcomes, this book is able to provide full descriptions of the mechanisms at work, and the examples that illustrate the machinery with specific, hackable code. The algorithms are explained in simple terms with no complex math and applied using Python, with guidance on algorithm selection, d

  17. Simulation of the «COSMONAUT-ROBOT» System Interaction on the Lunar Surface Based on Methods of Machine Vision and Computer Graphics

    Science.gov (United States)

    Kryuchkov, B. I.; Usov, V. M.; Chertopolokhov, V. A.; Ronzhin, A. L.; Karpov, A. A.

    2017-05-01

    Extravehicular activity (EVA) on the lunar surface, necessary for the future exploration of the Moon, involves extensive use of robots. One of the factors of safe EVA is a proper interaction between cosmonauts and robots in extreme environments. This requires a simple and natural man-machine interface, e.g. multimodal contactless interface based on recognition of gestures and cosmonaut's poses. When travelling in the "Follow Me" mode (master/slave), a robot uses onboard tools for tracking cosmonaut's position and movements, and on the basis of these data builds its itinerary. The interaction in the system "cosmonaut-robot" on the lunar surface is significantly different from that on the Earth surface. For example, a man, dressed in a space suit, has limited fine motor skills. In addition, EVA is quite tiring for the cosmonauts, and a tired human being less accurately performs movements and often makes mistakes. All this leads to new requirements for the convenient use of the man-machine interface designed for EVA. To improve the reliability and stability of human-robot communication it is necessary to provide options for duplicating commands at the task stages and gesture recognition. New tools and techniques for space missions must be examined at the first stage of works in laboratory conditions, and then in field tests (proof tests at the site of application). The article analyzes the methods of detection and tracking of movements and gesture recognition of the cosmonaut during EVA, which can be used for the design of human-machine interface. A scenario for testing these methods by constructing a virtual environment simulating EVA on the lunar surface is proposed. Simulation involves environment visualization and modeling of the use of the "vision" of the robot to track a moving cosmonaut dressed in a spacesuit.

  18. Phishtest: Measuring the Impact of Email Headers on the Predictive Accuracy of Machine Learning Techniques

    Science.gov (United States)

    Tout, Hicham

    2013-01-01

    The majority of documented phishing attacks have been carried by email, yet few studies have measured the impact of email headers on the predictive accuracy of machine learning techniques in detecting email phishing attacks. Research has shown that the inclusion of a limited subset of email headers as features in training machine learning…

  19. Phishtest: Measuring the Impact of Email Headers on the Predictive Accuracy of Machine Learning Techniques

    Science.gov (United States)

    Tout, Hicham

    2013-01-01

    The majority of documented phishing attacks have been carried by email, yet few studies have measured the impact of email headers on the predictive accuracy of machine learning techniques in detecting email phishing attacks. Research has shown that the inclusion of a limited subset of email headers as features in training machine learning…

  20. The use of computer vision techniques to augment home based sensorised environments.

    Science.gov (United States)

    Uhríková, Zdenka; Nugent, Chris D; Hlavác, Václav

    2008-01-01

    Technology within the home environment is becoming widely accepted as a means to facilitate independent living. Nevertheless, practical issues of detecting different tasks between multiple persons within the same environment along with managing instances of uncertainty associated with recorded sensor data are two key challenges yet to be fully solved. This work presents details of how computer vision techniques can be used as both alternative and complementary means in the assessment of behaviour in home based sensorised environments. Within our work we assessed the ability of vision processing techniques in conjunction with sensor based data to deal with instances of multiple occupancy. Our Results indicate that the inclusion of the video data improved the overall process of task identification by detecting and recognizing multiple people in the environment using color based tracking algorithm.

  1. Machine vision process monitoring on a poultry processing kill line: results from an implementation

    Science.gov (United States)

    Usher, Colin; Britton, Dougl; Daley, Wayne; Stewart, John

    2005-11-01

    Researchers at the Georgia Tech Research Institute designed a vision inspection system for poultry kill line sorting with the potential for process control at various points throughout a processing facility. This system has been successfully operating in a plant for over two and a half years and has been shown to provide multiple benefits. With the introduction of HACCP-Based Inspection Models (HIMP), the opportunity for automated inspection systems to emerge as viable alternatives to human screening is promising. As more plants move to HIMP, these systems have the great potential for augmenting a processing facilities visual inspection process. This will help to maintain a more consistent and potentially higher throughput while helping the plant remain within the HIMP performance standards. In recent years, several vision systems have been designed to analyze the exterior of a chicken and are capable of identifying Food Safety 1 (FS1) type defects under HIMP regulatory specifications. This means that a reliable vision system can be used in a processing facility as a carcass sorter to automatically detect and divert product that is not suitable for further processing. This improves the evisceration line efficiency by creating a smaller set of features that human screeners are required to identify. This can reduce the required number of screeners or allow for faster processing line speeds. In addition to identifying FS1 category defects, the Georgia Tech vision system can also identify multiple "Other Consumer Protection" (OCP) category defects such as skin tears, bruises, broken wings, and cadavers. Monitoring this data in an almost real-time system allows the processing facility to address anomalies as soon as they occur. The Georgia Tech vision system can record minute-by-minute averages of the following defects: Septicemia Toxemia, cadaver, over-scald, bruises, skin tears, and broken wings. In addition to these defects, the system also records the length and

  2. A Low Cost Vision Based Hybrid Fiducial Mark Tracking Technique for Mobile Industrial Robots

    Directory of Open Access Journals (Sweden)

    Mohammed Y Aalsalem

    2012-07-01

    Full Text Available The field of robotic vision is developing rapidly. Robots can react intelligently and provide assistance to user activities through sentient computing. Since industrial applications pose complex requirements that cannot be handled by humans, an efficient low cost and robust technique is required for the tracking of mobile industrial robots. The existing sensor based techniques for mobile robot tracking are expensive and complex to deploy, configure and maintain. Also some of them demand dedicated and often expensive hardware. This paper presents a low cost vision based technique called “Hybrid Fiducial Mark Tracking” (HFMT technique for tracking mobile industrial robot. HFMT technique requires off-the-shelf hardware (CCD cameras and printable 2-D circular marks used as fiducials for tracking a mobile industrial robot on a pre-defined path. This proposed technique allows the robot to track on a predefined path by using fiducials for the detection of Right and Left turns on the path and White Strip for tracking the path. The HFMT technique is implemented and tested on an indoor mobile robot at our laboratory. Experimental results from robot navigating in real environments have confirmed that our approach is simple and robust and can be adopted in any hostile industrial environment where humans are unable to work.

  3. A comparison of machine learning techniques for predicting downstream acid mine drainage

    CSIR Research Space (South Africa)

    van Zyl, TL

    2014-07-01

    Full Text Available Canadian Symposium on Remote Sensing (IGARSS) 2014, Quebec, Canada, 13-18 July 2014 A comparison of machine learning techniques for predicting downstream acid mine drainage Terence L van Zyl EOSIT, Meraka Institute, CSIR, Pretoria, South Africa...

  4. Non-traditional Machining Techniques for Fabricating Metal Aerospace Filters

    Institute of Scientific and Technical Information of China (English)

    Wang Wei; Zhu Di; D.M.Allen; H.J.A.Almondb

    2008-01-01

    Thanks to recent advances in manufacturing technology, aerospace system designers have many more options to fabricate high-quality, low-weight, high-capacity, cost-effective filters. Aside from traditional methods such as stamping, drilling and milling,many new approaches have been widely used in filter-manufacturing practices on account of their increased processing abilities. However, the restrictions on costs, the need for studying under stricter conditions such as in aggressive fluids, the complicity in design, the workability of materials, and others have made it difficult to choose a satisfactory method from the newly developed processes, such as,photochemical machining (PCM), photo electroforming (PEF) and laser beam machining (LBM) to produce small, inexpensive, lightweight aerospace filters. This article appraises the technical and economical viability of PCM, PEF, and LBM to help engineers choose the fittest approach to turn out aerospace filters.

  5. Relevance vector machine technique for the inverse scattering problem

    Institute of Scientific and Technical Information of China (English)

    Wang Fang-Fang; Zhang Ye-Rong

    2012-01-01

    A novel method based on the relevance vector machine(RVM)for the inverse scattering problem is presented in this paper.The nonlinearity and the ill-posedness inherent in this problem are simultaneously considered.The nonlinearity is embodied in the relation between the scattered field and the target property,which can be obtained through the RVM training process.Besides,rather than utilizing regularization,the ill-posed nature of the inversion is naturally accounted for because the RVM can produce a probabilistic output.Simulation results reveal that the proposed RVM-based approach can provide comparative performances in terms of accuracy,convergence,robustness,generalization,and improved performance in terms of sparse property in comparison with the support vector machine(SVM)based approach.

  6. A Planar-Dimensions Machine Vision Measurement Method Based on Lens Distortion Correction

    Directory of Open Access Journals (Sweden)

    Qiucheng Sun

    2013-01-01

    Full Text Available Lens distortion practically presents in a real optical imaging system causing nonuniform geometric distortion in the images and gives rise to additional errors in the vision measurement. In this paper, a planar-dimensions vision measurement method is proposed by improving camera calibration, in which the lens distortion is corrected on the pixel plane of image. The method can be divided into three steps: firstly, the feature points, only in the small central region of the image, are used to get a more accurate perspective projection model; secondly, rather than defining a uniform model, the smoothing spline function is used to describe the lens distortion in the measurement region of image, and two correction functions can be obtained by fitting two deviation surfaces; finally, a measurement method for planar dimensions is proposed, in which accurate magnification factor of imaging system can be obtained by using the correction functions. The effectiveness of the method is demonstrated by applying the proposed method to the test of measuring shaft diameter. Experimental data prove that the accurate planar-dimensions measurements can be performed using the proposed method even if images are deformed by lens distortion.

  7. Forecasting daily and monthly exchange rates with machine learning techniques

    OpenAIRE

    Papadimitriou, Theophilos; Gogas, Periklis; Plakandaras, Vasilios

    2013-01-01

    We combine signal processing to machine learning methodologies by introducing a hybrid Ensemble Empirical Mode Decomposition (EEMD), Multivariate Adaptive Regression Splines (MARS) and Support Vector Regression (SVR) model in order to forecast the monthly and daily Euro (EUR)/United States Dollar (USD), USD/Japanese Yen (JPY), Australian Dollar (AUD)/Norwegian Krone (NOK), New Zealand Dollar (NZD)/Brazilian Real (BRL) and South African Rand (ZAR)/Philippine Peso (PHP) exchange rates. After th...

  8. MODELING AND COMPENSATION TECHNIQUE FOR THE GEOMETRIC ERRORS OF FIVE-AXIS CNC MACHINE TOOLS

    Institute of Scientific and Technical Information of China (English)

    2003-01-01

    One of the important trends in precision machining is the development of real-time error compensation technique.The error compensation for multi-axis CNC machine tools is very difficult and attractive.The modeling for the geometric error of five-axis CNC machine tools based on multi-body systems is proposed.And the key technique of the compensation-identifying geometric error parameters-is developed.The simulation of cutting workpiece to verify the modeling based on the multi-body systems is also considered.

  9. Theory research of seam recognition and welding torch pose control based on machine vision

    Science.gov (United States)

    Long, Qiang; Zhai, Peng; Liu, Miao; He, Kai; Wang, Chunyang

    2017-03-01

    At present, the automation requirement of the welding become higher, so a method of the welding information extraction by vision sensor is proposed in this paper, and the simulation with the MATLAB has been conducted. Besides, in order to improve the quality of robot automatic welding, an information retrieval method for welding torch pose control by visual sensor is attempted. Considering the demands of welding technology and engineering habits, the relative coordinate systems and variables are strictly defined, and established the mathematical model of the welding pose, and verified its feasibility by using the MATLAB simulation in the paper, these works lay a foundation for the development of welding off-line programming system with high precision and quality.

  10. Design Considerations for Scalable High-Performance Vision Systems Embedded in Industrial Print Inspection Machines

    Directory of Open Access Journals (Sweden)

    Rössler Peter

    2007-01-01

    Full Text Available This paper describes the design of a scalable high-performance vision system which is used in the application area of optical print inspection. The system is able to process hundreds of megabytes of image data per second coming from several high-speed/high-resolution cameras. Due to performance requirements, some functionality has been implemented on dedicated hardware based on a field programmable gate array (FPGA, which is coupled to a high-end digital signal processor (DSP. The paper discusses design considerations like partitioning of image processing algorithms between hardware and software. The main chapters focus on functionality implemented on the FPGA, including low-level image processing algorithms (flat-field correction, image pyramid generation, neighborhood operations and advanced processing units (programmable arithmetic unit, geometry unit. Verification issues for the complex system are also addressed. The paper concludes with a summary of the FPGA resource usage and some performance results.

  11. Kernel-based machine learning techniques for infrasound signal classification

    Science.gov (United States)

    Tuma, Matthias; Igel, Christian; Mialle, Pierrick

    2014-05-01

    Infrasound monitoring is one of four remote sensing technologies continuously employed by the CTBTO Preparatory Commission. The CTBTO's infrasound network is designed to monitor the Earth for potential evidence of atmospheric or shallow underground nuclear explosions. Upon completion, it will comprise 60 infrasound array stations distributed around the globe, of which 47 were certified in January 2014. Three stages can be identified in CTBTO infrasound data processing: automated processing at the level of single array stations, automated processing at the level of the overall global network, and interactive review by human analysts. At station level, the cross correlation-based PMCC algorithm is used for initial detection of coherent wavefronts. It produces estimates for trace velocity and azimuth of incoming wavefronts, as well as other descriptive features characterizing a signal. Detected arrivals are then categorized into potentially treaty-relevant versus noise-type signals by a rule-based expert system. This corresponds to a binary classification task at the level of station processing. In addition, incoming signals may be grouped according to their travel path in the atmosphere. The present work investigates automatic classification of infrasound arrivals by kernel-based pattern recognition methods. It aims to explore the potential of state-of-the-art machine learning methods vis-a-vis the current rule-based and task-tailored expert system. To this purpose, we first address the compilation of a representative, labeled reference benchmark dataset as a prerequisite for both classifier training and evaluation. Data representation is based on features extracted by the CTBTO's PMCC algorithm. As classifiers, we employ support vector machines (SVMs) in a supervised learning setting. Different SVM kernel functions are used and adapted through different hyperparameter optimization routines. The resulting performance is compared to several baseline classifiers. All

  12. Analysis Of Machine Learning Techniques By Using Blogger Data

    Directory of Open Access Journals (Sweden)

    Gowsalya.R,

    2014-04-01

    Full Text Available Blogs are the recent fast progressing media which depends on information system and technological advancement. The mass media is not much developed for the developing countries are in government terms and their schemes are developed based on governmental concepts, so blogs are provided for knowledge and ideas sharing. This article has highlighted and performed simulations from obtained information, 100 instances of Bloggers by using Weka 3. 6 Tool, and by applying many machine learning algorithms and analyzed with the values of accuracy, precision, recall and F-measure for getting future tendency anticipation of users to blogging and using in strategical areas. Keywords -

  13. The impact of machine learning techniques in the study of bipolar disorder: A systematic review.

    Science.gov (United States)

    Librenza-Garcia, Diego; Kotzian, Bruno Jaskulski; Yang, Jessica; Mwangi, Benson; Cao, Bo; Pereira Lima, Luiza Nunes; Bermudez, Mariane Bagatin; Boeira, Manuela Vianna; Kapczinski, Flávio; Passos, Ives Cavalcante

    2017-07-18

    Machine learning techniques provide new methods to predict diagnosis and clinical outcomes at an individual level. We aim to review the existing literature on the use of machine learning techniques in the assessment of subjects with bipolar disorder. We systematically searched PubMed, Embase and Web of Science for articles published in any language up to January 2017. We found 757 abstracts and included 51 studies in our review. Most of the included studies used multiple levels of biological data to distinguish the diagnosis of bipolar disorder from other psychiatric disorders or healthy controls. We also found studies that assessed the prediction of clinical outcomes and studies using unsupervised machine learning to build more consistent clinical phenotypes of bipolar disorder. We concluded that given the clinical heterogeneity of samples of patients with BD, machine learning techniques may provide clinicians and researchers with important insights in fields such as diagnosis, personalized treatment and prognosis orientation. Copyright © 2017 Elsevier Ltd. All rights reserved.

  14. Vision-based system identification technique for building structures using a motion capture system

    Science.gov (United States)

    Oh, Byung Kwan; Hwang, Jin Woo; Kim, Yousok; Cho, Tongjun; Park, Hyo Seon

    2015-11-01

    This paper presents a new vision-based system identification (SI) technique for building structures by using a motion capture system (MCS). The MCS with outstanding capabilities for dynamic response measurements can provide gage-free measurements of vibrations through the convenient installation of multiple markers. In this technique, from the dynamic displacement responses measured by MCS, the dynamic characteristics (natural frequency, mode shape, and damping ratio) of building structures are extracted after the processes of converting the displacement from MCS to acceleration and conducting SI by frequency domain decomposition. A free vibration experiment on a three-story shear frame was conducted to validate the proposed technique. The SI results from the conventional accelerometer-based method were compared with those from the proposed technique and showed good agreement, which confirms the validity and applicability of the proposed vision-based SI technique for building structures. Furthermore, SI directly employing MCS measured displacements to FDD was performed and showed identical results to those of conventional SI method.

  15. Ecological Footprint Model Using the Support Vector Machine Technique

    Science.gov (United States)

    Ma, Haibo; Chang, Wenjuan; Cui, Guangbai

    2012-01-01

    The per capita ecological footprint (EF) is one of the most widely recognized measures of environmental sustainability. It aims to quantify the Earth's biological resources required to support human activity. In this paper, we summarize relevant previous literature, and present five factors that influence per capita EF. These factors are: National gross domestic product (GDP), urbanization (independent of economic development), distribution of income (measured by the Gini coefficient), export dependence (measured by the percentage of exports to total GDP), and service intensity (measured by the percentage of service to total GDP). A new ecological footprint model based on a support vector machine (SVM), which is a machine-learning method based on the structural risk minimization principle from statistical learning theory was conducted to calculate the per capita EF of 24 nations using data from 123 nations. The calculation accuracy was measured by average absolute error and average relative error. They were 0.004883 and 0.351078% respectively. Our results demonstrate that the EF model based on SVM has good calculation performance. PMID:22291949

  16. Machine Learning Techniques for Prediction of Early Childhood Obesity.

    Science.gov (United States)

    Dugan, T M; Mukhopadhyay, S; Carroll, A; Downs, S

    2015-01-01

    This paper aims to predict childhood obesity after age two, using only data collected prior to the second birthday by a clinical decision support system called CHICA. Analyses of six different machine learning methods: RandomTree, RandomForest, J48, ID3, Naïve Bayes, and Bayes trained on CHICA data show that an accurate, sensitive model can be created. Of the methods analyzed, the ID3 model trained on the CHICA dataset proved the best overall performance with accuracy of 85% and sensitivity of 89%. Additionally, the ID3 model had a positive predictive value of 84% and a negative predictive value of 88%. The structure of the tree also gives insight into the strongest predictors of future obesity in children. Many of the strongest predictors seen in the ID3 modeling of the CHICA dataset have been independently validated in the literature as correlated with obesity, thereby supporting the validity of the model. This study demonstrated that data from a production clinical decision support system can be used to build an accurate machine learning model to predict obesity in children after age two.

  17. 机器视觉在除草机器人中的应用%Application of Machine Vision in Weeding Robot

    Institute of Scientific and Technical Information of China (English)

    李谦; 蔡晓华

    2014-01-01

    随着农业自动化技术和农业机器人技术的发展,许多国家和企业开始致力于机器视觉除草机器人的试验与研究。为此,在介绍机器视觉技术系统的基础上,结合除草机器人苗草识别的试验,讨论了机器视觉系统在除草机器人中的应用,详细分析了基于机器视觉的苗草识别系统,并优化其系统的硬件、软件结构、功能以及原理等。%With the development of agricultural automation technology and robotics ,many countries and enterprises begin to dedicated to the experimental research of machine vision weeding robot .On the basis of machine vision technology sys-tem introduced in this paper ,combine with the Identification of the Blade of grass test ,th application of machine vision system in the weeding robot was discussed .and analyzed the recognition system of weeding grass based on machine vi-sion.And optimize the structure of hardware and software of the system .Function and principle , etc.

  18. Machine learning techniques for gait biometric recognition using the ground reaction force

    CERN Document Server

    Mason, James Eric; Woungang, Isaac

    2016-01-01

    This book focuses on how machine learning techniques can be used to analyze and make use of one particular category of behavioral biometrics known as the gait biometric. A comprehensive Ground Reaction Force (GRF)-based Gait Biometrics Recognition framework is proposed and validated by experiments. In addition, an in-depth analysis of existing recognition techniques that are best suited for performing footstep GRF-based person recognition is also proposed, as well as a comparison of feature extractors, normalizers, and classifiers configurations that were never directly compared with one another in any previous GRF recognition research. Finally, a detailed theoretical overview of many existing machine learning techniques is presented, leading to a proposal of two novel data processing techniques developed specifically for the purpose of gait biometric recognition using GRF. This book · introduces novel machine-learning-based temporal normalization techniques · bridges research gaps concerning the effect of ...

  19. Fabrication of titanium implant-retained restorations with nontraditional machining techniques.

    Science.gov (United States)

    Schmitt, S M; Chance, D A

    1995-01-01

    Traditional laboratory techniques are being supplemented by modern precision technologies to solve complex restorative problems. Electrical discharge machining combined with laser scanning and computer aided design-computer aided manufacturing can create very precise restorations without the lost wax method. A laser scanner is used to create a three-dimensional polyline data model that can then be converted into a stereolithography file format for output to a stereolithography apparatus or other rapid prototyping device. A stereolithography-generated model is used to create an electric discharge machining electrode via copper electroforming. This electrode is used to machine dental restorations from an ingot of titanium, bypassing the conventional lost wax casting process. Retaining screw access holes are machined using conventional drilling procedures, but could be accomplished with electric discharge machining if desired. Other rapid prototyping technologies are briefly discussed.

  20. A new optical flat surface measurement method based on machine vision and deflectometry

    Science.gov (United States)

    Kewei, E.; Li, Dahai; Yang, Lijie; Guo, Guangrao; Li, Mengyang; Wang, Xuemin; Zhang, Tao; Xiong, Zhao

    2016-10-01

    Phase Measuring Deflectometry(PMD) is a non-contact, high dynamic-range and full-field metrology which becomes a serious competitor to interferometry. However, the accuracy of deflectometry metrology is strongly influenced by the level of the calibrations. Our paper presents a calibration-based PMD method to test optical flat surface with a high accuracy. In our method, a pin-hole camera was set next to the LCD screen which is used to project sinusoidal fringes to the test flat. And the test flat was placed parallel to the direction of the LCD screen, which makes the geometry calibration process are simplified. The photogrammetric methods used in computer vision science was used to calibrate the pin-hole camera by using a checker pattern shown on another LCD display at six different orientations, the intrinsic parameters can be obtained by processing the obtained image of checker patterns. Further, by making the last orientation of checker pattern is aligned at the same position as the test optical flat, the algorithms used in this paper can obtain the mapping relationship between the CCD pixels and the subaperture coordinates on the test optical flat. We test a optical flat with a size of 50mm in diameter using our setup and algorithm. Our experimental results of optical flat figure from low to high order aberrations show a good agreement with that from the Fizeau interferometer.

  1. Feasibility of Applying Controllable Lubrication Techniques to Reciprocating Machines

    DEFF Research Database (Denmark)

    Pulido, Edgar Estupinan

    modified hydrostatic lubrication. In this case, the hydrostatic lubrication is modified by injecting oil at controllable pressures, through orifices circumferentially located around the bearing surface. In order to study the performance of journal bearings of reciprocating machines, operating under...... conventional lubrication conditions, a mathematical model of a reciprocating mechanism connected to a rigid / flexible rotor via thin fluid films was developed. The mathematical model involves the use of multibody dynamics theory for the modelling of the reciprocating mechanism (rigid bodies), finite elements...... of the reciprocating engine, obtained with the help of multibody dynamics (rigid components) and finite elements method (flexible components), and the global system of equations is numerically solved. The analysis of the results was carried out with focus on the behaviour of the journal orbits, maximum fluid film...

  2. FRC Separatrix inference using machine-learning techniques

    Science.gov (United States)

    Romero, Jesus; Roche, Thomas; the TAE Team

    2016-10-01

    As Field Reversed Configuration (FRC) devices approach lifetimes exceeding the characteristic time of conductive structures external to the plasma, plasma stabilization cannot be achieved solely by the flux conserving effect of the external structures, and active control systems are then necessary. An essential component of such control systems is a reconstruction method for the plasma separatrix suitable for real time. We report on a method to infer the separatrix in an FRC using the information of magnetic probes located externally to the plasma. The method uses machine learning methods, namely Bayesian inference of Gaussian Processes, to obtain the most likely plasma current density distribution given the measurements of magnetic field external to the plasma. From the current sources, flux function and in particular separatrix are easily computed. The reconstruction method is non iterative and hence suitable for deterministic real time applications. Validation results with numerical simulations and application to separatrix inference of C-2U plasma discharges will be presented.

  3. Use of machine learning techniques for modeling of snow depth

    Directory of Open Access Journals (Sweden)

    G. V. Ayzel

    2017-01-01

    Full Text Available Snow exerts significant regulating effect on the land hydrological cycle since it controls intensity of heat and water exchange between the soil-vegetative cover and the atmosphere. Estimating of a spring flood runoff or a rain-flood on mountainous rivers requires understanding of the snow cover dynamics on a watershed. In our work, solving a problem of the snow cover depth modeling is based on both available databases of hydro-meteorological observations and easily accessible scientific software that allows complete reproduction of investigation results and further development of this theme by scientific community. In this research we used the daily observational data on the snow cover and surface meteorological parameters, obtained at three stations situated in different geographical regions: Col de Porte (France, Sodankyla (Finland, and Snoquamie Pass (USA.Statistical modeling of the snow cover depth is based on a complex of freely distributed the present-day machine learning models: Decision Trees, Adaptive Boosting, Gradient Boosting. It is demonstrated that use of combination of modern machine learning methods with available meteorological data provides the good accuracy of the snow cover modeling. The best results of snow cover depth modeling for every investigated site were obtained by the ensemble method of gradient boosting above decision trees – this model reproduces well both, the periods of snow cover accumulation and its melting. The purposeful character of learning process for models of the gradient boosting type, their ensemble character, and use of combined redundancy of a test sample in learning procedure makes this type of models a good and sustainable research tool. The results obtained can be used for estimating the snow cover characteristics for river basins where hydro-meteorological information is absent or insufficient.

  4. TWO-DIMENSION CODE RECOGNITION BASED ON MACHINE VISION%基于机器视觉的2D码识别

    Institute of Scientific and Technical Information of China (English)

    常晓玮

    2014-01-01

    According to components detection of the auto parts supply chain,put forward a kind code recognition method for DataMatrix code,PDF41 7 code,and QR code based on machine vision.The method of obtaining image firstly,and then using the image denoising technique to the image acquired with a 2D code processing,and then select different processing methods according to the 2D code can effectively identify these three 2D codes.The experimental result shows that the proposed approach is feasible and effective and can recognize DataMatrix code,PDF41 7 code,QR code real -timely.%针对汽车生产中零部件供应环节的部件检测应用,提出一种基于机器视觉的DataMatrix码、PDF417码、QR码识别方法。本方法利用图像获取、图像去噪等技术对获取的具有2D码的图像进行处理,然后根据2D码选取不同的处理方法,能有效识别以上三种2D码。实验结果表明该方法是可行的,能实时识别DataMatrix码、PDF417码、QR码。

  5. Application of Artificial Intelligence Techniques for the Control of the Asynchronous Machine

    Directory of Open Access Journals (Sweden)

    F. Khammar

    2016-01-01

    Full Text Available The induction machine is experiencing a growing success for two decades by gradually replacing the DC machines and synchronous in many industrial applications. This paper is devoted to the study of advanced methods applied to the command of the asynchronous machine in order to obtain a system of control of high performance. While the criteria for response time, overtaking, and static error can be assured by the techniques of conventional control, the criterion of robustness remains a challenge for researchers. This criterion can be satisfied only by applying advanced techniques of command. After mathematical modeling of the asynchronous machine, it defines the control strategies based on the orientation of the rotor flux. The results of the different simulation tests highlight the properties of robustness of algorithms proposed and suggested to compare the different control strategies.

  6. Computer vision and machine learning for robust phenotyping in genome-wide studies

    Science.gov (United States)

    Zhang, Jiaoping; Naik, Hsiang Sing; Assefa, Teshale; Sarkar, Soumik; Reddy, R. V. Chowda; Singh, Arti; Ganapathysubramanian, Baskar; Singh, Asheesh K.

    2017-01-01

    Traditional evaluation of crop biotic and abiotic stresses are time-consuming and labor-intensive limiting the ability to dissect the genetic basis of quantitative traits. A machine learning (ML)-enabled image-phenotyping pipeline for the genetic studies of abiotic stress iron deficiency chlorosis (IDC) of soybean is reported. IDC classification and severity for an association panel of 461 diverse plant-introduction accessions was evaluated using an end-to-end phenotyping workflow. The workflow consisted of a multi-stage procedure including: (1) optimized protocols for consistent image capture across plant canopies, (2) canopy identification and registration from cluttered backgrounds, (3) extraction of domain expert informed features from the processed images to accurately represent IDC expression, and (4) supervised ML-based classifiers that linked the automatically extracted features with expert-rating equivalent IDC scores. ML-generated phenotypic data were subsequently utilized for the genome-wide association study and genomic prediction. The results illustrate the reliability and advantage of ML-enabled image-phenotyping pipeline by identifying previously reported locus and a novel locus harboring a gene homolog involved in iron acquisition. This study demonstrates a promising path for integrating the phenotyping pipeline into genomic prediction, and provides a systematic framework enabling robust and quicker phenotyping through ground-based systems. PMID:28272456

  7. Computer vision and machine learning for robust phenotyping in genome-wide studies.

    Science.gov (United States)

    Zhang, Jiaoping; Naik, Hsiang Sing; Assefa, Teshale; Sarkar, Soumik; Reddy, R V Chowda; Singh, Arti; Ganapathysubramanian, Baskar; Singh, Asheesh K

    2017-03-08

    Traditional evaluation of crop biotic and abiotic stresses are time-consuming and labor-intensive limiting the ability to dissect the genetic basis of quantitative traits. A machine learning (ML)-enabled image-phenotyping pipeline for the genetic studies of abiotic stress iron deficiency chlorosis (IDC) of soybean is reported. IDC classification and severity for an association panel of 461 diverse plant-introduction accessions was evaluated using an end-to-end phenotyping workflow. The workflow consisted of a multi-stage procedure including: (1) optimized protocols for consistent image capture across plant canopies, (2) canopy identification and registration from cluttered backgrounds, (3) extraction of domain expert informed features from the processed images to accurately represent IDC expression, and (4) supervised ML-based classifiers that linked the automatically extracted features with expert-rating equivalent IDC scores. ML-generated phenotypic data were subsequently utilized for the genome-wide association study and genomic prediction. The results illustrate the reliability and advantage of ML-enabled image-phenotyping pipeline by identifying previously reported locus and a novel locus harboring a gene homolog involved in iron acquisition. This study demonstrates a promising path for integrating the phenotyping pipeline into genomic prediction, and provides a systematic framework enabling robust and quicker phenotyping through ground-based systems.

  8. Towards Sustainable Green Production: Exploring Automated Grading for Oil Palm Fresh Fruit Bunches (FFB Using Machine Vision and Spectral Analysis

    Directory of Open Access Journals (Sweden)

    Muhammad Makky

    2013-01-01

    Full Text Available Over the last decade, Indonesian palm oil industry has become a leading producer of the world, and been able to generatenotable foreign export reserves. In spite of this, problems still persist in this industry, including low productivity due to mishandling of raw material in post-harvest operations. One of the prime causes of this is manual grading/sorting of fresh fruit bunches, which is prone to error and misjudgement, as well as subjectivity. High demand of oil palm establishes its high price in world market, which drives the industry to expand its plantation area to increase production. Ultimately, it compromise forests and agricultural land, resulting stagnation or decline in several food products. Alternatively, before expanding plantation extent, oil extraction productivity of existing plantation can be improved by carefully selecting appropriate FFBs for post-harvest processing through introduction of automation. The use of machine vision and spectral analysis has shown to assist productivity of agricultural processing industry. This study employs automation technology for FFB grading in oil palm mills, resulting in improved raw material quality, thereby increasing the oil extraction productivity, and simultaneously contributing to partly release the pressure of deforestation by maintaining green agricultural areas.

  9. DEVELOPMENT OF A PROXIMAL MACHINE VISION SYSTEM FOR OFF-SEASON WEED MAPPING IN BROADACRE NO-TILLAGE FALLOWS

    Directory of Open Access Journals (Sweden)

    H. Liu

    2013-01-01

    Full Text Available Weeds are among the most significant and costly environmental threats in Australian agriculture. Weeds compete with crop plants for moisture, nutrients and sunlight and can have a detrimental impact on crop yields and quality if uncontrolled. The distribution, size, density and species of the weeds are often heterogeneous in the cropping land. Instead of uniformly spray the same type of herbicide to the whole farm land, selective spray can reduce the herbicide usage therefore can reduce the serious problems of herbicide resistance, soil damage and food safety. This study describes a weed mapping method which could be used for broadacre no-tillage fallow weed management. The weed maps have the potential to be used as powerful herbicide prescription maps for spot spray. The weed mapping is realized by the machine vision technologies which including image acquisition, image stitching and photomosaic processing. The sampling points are continuous and the interpolation methods are used at the minimum levels. The experiment result shows that this weed mapping method can map weed under limited conditions.

  10. 机器视觉电动缝纫机关键技术研究%A Study on Key Technologies of Electric Sewing Machine Based on Machine Vision

    Institute of Scientific and Technical Information of China (English)

    崔群; 白顺科

    2014-01-01

    This article introduces the machine vision electric sewing machine,which is an intelligent electric sewing machine based on machine vision and numerical control tool. This machine can recognize and localize all of the sewing pieces of cloth on working table through the image sensors,can grab the sewing pieces of cloth by folding arm manipulator and can complete the accurate and reliable feeding work. So,the form clamp would be removed,and the manual work could be reduced.%研究了一种基于机器视觉和数控技术的工业智能缝纫设备的关键技术。该设备通过图像传感器对工作台上摆放的缝片进行识别与定位,通过送料机械手实现缝片的抓取并实现精确、快速、可靠地缝片的送料,以抛弃目前电动缝纫机的模板夹具,并实现减少操作工干预。

  11. Michelson interferometer experiment based on machine vision%基于 LabVIEW机器视觉的迈克耳孙干涉仪实验

    Institute of Scientific and Technical Information of China (English)

    王建中; 黄林; 唐一文

    2014-01-01

    基于机器视觉设计了迈克耳孙干涉仪实验,采用机器视觉技术替代人眼对目标(干涉圆环)进行分析与测量,可有效避免因视觉疲劳而造成的测量错误。%Based on the machine vision ,the Michelson interferometer was put forward to consider-ably reducing the measurement errors due to visual fatigue in experimental analysis and measure-ments .The interference ring in Michelson interferometer was measured via the machine vision tech-nology ,without observing by naked eyes .

  12. Down syndrome detection from facial photographs using machine learning techniques

    Science.gov (United States)

    Zhao, Qian; Rosenbaum, Kenneth; Sze, Raymond; Zand, Dina; Summar, Marshall; Linguraru, Marius George

    2013-02-01

    Down syndrome is the most commonly occurring chromosomal condition; one in every 691 babies in United States is born with it. Patients with Down syndrome have an increased risk for heart defects, respiratory and hearing problems and the early detection of the syndrome is fundamental for managing the disease. Clinically, facial appearance is an important indicator in diagnosing Down syndrome and it paves the way for computer-aided diagnosis based on facial image analysis. In this study, we propose a novel method to detect Down syndrome using photography for computer-assisted image-based facial dysmorphology. Geometric features based on facial anatomical landmarks, local texture features based on the Contourlet transform and local binary pattern are investigated to represent facial characteristics. Then a support vector machine classifier is used to discriminate normal and abnormal cases; accuracy, precision and recall are used to evaluate the method. The comparison among the geometric, local texture and combined features was performed using the leave-one-out validation. Our method achieved 97.92% accuracy with high precision and recall for the combined features; the detection results were higher than using only geometric or texture features. The promising results indicate that our method has the potential for automated assessment for Down syndrome from simple, noninvasive imaging data.

  13. Twitter Sentiment Analysis of Movie Reviews using Machine Learning Techniques.

    Directory of Open Access Journals (Sweden)

    Akshay Amolik

    2015-12-01

    Full Text Available Sentiment analysis is basically concerned with analysis of emotions and opinions from text. We can refer sentiment analysis as opinion mining. Sentiment analysis finds and justifies the sentiment of the person with respect to a given source of content. Social media contain huge amount of the sentiment data in the form of tweets, blogs, and updates on the status, posts, etc. Sentiment analysis of this largely generated data is very useful to express the opinion of the mass. Twitter sentiment analysis is tricky as compared to broad sentiment analysis because of the slang words and misspellings and repeated characters. We know that the maximum length of each tweet in Twitter is 140 characters. So it is very important to identify correct sentiment of each word. In our project we are proposing a highly accurate model of sentiment analysis of tweets with respect to latest reviews of upcoming Bollywood or Hollywood movies. With the help of feature vector and classifiers such as Support vector machine and Naïve Bayes, we are correctly classifying these tweets as positive, negative and neutral to give sentiment of each tweet.

  14. Resistance gene identification from Larimichthys crocea with machine learning techniques

    Science.gov (United States)

    Cai, Yinyin; Liao, Zhijun; Ju, Ying; Liu, Juan; Mao, Yong; Liu, Xiangrong

    2016-12-01

    The research on resistance genes (R-gene) plays a vital role in bioinformatics as it has the capability of coping with adverse changes in the external environment, which can form the corresponding resistance protein by transcription and translation. It is meaningful to identify and predict R-gene of Larimichthys crocea (L.Crocea). It is friendly for breeding and the marine environment as well. Large amounts of L.Crocea’s immune mechanisms have been explored by biological methods. However, much about them is still unclear. In order to break the limited understanding of the L.Crocea’s immune mechanisms and to detect new R-gene and R-gene-like genes, this paper came up with a more useful combination prediction method, which is to extract and classify the feature of available genomic data by machine learning. The effectiveness of feature extraction and classification methods to identify potential novel R-gene was evaluated, and different statistical analyzes were utilized to explore the reliability of prediction method, which can help us further understand the immune mechanisms of L.Crocea against pathogens. In this paper, a webserver called LCRG-Pred is available at http://server.malab.cn/rg_lc/.

  15. Autonomous Segmentation of Outcrop Images Using Computer Vision and Machine Learning

    Science.gov (United States)

    Francis, R.; McIsaac, K.; Osinski, G. R.; Thompson, D. R.

    2013-12-01

    As planetary exploration missions become increasingly complex and capable, the motivation grows for improved autonomous science. New capabilities for onboard science data analysis may relieve radio-link data limits and provide greater throughput of scientific information. Adaptive data acquisition, storage and downlink may ultimately hold implications for mission design and operations. For surface missions, geology remains an essential focus, and the investigation of in place, exposed geological materials provides the greatest scientific insight and context for the formation and history of planetary materials and processes. The goal of this research program is to develop techniques for autonomous segmentation of images of rock outcrops. Recognition of the relationships between different geological units is the first step in mapping and interpreting a geological setting. Applications of automatic segmentation include instrument placement and targeting and data triage for downlink. Here, we report on the development of a new technique in which a photograph of a rock outcrop is processed by several elementary image processing techniques, generating a feature space which can be interrogated and classified. A distance metric learning technique (Multiclass Discriminant Analysis, or MDA) is tested as a means of finding the best numerical representation of the feature space. MDA produces a linear transformation that maximizes the separation between data points from different geological units. This ';training step' is completed on one or more images from a given locality. Then we apply the same transformation to improve the segmentation of new scenes containing similar materials to those used for training. The technique was tested using imagery from Mars analogue settings at the Cima volcanic flows in the Mojave Desert, California; impact breccias from the Sudbury impact structure in Ontario, Canada; and an outcrop showing embedded mineral veins in Gale Crater on Mars

  16. Machinability of hypereutectic cast Al–Si alloys processed by SSM processing technique

    Indian Academy of Sciences (India)

    P K SOOD; RAKESH SEHGAL; D K DWIVEDI

    2017-03-01

    Experimental investigation carried out on the machinability studies to determine the influence of semi-solid metal processing and modification on hypereutectic Al–20Si–0.5Mg–1.2Fe-based alloy produced by conventional cast and semi-solid metal processing technique (mechanical stirring) and modified with iron correctors (Be and Cd) has been presented in this paper. The alloys under investigation were prepared bycontrolling melt using an induction melting furnace. Stirring of semi-solid metal takes place at constant cooling conditions from liquidus temperature at a constant stirring speed of 400 rpm. To determine the machining performance characteristics an orthogonal array, signal-to-noise ratio and statistical tool analysis of variance were jointly used during experimentation. A CNC lathe was used to conduct experiments in dry condition and coated carbide inserts were used as tool inserts. Machining variables like cutting velocity, approaching angle,feed rate and depth of cut, which can be considered as process parameters, are taken into account. The combined effect of modification and semi-solid metal processing has a significant effect on the machining characteristics,which was concluded from study. The modified alloy processed by semi-solid metal processing technique exhibits better machinability conditions when compared with the conventional cast. The feed rate has more effect on machining behaviour.

  17. A Novel Machine Vision System for the Inspection of Micro-Spray Nozzle

    Directory of Open Access Journals (Sweden)

    Kuo-Yi Huang

    2015-06-01

    Full Text Available In this study, we present an application of neural network and image processing techniques for detecting the defects of an internal micro-spray nozzle. The defect regions were segmented by Canny edge detection, a randomized algorithm for detecting circles and a circle inspection (CI algorithm. The gray level co-occurrence matrix (GLCM was further used to evaluate the texture features of the segmented region. These texture features (contrast, entropy, energy, color features (mean and variance of gray level and geometric features (distance variance, mean diameter and diameter ratio were used in the classification procedures. A back-propagation neural network classifier was employed to detect the defects of micro-spray nozzles. The methodology presented herein effectively works for detecting micro-spray nozzle defects to an accuracy of 90.71%.

  18. Arabic Keyphrase Extraction using Linguistic knowledge and Machine Learning Techniques

    CERN Document Server

    El-shishtawy, Tarek

    2012-01-01

    In this paper, a supervised learning technique for extracting keyphrases of Arabic documents is presented. The extractor is supplied with linguistic knowledge to enhance its efficiency instead of relying only on statistical information such as term frequency and distance. During analysis, an annotated Arabic corpus is used to extract the required lexical features of the document words. The knowledge also includes syntactic rules based on part of speech tags and allowed word sequences to extract the candidate keyphrases. In this work, the abstract form of Arabic words is used instead of its stem form to represent the candidate terms. The Abstract form hides most of the inflections found in Arabic words. The paper introduces new features of keyphrases based on linguistic knowledge, to capture titles and subtitles of a document. A simple ANOVA test is used to evaluate the validity of selected features. Then, the learning model is built using the LDA - Linear Discriminant Analysis - and training documents. Althou...

  19. Fractographic classification in metallic materials by using 3D processing and computer vision techniques

    Directory of Open Access Journals (Sweden)

    Maria Ximena Bastidas-Rodríguez

    2016-09-01

    Full Text Available Failure analysis aims at collecting information about how and why a failure is produced. The first step in this process is a visual inspection on the flaw surface that will reveal the features, marks, and texture, which characterize each type of fracture. This is generally carried out by personnel with no experience that usually lack the knowledge to do it. This paper proposes a classification method for three kinds of fractures in crystalline materials: brittle, fatigue, and ductile. The method uses 3D vision, and it is expected to support failure analysis. The features used in this work were: i Haralick’s features and ii the fractal dimension. These features were applied to 3D images obtained from a confocal laser scanning microscopy Zeiss LSM 700. For the classification, we evaluated two classifiers: Artificial Neural Networks and Support Vector Machine. The performance evaluation was made by extracting four marginal relations from the confusion matrix: accuracy, sensitivity, specificity, and precision, plus three evaluation methods: Receiver Operating Characteristic space, the Individual Classification Success Index, and the Jaccard’s coefficient. Despite the classification percentage obtained by an expert is better than the one obtained with the algorithm, the algorithm achieves a classification percentage near or exceeding the 60 % accuracy for the analyzed failure modes. The results presented here provide a good approach to address future research on texture analysis using 3D data.

  20. An element search ant colony technique for solving virtual machine placement problem

    Science.gov (United States)

    Srija, J.; Rani John, Rose; Kanaga, Grace Mary, Dr.

    2017-09-01

    The data centres in the cloud environment play a key role in providing infrastructure for ubiquitous computing, pervasive computing, mobile computing etc. This computing technique tries to utilize the available resources in order to provide services. Hence maintaining the resource utilization without wastage of power consumption has become a challenging task for the researchers. In this paper we propose the direct guidance ant colony system for effective mapping of virtual machines to the physical machine with maximal resource utilization and minimal power consumption. The proposed algorithm has been compared with the existing ant colony approach which is involved in solving virtual machine placement problem and thus the proposed algorithm proves to provide better result than the existing technique.

  1. 机器视觉在农业生产中的应用研究%Summary of Research on Machine Vision Application in Agricultural Production

    Institute of Scientific and Technical Information of China (English)

    刁智华; 王会丹; 魏伟

    2014-01-01

    Because of the non-destructive , high accuracy , speed and other advantages , Machine vision technology was widely used in modern agricultural production .Based on the previous research results and analysis of literature , the pro-gress in agricultural product quality grading and testing ,controlling injurious insects and weeds in farmland , agricultural automatic picking system and crop growth process detection and agricultural machine navigation based on machine vision technology were reviewed in this paper .And then they were analyzed and summarized .In the end ,open problems and fur-ther research of machine vision technology in agricultural production application were discussed .%机器视觉技术因其非破坏性、精度高、速度快等特点,在现代农业生产中得到广泛应用。基于前人研究成果和文献分析,综述了近年来机器视觉技术在农产品质量分级与检测、农田病虫草害控制、农业自动采摘系统、农作物生长过程检测以及农业机械导航等方面的国内外研究进展,并对机器视觉技术在各领域的研究情况进行分析和总结,提出了机器视觉技术在农业生产应用中存在的问题和未来的研究方向。

  2. Reconstructing muscle activation during normal walking: a comparison of symbolic and connectionist machine learning techniques

    NARCIS (Netherlands)

    Heller, Ben W.; Veltink, Peter H.; Rijkhoff, Nico J.M.; Rutten, Wim L.C.; Andrews, Brian J.

    1993-01-01

    One symbolic (rule-based inductive learning) and one connectionist (neural network) machine learning technique were used to reconstruct muscle activation patterns from kinematic data measured during normal human walking at several speeds. The activation patterns (or desired outputs) consisted of sur

  3. A novel method to estimate model uncertainty using machine learning techniques

    NARCIS (Netherlands)

    Solomatine, D.P.; Lal Shrestha, D.

    2009-01-01

    A novel method is presented for model uncertainty estimation using machine learning techniques and its application in rainfall runoff modeling. In this method, first, the probability distribution of the model error is estimated separately for different hydrological situations and second, the

  4. Reconstructing muscle activation during normal walking: a comparison of symbolic and connectionist machine learning techniques

    NARCIS (Netherlands)

    Heller, Ben W.; Veltink, Petrus H.; Rijkhoff, N.J.M.; Rijkhoff, Nico J.M.; Rutten, Wim; Andrews, Brian J.

    1993-01-01

    One symbolic (rule-based inductive learning) and one connectionist (neural network) machine learning technique were used to reconstruct muscle activation patterns from kinematic data measured during normal human walking at several speeds. The activation patterns (or desired outputs) consisted of

  5. Exploring Machine Learning Techniques Using Patient Interactions in Online Health Forums to Classify Drug Safety

    Science.gov (United States)

    Chee, Brant Wah Kwong

    2011-01-01

    This dissertation explores the use of personal health messages collected from online message forums to predict drug safety using natural language processing and machine learning techniques. Drug safety is defined as any drug with an active safety alert from the US Food and Drug Administration (FDA). It is believed that this is the first…

  6. Exploring Machine Learning Techniques Using Patient Interactions in Online Health Forums to Classify Drug Safety

    Science.gov (United States)

    Chee, Brant Wah Kwong

    2011-01-01

    This dissertation explores the use of personal health messages collected from online message forums to predict drug safety using natural language processing and machine learning techniques. Drug safety is defined as any drug with an active safety alert from the US Food and Drug Administration (FDA). It is believed that this is the first…

  7. Locomotion training of legged robots using hybrid machine learning techniques

    Science.gov (United States)

    Simon, William E.; Doerschuk, Peggy I.; Zhang, Wen-Ran; Li, Andrew L.

    1995-01-01

    In this study artificial neural networks and fuzzy logic are used to control the jumping behavior of a three-link uniped robot. The biped locomotion control problem is an increment of the uniped locomotion control. Study of legged locomotion dynamics indicates that a hierarchical controller is required to control the behavior of a legged robot. A structured control strategy is suggested which includes navigator, motion planner, biped coordinator and uniped controllers. A three-link uniped robot simulation is developed to be used as the plant. Neurocontrollers were trained both online and offline. In the case of on-line training, a reinforcement learning technique was used to train the neurocontroller to make the robot jump to a specified height. After several hundred iterations of training, the plant output achieved an accuracy of 7.4%. However, when jump distance and body angular momentum were also included in the control objectives, training time became impractically long. In the case of off-line training, a three-layered backpropagation (BP) network was first used with three inputs, three outputs and 15 to 40 hidden nodes. Pre-generated data were presented to the network with a learning rate as low as 0.003 in order to reach convergence. The low learning rate required for convergence resulted in a very slow training process which took weeks to learn 460 examples. After training, performance of the neurocontroller was rather poor. Consequently, the BP network was replaced by a Cerebeller Model Articulation Controller (CMAC) network. Subsequent experiments described in this document show that the CMAC network is more suitable to the solution of uniped locomotion control problems in terms of both learning efficiency and performance. A new approach is introduced in this report, viz., a self-organizing multiagent cerebeller model for fuzzy-neural control of uniped locomotion is suggested to improve training efficiency. This is currently being evaluated for a possible

  8. Classification of the Regional Ionospheric Disturbance Based on Machine Learning Techniques

    Science.gov (United States)

    Terzi, Merve Begum; Arikan, Orhan; Karatay, Secil; Arikan, Feza; Gulyaeva, Tamara

    2016-08-01

    In this study, Total Electron Content (TEC) estimated from GPS receivers is used to model the regional and local variability that differs from global activity along with solar and geomagnetic indices. For the automated classification of regional disturbances, a classification technique based on a robust machine learning technique that have found wide spread use, Support Vector Machine (SVM) is proposed. Performance of developed classification technique is demonstrated for midlatitude ionosphere over Anatolia using TEC estimates generated from GPS data provided by Turkish National Permanent GPS Network (TNPGN-Active) for solar maximum year of 2011. As a result of implementing developed classification technique to Global Ionospheric Map (GIM) TEC data, which is provided by the NASA Jet Propulsion Laboratory (JPL), it is shown that SVM can be a suitable learning method to detect anomalies in TEC variations.

  9. Prediction of mortality after radical cystectomy for bladder cancer by machine learning techniques.

    Science.gov (United States)

    Wang, Guanjin; Lam, Kin-Man; Deng, Zhaohong; Choi, Kup-Sze

    2015-08-01

    Bladder cancer is a common cancer in genitourinary malignancy. For muscle invasive bladder cancer, surgical removal of the bladder, i.e. radical cystectomy, is in general the definitive treatment which, unfortunately, carries significant morbidities and mortalities. Accurate prediction of the mortality of radical cystectomy is therefore needed. Statistical methods have conventionally been used for this purpose, despite the complex interactions of high-dimensional medical data. Machine learning has emerged as a promising technique for handling high-dimensional data, with increasing application in clinical decision support, e.g. cancer prediction and prognosis. Its ability to reveal the hidden nonlinear interactions and interpretable rules between dependent and independent variables is favorable for constructing models of effective generalization performance. In this paper, seven machine learning methods are utilized to predict the 5-year mortality of radical cystectomy, including back-propagation neural network (BPN), radial basis function (RBFN), extreme learning machine (ELM), regularized ELM (RELM), support vector machine (SVM), naive Bayes (NB) classifier and k-nearest neighbour (KNN), on a clinicopathological dataset of 117 patients of the urology unit of a hospital in Hong Kong. The experimental results indicate that RELM achieved the highest average prediction accuracy of 0.8 at a fast learning speed. The research findings demonstrate the potential of applying machine learning techniques to support clinical decision making. Copyright © 2015 Elsevier Ltd. All rights reserved.

  10. Design of vision concepts to explore the future: Nature, context and design techniques

    NARCIS (Netherlands)

    Mejia Sarmiento, J.R.; Simonse, W.L.

    2015-01-01

    Industrial firms are facing a constant dilemma, to be ready for the future, have a vision, and at the same time act within the current situation, exploit current products efficiently. This research examines visions that embody future opportunities and ideas, “vision concepts” such as concept cars an

  11. Design of vision concepts to explore the future: Nature, context and design techniques

    NARCIS (Netherlands)

    Mejia Sarmiento, J.R.; Simonse, W.L.

    2015-01-01

    Industrial firms are facing a constant dilemma, to be ready for the future, have a vision, and at the same time act within the current situation, exploit current products efficiently. This research examines visions that embody future opportunities and ideas, “vision concepts” such as concept cars

  12. State of the Art Review for Applying Computational Intelligence and Machine Learning Techniques to Portfolio Optimisation

    CERN Document Server

    Hurwitz, Evan

    2009-01-01

    Computational techniques have shown much promise in the field of Finance, owing to their ability to extract sense out of dauntingly complex systems. This paper reviews the most promising of these techniques, from traditional computational intelligence methods to their machine learning siblings, with particular view to their application in optimising the management of a portfolio of financial instruments. The current state of the art is assessed, and prospective further work is assessed and recommended

  13. Techniques and applications for binaural sound manipulation in human-machine interfaces

    Science.gov (United States)

    Begault, Durand R.; Wenzel, Elizabeth M.

    1992-01-01

    The implementation of binaural sound to speech and auditory sound cues (auditory icons) is addressed from both an applications and technical standpoint. Techniques overviewed include processing by means of filtering with head-related transfer functions. Application to advanced cockpit human interface systems is discussed, although the techniques are extendable to any human-machine interface. Research issues pertaining to three-dimensional sound displays under investigation at the Aerospace Human Factors Division at NASA Ames Research Center are described.

  14. The Research of Multi Target Tracking Based on Machine Vision%基于机器视觉的多目标跟踪技术的研究

    Institute of Scientific and Technical Information of China (English)

    徐仲勋; 黄科程

    2015-01-01

    Machine vision can help the robot object recognition and operations on objects. In this paper,based on the industrial robot, a machine vision system is added. The system is composed of the robot,camera,image acquisition card,computer and system software. With the robot vision system,features and objects recognition can capture the target object,then the object position,the control of industri⁃al robots to perform tasks.%机器视觉可以帮助机器识别物体并对物体进行作业。本文在工业机器人的设计基础上,增加一个机器视觉系统。系统由机器人、摄像机、图像采集卡、计算机及系统软件所构成。机器人凭着这个视觉系统,可以捕获目标物体的特征并且识别目标物体,然后对物体进行定位,最后控制工业机器人完成作业任务。

  15. ISOLATED SPEECH RECOGNITION SYSTEM FOR TAMIL LANGUAGE USING STATISTICAL PATTERN MATCHING AND MACHINE LEARNING TECHNIQUES

    Directory of Open Access Journals (Sweden)

    VIMALA C.

    2015-05-01

    Full Text Available In recent years, speech technology has become a vital part of our daily lives. Various techniques have been proposed for developing Automatic Speech Recognition (ASR system and have achieved great success in many applications. Among them, Template Matching techniques like Dynamic Time Warping (DTW, Statistical Pattern Matching techniques such as Hidden Markov Model (HMM and Gaussian Mixture Models (GMM, Machine Learning techniques such as Neural Networks (NN, Support Vector Machine (SVM, and Decision Trees (DT are most popular. The main objective of this paper is to design and develop a speaker-independent isolated speech recognition system for Tamil language using the above speech recognition techniques. The background of ASR system, the steps involved in ASR, merits and demerits of the conventional and machine learning algorithms and the observations made based on the experiments are presented in this paper. For the above developed system, highest word recognition accuracy is achieved with HMM technique. It offered 100% accuracy during training process and 97.92% for testing process.

  16. Research on color difference detection algorithm based on machine vision%基于机器视觉的色差检测算法

    Institute of Scientific and Technical Information of China (English)

    范鹏飞; 孙俊

    2016-01-01

    Algorithm for color difference detection based on machine vision is studied to solve the problem of color difference detection in metal printing. By printing color mark in metal blank area,capture color mark areas on metal printing products using industrial CCD cameras,digital image processing technique is applied to extract color. Use color difference detection algorithm based on HSV color space and color difference detection algorithm based on CIELAB color space,analyze color difference detection results in two kinds of color space,using the method combining the two kinds of color space detection algorithms,implement rapid and effective detection,and at the same time,it can ensure accuracy of detection results.%针对金属印刷质量中的色差检测问题,采用机器视觉的技术对色差检测算法进行了研究。通过在金属印刷品的留白区域印刷色标,使用工业CCD相机采集金属印刷产品上的色标区域,使用数字图像处理技术提取色标。使用了基于HSV颜色空间的色差检测算法和基于CIELAB颜色空间的色差检测算法,分析了两种颜色空间下色差检测的实验结果,采用两种颜色空间检测算法相结合的方法,实现对色差合理有效的快速检测,同时能保证检测结果的准确性。

  17. Remotely sensed data assimilation technique to develop machine learning models for use in water management

    Science.gov (United States)

    Zaman, Bushra

    Increasing population and water conflicts are making water management one of the most important issues of the present world. It has become absolutely necessary to find ways to manage water more efficiently. Technological advancement has introduced various techniques for data acquisition and analysis, and these tools can be used to address some of the critical issues that challenge water resource management. This research used learning machine techniques and information acquired through remote sensing, to solve problems related to soil moisture estimation and crop identification on large spatial scales. In this dissertation, solutions were proposed in three problem areas that can be important in the decision making process related to water management in irrigated systems. A data assimilation technique was used to build a learning machine model that generated soil moisture estimates commensurate with the scale of the data. The research was taken further by developing a multivariate machine learning algorithm to predict root zone soil moisture both in space and time. Further, a model was developed for supervised classification of multi-spectral reflectance data using a multi-class machine learning algorithm. The procedure was designed for classifying crops but the model is data dependent and can be used with other datasets and hence can be applied to other landcover classification problems. The dissertation compared the performance of relevance vector and the support vector machines in estimating soil moisture. A multivariate relevance vector machine algorithm was tested in the spatio-temporal prediction of soil moisture, and the multi-class relevance vector machine model was used for classifying different crop types. It was concluded that the classification scheme may uncover important data patterns contributing greatly to knowledge bases, and to scientific and medical research. The results for the soil moisture models would give a rough idea to farmers

  18. Machine learning and microsimulation techniques on the prognosis of dementia: A systematic literature review.

    Science.gov (United States)

    Dallora, Ana Luiza; Eivazzadeh, Shahryar; Mendes, Emilia; Berglund, Johan; Anderberg, Peter

    2017-01-01

    Dementia is a complex disorder characterized by poor outcomes for the patients and high costs of care. After decades of research little is known about its mechanisms. Having prognostic estimates about dementia can help researchers, patients and public entities in dealing with this disorder. Thus, health data, machine learning and microsimulation techniques could be employed in developing prognostic estimates for dementia. The goal of this paper is to present evidence on the state of the art of studies investigating and the prognosis of dementia using machine learning and microsimulation techniques. To achieve our goal we carried out a systematic literature review, in which three large databases-Pubmed, Socups and Web of Science were searched to select studies that employed machine learning or microsimulation techniques for the prognosis of dementia. A single backward snowballing was done to identify further studies. A quality checklist was also employed to assess the quality of the evidence presented by the selected studies, and low quality studies were removed. Finally, data from the final set of studies were extracted in summary tables. In total 37 papers were included. The data summary results showed that the current research is focused on the investigation of the patients with mild cognitive impairment that will evolve to Alzheimer's disease, using machine learning techniques. Microsimulation studies were concerned with cost estimation and had a populational focus. Neuroimaging was the most commonly used variable. Prediction of conversion from MCI to AD is the dominant theme in the selected studies. Most studies used ML techniques on Neuroimaging data. Only a few data sources have been recruited by most studies and the ADNI database is the one most commonly used. Only two studies have investigated the prediction of epidemiological aspects of Dementia using either ML or MS techniques. Finally, care should be taken when interpreting the reported accuracy of ML

  19. Process acceptance and adjustment techniques for Swiss automatic screw machine parts. Final report

    Energy Technology Data Exchange (ETDEWEB)

    Robb, J.M.

    1976-01-01

    Product tolerance requirements for small, cylindrical, piece parts produced on swiss automatic screw machines have progressed to the reliability limits of inspection equipment. The miniature size, configuration, and tolerance requirements (plus or minus 0.0001 in.) (0.00254 mm) of these parts preclude the use of screening techniques to accept product or adjust processes during setup and production runs; therefore, existing means of product acceptance and process adjustment must be refined or new techniques must be developed. The purpose of this endeavor has been to determine benefits gained through the implementation of a process acceptance technique (PAT) to swiss automatic screw machine processes. PAT is a statistical approach developed for the purpose of accepting product and centering processes for parts produced by selected, controlled processes. Through this endeavor a determination has been made of the conditions under which PAT can benefit a controlled process and some specific types of screw machine processes upon which PAT could be applied. However, it was also determined that PAT, if used indiscriminately, may become a record keeping burden when applied to more than one dimension at a given machining operation. (auth)

  20. Micro Vision

    OpenAIRE

    Ohba, Kohtaro; OHARA, Kenichi

    2007-01-01

    In the field of the micro vision, there are few researches compared with macro environment. However, applying to the study result for macro computer vision technique, you can measure and observe the micro environment. Moreover, based on the effects of micro environment, it is possible to discovery the new theories and new techniques.

  1. Machine and deep learning techniques in heavy-ion collisions with ALICE arXiv

    CERN Document Server

    INSPIRE-00382877

    Over the last years, machine learning tools have been successfully applied to a wealth of problems in high-energy physics. A typical example is the classification of physics objects. Supervised machine learning methods allow for significant improvements in classification problems by taking into account observable correlations and by learning the optimal selection from examples, e.g. from Monte Carlo simulations. Even more promising is the usage of deep learning techniques. Methods like deep convolutional networks might be able to catch features from low-level parameters that are not exploited by default cut-based methods. These ideas could be particularly beneficial for measurements in heavy-ion collisions, because of the very large multiplicities. Indeed, machine learning methods potentially perform much better in systems with a large number of degrees of freedom compared to cut-based methods. Moreover, many key heavy-ion observables are most interesting at low transverse momentum where the underlying event ...

  2. The Recognition of Face-Gear' s Tooth Surface based on the Theory of Machine Vision%基于计算机视觉的面齿轮齿面重构技术

    Institute of Scientific and Technical Information of China (English)

    冯美君; 佟勇

    2012-01-01

    As face-gear is used on aeronautical drives system, the advantages of the face-gear drives has been known by people and the research of the face-gear is deep going. Machine vision has the characteristics of high precision, fast speed, no-contact, the thesis has research the application of the machine vision measurement technique on the tooth surface reconstruction, the mathematic model of camera imaging is analyzed, 2D digital image of tooth surface is acquired by two cameras, computer is used to process image and reconstruct 3D coordinates of the key point on the surface of the face-gear and finally realize the reconstruction, this paper provides support for further machining of the face-gear.%随着面齿轮在航空传动系统上的应用,面齿轮传动的优点逐渐被人们认识,对面齿轮的研究也不断深入.计算机视觉检测技术具有检测精度高、实现速度快、非接触检测的特点,论文研究了计算机视觉测量技术在面齿轮齿面重构中的应用,建立了摄像机成像的数学模型,由摄像机获取齿轮齿面的二维图像,通过计算机对数字图像进行图像处理,计算出关键齿面点的三维坐标,实现面齿轮齿面的重构,为基于面齿轮实体的数字化加工奠定了基础.

  3. Parallel Algorithm for GPU Processing; for use in High Speed Machine Vision Sensing of Cotton Lint Trash

    Directory of Open Access Journals (Sweden)

    Mathew G. Pelletier

    2008-02-01

    Full Text Available One of the main hurdles standing in the way of optimal cleaning of cotton lint isthe lack of sensing systems that can react fast enough to provide the control system withreal-time information as to the level of trash contamination of the cotton lint. This researchexamines the use of programmable graphic processing units (GPU as an alternative to thePC’s traditional use of the central processing unit (CPU. The use of the GPU, as analternative computation platform, allowed for the machine vision system to gain asignificant improvement in processing time. By improving the processing time, thisresearch seeks to address the lack of availability of rapid trash sensing systems and thusalleviate a situation in which the current systems view the cotton lint either well before, orafter, the cotton is cleaned. This extended lag/lead time that is currently imposed on thecotton trash cleaning control systems, is what is responsible for system operators utilizing avery large dead-band safety buffer in order to ensure that the cotton lint is not undercleaned.Unfortunately, the utilization of a large dead-band buffer results in the majority ofthe cotton lint being over-cleaned which in turn causes lint fiber-damage as well assignificant losses of the valuable lint due to the excessive use of cleaning machinery. Thisresearch estimates that upwards of a 30% reduction in lint loss could be gained through theuse of a tightly coupled trash sensor to the cleaning machinery control systems. Thisresearch seeks to improve processing times through the development of a new algorithm forcotton trash sensing that allows for implementation on a highly parallel architecture.Additionally, by moving the new parallel algorithm onto an alternative computing platform,the graphic processing unit “GPU”, for processing of the cotton trash images, a speed up ofover 6.5 times, over optimized code running on the PC’s central processing

  4. 基于机器视觉技术的无精蛋鉴别研究%Identification of Wind Egg Based on Machine Vision Technology

    Institute of Scientific and Technical Information of China (English)

    李天华; 李海亮

    2011-01-01

    [Objectives] To identify the wind egg by machine vision technology so as to improve the accuracy and rapidness of removing the wind eggs during the incubation process. [ Method] The images of hatching eggs were firstly acquired by CCD camera, and then imported into the computer to analyze it gray scale and detect the hatching eggs, the computer transmitted the signals to the core controller, which controlled the sense station to screen out the wind eggs. [ Result ] The wind eggs could be rapidly and accurately removed after 6-day hatching by using the machine vision technology. [Conclusion] The identification of wind egg by machine vision technology had high theoretical and practical significance.%[目的]采用机器视觉技术鉴别无精蛋,提高孵化生产过程中剔除无精蛋的准确率和速度.[方法]首先用CCD照相机获取入孵蛋图像,再将入孵蛋图像传输到计算机主机,主机测控软件对图像进行灰度特征分析、比较等,对入孵蛋进行检测,主机输出信号给核心控制器,由核心控制器控制检测台对无精蛋进行分选.[结果]采用机器视觉技术可以在入孵后第6天快速准确地剔除无精蛋.[结论]利用机器视觉检测技术鉴别无精蛋,具有较高的理论价值和实际生产意义.

  5. Socket Quality Detection Based on Machine Vision%基于机器视觉的插座品质检测方法

    Institute of Scientific and Technical Information of China (English)

    徐德明; 汪成龙

    2016-01-01

    针对目前人工成本上升,人眼易出现视觉疲劳导致检测效率低下等问题,本文提出了一种基于机器视觉的插座品质检测方法.主要研究内容如下:1)研究了基于模板匹配的插座机器视觉定位方法.2)研究了基于灰度均值的螺丝缺陷机器视觉检测方法.3)研究了基于灰度标准偏差的插孔缺陷机器视觉检测方法.根据以上检测方法,编写了检测软件,在生产线上进行测试,对插座螺丝和插孔缺陷的识别率达到100%,平均每帧图像处理仅耗时100ms,表明该方法具有准确率高、速度快的优点.%For the current rising labor costs and low detection efficiency of eye fatigue, this paper presents a method for detecting socket quality based on machine vision. The main research contents are as follows: 1) Socket positioning method based on template matching with machine vision. 2) The machine vision detection method for screws defects based on the mean of gray value. 3) The detection method of jack defect based on grayscale standard deviation. In the production line testing, screws and jack defect recognition rate have reached 100%. And the average per-frame image processing only takes 100ms, which shows that this method has high accuracy, fast speed advantages.

  6. Impact of corpus domain for sentiment classification: An evaluation study using supervised machine learning techniques

    Science.gov (United States)

    Karsi, Redouane; Zaim, Mounia; El Alami, Jamila

    2017-07-01

    Thanks to the development of the internet, a large community now has the possibility to communicate and express its opinions and preferences through multiple media such as blogs, forums, social networks and e-commerce sites. Today, it becomes clearer that opinions published on the web are a very valuable source for decision-making, so a rapidly growing field of research called “sentiment analysis” is born to address the problem of automatically determining the polarity (Positive, negative, neutral,…) of textual opinions. People expressing themselves in a particular domain often use specific domain language expressions, thus, building a classifier, which performs well in different domains is a challenging problem. The purpose of this paper is to evaluate the impact of domain for sentiment classification when using machine learning techniques. In our study three popular machine learning techniques: Support Vector Machines (SVM), Naive Bayes and K nearest neighbors(KNN) were applied on datasets collected from different domains. Experimental results show that Support Vector Machines outperforms other classifiers in all domains, since it achieved at least 74.75% accuracy with a standard deviation of 4,08.

  7. a Holistic Approach for Inspection of Civil Infrastructures Based on Computer Vision Techniques

    Science.gov (United States)

    Stentoumis, C.; Protopapadakis, E.; Doulamis, A.; Doulamis, N.

    2016-06-01

    In this work, it is examined the 2D recognition and 3D modelling of concrete tunnel cracks, through visual cues. At the time being, the structural integrity inspection of large-scale infrastructures is mainly performed through visual observations by human inspectors, who identify structural defects, rate them and, then, categorize their severity. The described approach targets at minimum human intervention, for autonomous inspection of civil infrastructures. The shortfalls of existing approaches in crack assessment are being addressed by proposing a novel detection scheme. Although efforts have been made in the field, synergies among proposed techniques are still missing. The holistic approach of this paper exploits the state of the art techniques of pattern recognition and stereo-matching, in order to build accurate 3D crack models. The innovation lies in the hybrid approach for the CNN detector initialization, and the use of the modified census transformation for stereo matching along with a binary fusion of two state-of-the-art optimization schemes. The described approach manages to deal with images of harsh radiometry, along with severe radiometric differences in the stereo pair. The effectiveness of this workflow is evaluated on a real dataset gathered in highway and railway tunnels. What is promising is that the computer vision workflow described in this work can be transferred, with adaptations of course, to other infrastructure such as pipelines, bridges and large industrial facilities that are in the need of continuous state assessment during their operational life cycle.

  8. Visual Behaviour Based Bio-Inspired Polarization Techniques in Computer Vision and Robotics

    OpenAIRE

    Shabayek, Abd El Rahman; Morel, Olivier; Fofi, David

    2012-01-01

    For long time, it was thought that the sensing of polarization by animals is invariably related to their behavior, such as navigation and orientation. Recently, it was found that polarization can be part of a high-level visual perception, permitting a wide area of vision applications. Polarization vision can be used for most tasks of color vision including object recognition, contrast enhancement, camouflage breaking, and signal detection and discrimination. The polarization based visual beha...

  9. Statistical and Machine-Learning Data Mining Techniques for Better Predictive Modeling and Analysis of Big Data

    CERN Document Server

    Ratner, Bruce

    2011-01-01

    The second edition of a bestseller, Statistical and Machine-Learning Data Mining: Techniques for Better Predictive Modeling and Analysis of Big Data is still the only book, to date, to distinguish between statistical data mining and machine-learning data mining. The first edition, titled Statistical Modeling and Analysis for Database Marketing: Effective Techniques for Mining Big Data, contained 17 chapters of innovative and practical statistical data mining techniques. In this second edition, renamed to reflect the increased coverage of machine-learning data mining techniques, the author has

  10. Retrieval of Similar Objects in Simulation Data Using Machine Learning Techniques

    Energy Technology Data Exchange (ETDEWEB)

    Cantu-Paz, E; Cheung, S-C; Kamath, C

    2003-06-19

    Comparing the output of a physics simulation with an experiment is often done by visually comparing the two outputs. In order to determine which simulation is a closer match to the experiment, more quantitative measures are needed. This paper describes our early experiences with this problem by considering the slightly simpler problem of finding objects in a image that are similar to a given query object. Focusing on a dataset from a fluid mixing problem, we report on our experiments using classification techniques from machine learning to retrieve the objects of interest in the simulation data. The early results reported in this paper suggest that machine learning techniques can retrieve more objects that are similar to the query than distance-based similarity methods.

  11. Hybrid Machine Learning Technique for Forecasting Dhaka Stock Market Timing Decisions

    OpenAIRE

    Shipra Banik; Khodadad Khan, A. F. M.; Mohammad Anwer

    2014-01-01

    Forecasting stock market has been a difficult job for applied researchers owing to nature of facts which is very noisy and time varying. However, this hypothesis has been featured by several empirical experiential studies and a number of researchers have efficiently applied machine learning techniques to forecast stock market. This paper studied stock prediction for the use of investors. It is always true that investors typically obtain loss because of uncertain investment purposes and unsigh...

  12. An Empirical Study of Machine Learning Techniques for Classifying Emotional States from EEG Data

    OpenAIRE

    2012-01-01

    With the great advancement in robot technology, smart human-robot interaction is considered to be the most wanted success by the researchers these days. If a robot can identify emotions and intentions of a human interacting with it, that would make robots more useful. Electroencephalography (EEG) is considered one effective way of recording emotions and motivations of a human using brain. Various machine learning techniques are used successfully to classify EEG data accurately. K-Nearest Neig...

  13. Using Adaptive Tools and Techniques To Teach a Class of Students Who Are Blind or Low-Vision

    Science.gov (United States)

    Supalo, Cary A.; Mallouk, Thomas E.; Lanouette, James; Amorosi, Christeallia; Wohlers, H. David; McEnnis, Kathleen

    2009-05-01

    A brief overview of the 2007 National Federation of the Blind-Jernigan Institute Youth Slam Chemistry Track, a course of study within a science camp that provided firsthand experimental experience to 200 students who are blind and low-vision, is given. For many of these students, this was their first hands-on experience with laboratory chemistry. Several new blind and low vision-accessible laboratory technologies were successfully debuted. These tools and techniques bring a greater degree of freedom and independence to students with visual impairments in their science classes. Modifications of standard chemistry experiments that incorporated these new tools are described.

  14. CORROSION DETECTION USING A.I. : A COMPARISON OF STANDARD COMPUTER VISION TECHNIQUES AND DEEP LEARNING MODEL

    Directory of Open Access Journals (Sweden)

    Luca Petricca

    2016-05-01

    Full Text Available In this paper we present a comparison between standard computer vision techniques and Deep Learning approach for automatic metal corrosion (rust detection. For the classic approach, a classification based on the number of pixels containing specific red components has been utilized. The code written in Python used OpenCV libraries to compute and categorize the images. For the Deep Learning approach, we chose Caffe, a powerful framework developed at “Berkeley Vision and Learning Center” (BVLC. The test has been performed by classifying images and calculating the total accuracy for the two different approaches.

  15. Study on a New Technique of On-line Monitoring of Oil Contamination Level Using Computer Vision Technology

    Institute of Scientific and Technical Information of China (English)

    TU Qun-zhang; ZUO Hong-fu

    2004-01-01

    In this paper,a new technique of capturing the images of debris in lubrication or hydraulic oil using micro-imaging and computer vision techniques is introduced.By way of image processing,the size and distribution of debris are obtained,and then the oil contamination level is also obtained.Because the information of oil contamination is obtained directly from the images of debris by this method,the monitoring result is more intuitive and reliable.

  16. Technique of Substantiating Requirements for the Vision Systems of Industrial Robotic Complexes

    Directory of Open Access Journals (Sweden)

    V. Ya. Kolyuchkin

    2015-01-01

    Full Text Available In references, there is a lack of approaches to describe the justified technical requirements for the vision systems (VS of industrial robotics complexes (IRC. Therefore, an objective of the work is to develop a technique that allows substantiating requirements for the main quality indicators of VS, functioning as a part of the IRC.The proposed technique uses a model representation of VS, which, as a part of the IRC information system, sorts the objects in the work area, as well as measures their linear and angular coordinates. To solve the problem of statement there is a proposal to define the target function of a designed IRC as a dependence of the IRC indicator efficiency on the VS quality indicators. The paper proposes to use, as an indicator of the IRC efficiency, the probability of a lack of fault products when manufacturing. Based on the functions the VS perform as a part of the IRC information system, the accepted indicators of VS quality are as follows: a probability of the proper recognition of objects in the working IRC area, and confidential probabilities of measuring linear and angular orientation coordinates of objects with the specified values of permissible error. Specific values of these errors depend on the orientation errors of working bodies of manipulators that are a part of the IRC. The paper presents mathematical expressions that determine the functional dependence of the probability of a lack of fault products when manufacturing on the VS quality indicators and the probability of failures of IRC technological equipment.The offered technique for substantiating engineering requirements for the VS of IRC has novelty. The results obtained in this work can be useful for professionals involved in IRC VS development, and, in particular, in development of VS algorithms and software.

  17. 机器视觉技术在烟箱缺条检测中的应用%Application of Machine Vision Technology in Detection of Lacking Carton of Cigarettes during Case-packing

    Institute of Scientific and Technical Information of China (English)

    董鸿江; 赵日峰

    2011-01-01

    The auto case-packing plays an important role in the production of cigarettes. The lacking carton of cigarettes in the case-packing process may result in line or row vacancy, which will cause reputation lapse and serious quality problems. At present, the mature methods, including eddy current, weighing and radiographic detection, all have their own shortcomings. The machine vision detection technique is widely used in the modern cigarette production due to its superiorities, such as low cost, mature technique and strong applicability. The machine vision technique was used in detecting the lacking carton in the case-packing process of cigarettes, which have positive and important significance in removing the hided quality problems, maintaining and increasing the the enterprise reputation. Based on the existing machines and techniques, the system for detecting the lacking carton in the case-packing process of cigarettes was successfully developed and its reliability was also tested in practice.%在卷烟生产过程中,自动装箱是一道重要的工序,在装箱过程中可能产生漏装导致缺条、缺排现象,造成严重的质量问题.目前比较成熟的检测方法有涡流、称重和射线检测等,但都存在不同程度的缺陷.机器视觉检测技术在现代卷烟生产过程中具有日益广泛的应用,其具有费用低、检测技术成熟、适用性强等优点.在此将机器视觉技术应用到装箱机缺条检测中,对于消除产品质量隐患、提高生产可靠性和维护企业信誉具有十分重要的意义.依据现有的生产机械设备和技术手段,成功开发了卷烟生产过程中的烟箱缺条检测系统,并在生产实践中检验了其可靠性.

  18. Auditory inspired machine learning techniques can improve speech intelligibility and quality for hearing-impaired listeners.

    Science.gov (United States)

    Monaghan, Jessica J M; Goehring, Tobias; Yang, Xin; Bolner, Federico; Wang, Shangqiguo; Wright, Matthew C M; Bleeck, Stefan

    2017-03-01

    Machine-learning based approaches to speech enhancement have recently shown great promise for improving speech intelligibility for hearing-impaired listeners. Here, the performance of three machine-learning algorithms and one classical algorithm, Wiener filtering, was compared. Two algorithms based on neural networks were examined, one using a previously reported feature set and one using a feature set derived from an auditory model. The third machine-learning approach was a dictionary-based sparse-coding algorithm. Speech intelligibility and quality scores were obtained for participants with mild-to-moderate hearing impairments listening to sentences in speech-shaped noise and multi-talker babble following processing with the algorithms. Intelligibility and quality scores were significantly improved by each of the three machine-learning approaches, but not by the classical approach. The largest improvements for both speech intelligibility and quality were found by implementing a neural network using the feature set based on auditory modeling. Furthermore, neural network based techniques appeared more promising than dictionary-based, sparse coding in terms of performance and ease of implementation.

  19. 一种基于机器视觉的跑偏角估计算法%An algorithm of machine vision-based yaw-angle estimation

    Institute of Scientific and Technical Information of China (English)

    魏丽; 田克纯

    2011-01-01

    在移动机器人、汽车等自主行进过程中,由于受到道路情况等因素的影响,极易出现方向跑偏,导致其无法按照预定路径前进.为了防止出现跑偏等危险情况,研究了利用机器视觉估计预定运动方向和实际运动方向夹角即跑偏角的方法.介绍了利用机器视觉测量跑偏角的基本原理;研究了基于特征点跟踪的跑偏角估计算法.实验研究表明:该方法可以有效地在行进过程中计算跑偏角.%In the process of robot and automobile autonomous moving, vehicle cannot move along predestinate path due to road status and other factors. An algorithm of machine vision-based yaw angle computation for avoiding vehicle in danger is presented. The system constitutes of machine vision-based yaw angle computation is introduced. Algorithm of yaw angle computation which is built on feature points tracking is studied. Experimental results show that this method can compute yaw-angle in the process of vehicle moving effectively.

  20. Gradual Reduction in Sodium Content in Cooked Ham, with Corresponding Change in Sensorial Properties Measured by Sensory Evaluation and a Multimodal Machine Vision System.

    Science.gov (United States)

    Greiff, Kirsti; Mathiassen, John Reidar; Misimi, Ekrem; Hersleth, Margrethe; Aursand, Ida G

    2015-01-01

    The European diet today generally contains too much sodium (Na(+)). A partial substitution of NaCl by KCl has shown to be a promising method for reducing sodium content. The aim of this work was to investigate the sensorial changes of cooked ham with reduced sodium content. Traditional sensorial evaluation and objective multimodal machine vision were used. The salt content in the hams was decreased from 3.4% to 1.4%, and 25% of the Na(+) was replaced by K(+). The salt reduction had highest influence on the sensory attributes salty taste, after taste, tenderness, hardness and color hue. The multimodal machine vision system showed changes in lightness, as a function of reduced salt content. Compared to the reference ham (3.4% salt), a replacement of Na(+)-ions by K(+)-ions of 25% gave no significant changes in WHC, moisture, pH, expressed moisture, the sensory profile attributes or the surface lightness and shininess. A further reduction of salt down to 1.7-1.4% salt, led to a decrease in WHC and an increase in expressible moisture.

  1. Gradual Reduction in Sodium Content in Cooked Ham, with Corresponding Change in Sensorial Properties Measured by Sensory Evaluation and a Multimodal Machine Vision System

    Science.gov (United States)

    Greiff, Kirsti; Mathiassen, John Reidar; Misimi, Ekrem; Hersleth, Margrethe; Aursand, Ida G.

    2015-01-01

    The European diet today generally contains too much sodium (Na+). A partial substitution of NaCl by KCl has shown to be a promising method for reducing sodium content. The aim of this work was to investigate the sensorial changes of cooked ham with reduced sodium content. Traditional sensorial evaluation and objective multimodal machine vision were used. The salt content in the hams was decreased from 3.4% to 1.4%, and 25% of the Na+ was replaced by K+. The salt reduction had highest influence on the sensory attributes salty taste, after taste, tenderness, hardness and color hue. The multimodal machine vision system showed changes in lightness, as a function of reduced salt content. Compared to the reference ham (3.4% salt), a replacement of Na+-ions by K+-ions of 25% gave no significant changes in WHC, moisture, pH, expressed moisture, the sensory profile attributes or the surface lightness and shininess. A further reduction of salt down to 1.7–1.4% salt, led to a decrease in WHC and an increase in expressible moisture. PMID:26422367

  2. New Technique of High-Performance Torque Control Developed for Induction Machines

    Science.gov (United States)

    Kenny, Barbara H.

    2003-01-01

    Two forms of high-performance torque control for motor drives have been described in the literature: field orientation control and direct torque control. Field orientation control has been the method of choice for previous NASA electromechanical actuator research efforts with induction motors. Direct torque control has the potential to offer some advantages over field orientation, including ease of implementation and faster response. However, the most common form of direct torque control is not suitable for the highspeed, low-stator-flux linkage induction machines designed for electromechanical actuators with the presently available sample rates of digital control systems (higher sample rates are required). In addition, this form of direct torque control is not suitable for the addition of a high-frequency carrier signal necessary for the "self-sensing" (sensorless) position estimation technique. This technique enables low- and zero-speed position sensorless operation of the machine. Sensorless operation is desirable to reduce the number of necessary feedback signals and transducers, thus improving the reliability and reducing the mass and volume of the system. This research was directed at developing an alternative form of direct torque control known as a "deadbeat," or inverse model, solution. This form uses pulse-width modulation of the voltage applied to the machine, thus reducing the necessary sample and switching frequency for the high-speed NASA motor. In addition, the structure of the deadbeat form allows the addition of the high-frequency carrier signal so that low- and zero-speed sensorless operation is possible. The new deadbeat solution is based on using the stator and rotor flux as state variables. This choice of state variables leads to a simple graphical representation of the solution as the intersection of a constant torque line with a constant stator flux circle. Previous solutions have been expressed only in complex mathematical terms without a

  3. Classification of Cytochrome P450 1A2 Inhibitors and Non-Inhibitors by Machine Learning Techniques

    DEFF Research Database (Denmark)

    Vasanthanathan, Poongavanam; Taboureau, Olivier; Oostenbrink, Chris

    2009-01-01

    of CYP1A2 inhibitors and non-inhibitors. Training and test sets consisted of about 400 and 7000 compounds, respectively. Various machine learning techniques, like binary QSAR, support vector machine (SVM), random forest, kappa nearest neighbors (kNN), and decision tree methods were used to develop...

  4. Machine learning techniques for astrophysical modelling and photometric redshift estimation of quasars in optical sky surveys

    CERN Document Server

    Kumar, N Daniel

    2008-01-01

    Machine learning techniques are utilised in several areas of astrophysical research today. This dissertation addresses the application of ML techniques to two classes of problems in astrophysics, namely, the analysis of individual astronomical phenomena over time and the automated, simultaneous analysis of thousands of objects in large optical sky surveys. Specifically investigated are (1) techniques to approximate the precise orbits of the satellites of Jupiter and Saturn given Earth-based observations as well as (2) techniques to quickly estimate the distances of quasars observed in the Sloan Digital Sky Survey. Learning methods considered include genetic algorithms, particle swarm optimisation, artificial neural networks, and radial basis function networks. The first part of this dissertation demonstrates that GAs and PSOs can both be efficiently used to model functions that are highly non-linear in several dimensions. It is subsequently demonstrated in the second part that ANNs and RBFNs can be used as ef...

  5. A Model of an Expert Computer Vision and Recognition Facility with Applications of a Proportion Technique.

    Science.gov (United States)

    2014-09-26

    of research is being 14 function called WHATISFACE. [Rhodes][Tucker][ Hogg ][Sowa] The model offering the most specific information about structure and...1983. Hogg , D., "Model-based vision: a program to see a walking person", from "Image and Vision Computing", Vol. 1, No. 1, February 1983, pp. 5-20...Systems", Addison-Wesley Publishing Company, Inc., Massachusetts, 1983. Hogg , D., "Model-based vision: a program to see a walking person", from "Image

  6. Developing Fire Detection Algorithms by Geostationary Orbiting Platforms and Machine Learning Techniques

    Science.gov (United States)

    Salvador, Pablo; Sanz, Julia; Garcia, Miguel; Casanova, Jose Luis

    2016-08-01

    Fires in general and forest fires specific are a major concern in terms of economical and biological loses. Remote sensing technologies have been focusing on developing several algorithms, adapted to a large kind of sensors, platforms and regions in order to obtain hotspots as faster as possible. The aim of this study is to establish an automatic methodology to develop hotspots detection algorithms with Spinning Enhanced Visible and Infrared Imager (SEVIRI) sensor on board Meteosat Second Generation platform (MSG) based on machine learning techniques that can be exportable to others geostationary platforms and sensors and to any area of the Earth. The sensitivity (SE), specificity (SP) and accuracy (AC) parameters have been analyzed in order to develop the final machine learning algorithm taking into account the preferences and final use of the predicted data.

  7. The influence of cooling techniques on cutting forces and surface roughness during cryogenic machining of titanium alloys

    Science.gov (United States)

    Wstawska, Iwona; Ślimak, Krzysztof

    2016-12-01

    Titanium alloys are one of the materials extensively used in the aerospace industry due to its excellent properties of high specific strength and corrosion resistance. On the other hand, they also present problems wherein titanium alloys are extremely difficult materials to machine. In addition, the cost associated with titanium machining is also high due to lower cutting velocities and shorter tool life. The main objective of this work is a comparison of different cooling techniques during cryogenic machining of titanium alloys. The analysis revealed that applied cooling technique has a significant influence on cutting force and surface roughness (Ra parameter) values. Furthermore, in all cases observed a positive influence of cryogenic machining on selected aspects after turning and milling of titanium alloys. This work can be also the starting point to the further research, related to the analysis of cutting forces and surface roughness during cryogenic machining of titanium alloys.

  8. Reservation Resource Technique for Virtual Machine Placement in Cloud Data Centre

    Directory of Open Access Journals (Sweden)

    Ajith Singh. N

    2014-04-01

    Full Text Available Migrations of Virtual Machine directly influence on energy consumption and QoS, to avoid migration of virtual machine when a host is overloaded a good placement technique need to be applied. Virtual Machine Placement is vital in cloud computing to utilize the resources in an efficient manner. Migration of a VM instance when a host is overloaded is familiar in cloud computing. VM selection policy finds a suitable VM to migrate from overloaded host and place to an under loaded host or turn on a new host. While migration there is small downtime of the service, even thou down time is small there is a huge change in energy consumption. Energy consumption in data centre has lead to emission of carbon dioxide to the environment. Frequent VM migration may cause the services to high latency in the network and may disturb the network environment. These works focus to reduce the VM migration, improve SLA and energy consumption. Therefore, a reservation method known as RTBBE (RTBBE (Reservation Technique Bin BECK Entropy proposed in the study that is by allocating and assigning double upper threshold with entropy method with new overload detection PR (Polynomial Regression and a VM selection policy MUR (Minimum Utilization Rank had proposed in this study. The result shows that the proposed technique reduces the energy consumption, SLA and VM migration. Experimental shows that the proposed method reduce the energy up to 21.30 kWh when the overload detection PR combines with MUR, SLA of 0.00029% with IQR with MUR and 775 VM were migrated with LRR and MC.

  9. Clustering technique-based least square support vector machine for EEG signal classification.

    Science.gov (United States)

    Siuly; Li, Yan; Wen, Peng Paul

    2011-12-01

    This paper presents a new approach called clustering technique-based least square support vector machine (CT-LS-SVM) for the classification of EEG signals. Decision making is performed in two stages. In the first stage, clustering technique (CT) has been used to extract representative features of EEG data. In the second stage, least square support vector machine (LS-SVM) is applied to the extracted features to classify two-class EEG signals. To demonstrate the effectiveness of the proposed method, several experiments have been conducted on three publicly available benchmark databases, one for epileptic EEG data, one for mental imagery tasks EEG data and another one for motor imagery EEG data. Our proposed approach achieves an average sensitivity, specificity and classification accuracy of 94.92%, 93.44% and 94.18%, respectively, for the epileptic EEG data; 83.98%, 84.37% and 84.17% respectively, for the motor imagery EEG data; and 64.61%, 58.77% and 61.69%, respectively, for the mental imagery tasks EEG data. The performance of the CT-LS-SVM algorithm is compared in terms of classification accuracy and execution (running) time with our previous study where simple random sampling with a least square support vector machine (SRS-LS-SVM) was employed for EEG signal classification. We also compare the proposed method with other existing methods in the literature for the three databases. The experimental results show that the proposed algorithm can produce a better classification rate than the previous reported methods and takes much less execution time compared to the SRS-LS-SVM technique. The research findings in this paper indicate that the proposed approach is very efficient for classification of two-class EEG signals.

  10. OVERVIEW OF WORK PIECE TEMPERATURE MEASUREMENT TECHNIQUES FOR MACHINING OF Ti6Al4V#

    Directory of Open Access Journals (Sweden)

    P.J.T. Conradie

    2012-01-01

    Full Text Available

    ENGLISH ABSTRACT: Ti6Al4V is one of the most widely used titanium alloys in aerospace applications, but its machining remains a challenge. Comprehensive research has been done in the past, mainly investigating tool failure of various materials. Less research has been done to investigate the thermal effect of machining on work piece quality, including fatigue performance. Temperature measurement is considered to be a key enabling technology. This study presents an overview of current temperature measurement techniques for machined and tool surfaces. Two categories of methods were investigated: slower contact, and faster optical methods. Optical fibre two colour pyrometry experiments are reported that demonstrate the technique’s adequate response time. The infrared camera temperature measurement experiments synchronised temperature measurement with visual observation, aimed at mechanism analysis. The results corresponded with the literature.

    AFRIKAANSE OPSOMMING: Ti6Al4V is een van die mees gewilde lugvaart allooie, maar sy masjinering is ’n uitdaging. Bestaande navorsing dek beitelslytasie omvattend. Die termiese effek van masjinering op werkstuk integriteit, insluitend vermoeiingleeftyd, het egter veel minder dekking geniet. Temperatuurmeting wat in hierdie studie ondersoek word, word as ’n sleuteltegnologie beskou. Twee kategorië metodes is ondersoek, nl stadige kontakmetodes en optiese metodes met vinnige respons, wat die meting van oorgangsverskynsels moontlik maak. Eksperimentele werk wat beide optiese vesel tweekleurpirometrie en termiese kamera tegnieke insluit bewys die tegnieke as geskik vir die benodigde navorsing.

  11. Digital Mayhem 3D machine techniques where inspiration, techniques and digital art meet

    CERN Document Server

    Evans, Duncan

    2014-01-01

    From Icy Tundras to Desert savannahs, master the art of landscape and environment design for 2D and 3D digital content. Make it rain, shower your digital scene with a snow storm or develop a believable urban scene with a critical eye for modeling, lighting and composition. Move beyond the limitations of gallery style coffee table books with Digital Mayhem: 3D Landscapes-offering leading professional techniques, groundbreaking inspiration, and artistic mastery from some of the greatest digital artists. More than just a gallery book - each artist has written a breakdown overview, with supporting

  12. Detection of citrus canker and Huanglongbing using fluorescence imaging spectroscopy and support vector machine technique.

    Science.gov (United States)

    Wetterich, Caio Bruno; Felipe de Oliveira Neves, Ruan; Belasque, José; Marcassa, Luis Gustavo

    2016-01-10

    Citrus canker and Huanglongbing (HLB) are citrus diseases that represent a serious threat to the citrus production worldwide and may cause large economic losses. In this work, we combined fluorescence imaging spectroscopy (FIS) and a machine learning technique to discriminate between these diseases and other ordinary citrus conditions that may be present at citrus orchards, such as citrus scab and zinc deficiency. Our classification results are highly accurate when discriminating citrus canker from citrus scab (97.8%), and HLB from zinc deficiency (95%). These results show that it is possible to accurately identify citrus diseases that present similar symptoms.

  13. 基于机器视觉维氏硬度检测技术研究%Research on Technigues of Vickers Hardness Test Based on Machine Vision

    Institute of Scientific and Technical Information of China (English)

    韦喆; 张绍荣

    2015-01-01

    Vickers hardness test has advantages of high precision and wide application. But it has great manual operation error and limitation. The Vickers hardness test platform is set up base on machine vision method in this paper. Through acquiring and processing the images in dentation, hardness value is obtained by calculating. Its feasibility is verified through experiment.%维氏硬度测试是一种精度高、应用范围广的硬度测量方法,但人工操作测量误差高、局限性大。本研究基于机器视觉的方法,搭建维氏硬度测试平台,通过采集压痕图像并对其进行处理、运算得出被测物硬度值。实验验证了其可行性。

  14. Computer vision and color measurement techniques for inline monitoring of cheese curd syneresis.

    Science.gov (United States)

    Everard, C D; O'Callaghan, D J; Fagan, C C; O'Donnell, C P; Castillo, M; Payne, F A

    2007-07-01

    Optical characteristics of stirred curd were simultaneously monitored during syneresis in a 10-L cheese vat using computer vision and colorimetric measurements. Curd syneresis kinetic conditions were varied using 2 levels of milk pH (6.0 and 6.5) and 2 agitation speeds (12.1 and 27.2 rpm). Measured optical parameters were compared with gravimetric measurements of syneresis, taken simultaneously. The results showed that computer vision and colorimeter measurements have potential for monitoring syneresis. The 2 different phases, curd and whey, were distinguished by means of color differences. As syneresis progressed, the backscattered light became increasingly yellow in hue for circa 20 min for the higher stirring speed and circa 30 min for the lower stirring speed. Syneresis-related gravimetric measurements of importance to cheese making (e.g., curd moisture content, total solids in whey, and yield of whey) correlated significantly with computer vision and colorimetric measurements.

  15. Real-time drogue recognition and 3D locating for UAV autonomous aerial refueling based on monocular machine vision

    Institute of Scientific and Technical Information of China (English)

    Wang Xufeng; Kong Xingwei; Zhi Jianhui; Chen Yong; Dong Xinmin

    2015-01-01

    Drogue recognition and 3D locating is a key problem during the docking phase of the autonomous aerial refueling (AAR). To solve this problem, a novel and effective method based on monocular vision is presented in this paper. Firstly, by employing computer vision with red-ring-shape feature, a drogue detection and recognition algorithm is proposed to guarantee safety and ensure the robustness to the drogue diversity and the changes in environmental condi-tions, without using a set of infrared light emitting diodes (LEDs) on the parachute part of the dro-gue. Secondly, considering camera lens distortion, a monocular vision measurement algorithm for drogue 3D locating is designed to ensure the accuracy and real-time performance of the system, with the drogue attitude provided. Finally, experiments are conducted to demonstrate the effective-ness of the proposed method. Experimental results show the performances of the entire system in contrast with other methods, which validates that the proposed method can recognize and locate the drogue three dimensionally, rapidly and precisely.

  16. Rehabilitation of patients with motor disabilities using computer vision based techniques

    Directory of Open Access Journals (Sweden)

    Alejandro Reyes-Amaro

    2012-05-01

    Full Text Available In this paper we present details about the implementation of computer vision based applications for the rehabilitation of patients with motor disabilities. The applications are conceived as serious games, where the computer-patient interaction during playing contributes to the development of different motor skills. The use of computer vision methods allows the automatic guidance of the patient’s movements making constant specialized supervision unnecessary. The hardware requirements are limited to low-cost devices like usual webcams and Netbooks.

  17. GPR Signal Characterization for Automated Landmine and UXO Detection Based on Machine Learning Techniques

    Directory of Open Access Journals (Sweden)

    Xavier Núñez-Nieto

    2014-10-01

    Full Text Available Landmine clearance is an ongoing problem that currently affects millions of people around the world. This study evaluates the effectiveness of ground penetrating radar (GPR in demining and unexploded ordnance detection using 2.3-GHz and 1-GHz high-frequency antennas. An automated detection tool based on machine learning techniques is also presented with the aim of automatically detecting underground explosive artifacts. A GPR survey was conducted on a designed scenario that included the most commonly buried items in historic battle fields, such as mines, projectiles and mortar grenades. The buried targets were identified using both frequencies, although the higher vertical resolution provided by the 2.3-GHz antenna allowed for better recognition of the reflection patterns. The targets were also detected automatically using machine learning techniques. Neural networks and logistic regression algorithms were shown to be able to discriminate between potential targets and clutter. The neural network had the most success, with accuracies ranging from 89% to 92% for the 1-GHz and 2.3-GHz antennas, respectively.

  18. A First Look at creating mock catalogs with machine learning techniques

    CERN Document Server

    Xu, Xiaoying; Trac, Hy; Schneider, Jeff; Poczos, Barnabas; Ntampaka, Michelle

    2013-01-01

    We investigate machine learning (ML) techniques for predicting the number of galaxies (N_gal) that occupy a halo, given the halo's properties. These types of mappings are crucial for constructing the mock galaxy catalogs necessary for analyses of large-scale structure. The ML techniques proposed here distinguish themselves from traditional halo occupation distribution (HOD) modeling as they do not assume a prescribed relationship between halo properties and N_gal. In addition, our ML approaches are only dependent on parent halo properties (like HOD methods), which are advantageous over subhalo-based approaches as identifying subhalos correctly is difficult. We test 2 algorithms: support vector machines (SVM) and k-nearest-neighbour (kNN) regression. We take galaxies and halos from the Millennium simulation and predict N_gal by training our algorithms on the following 6 halo properties: number of particles, M_200, \\sigma_v, v_max, half-mass radius and spin. For Millennium, our predicted N_gal values have a mea...

  19. Acronym Disambiguation in Spanish Electronic Health Narratives Using Machine Learning Techniques.

    Science.gov (United States)

    Rubio-López, Ignacio; Costumero, Roberto; Ambit, Héctor; Gonzalo-Martín, Consuelo; Menasalvas, Ernestina; Rodríguez González, Alejandro

    2017-01-01

    Electronic Health Records (EHRs) are now being massively used in hospitals what has motivated current developments of new methods to process clinical narratives (unstructured data) making it possible to perform context-based searches. Current approaches to process the unstructured texts in EHRs are based in applying text mining or natural language processing (NLP) techniques over the data. In particular Named Entity Recognition (NER) is of paramount importance to retrieve specific biomedical concepts from the text providing the semantic type of the concept retrieved. However, it is very common that clinical notes contain lots of acronyms that cannot be identified by NER processes and even if they are identified, an acronym may correspond to several meanings, so disambiguation of the found term is needed. In this work we provide an approach to perform acronym disambiguation in Spanish EHR using machine learning techniques.

  20. Advanced design technique of human-machine interfaces for PLC control of complex systems

    Directory of Open Access Journals (Sweden)

    Árpád-István Sütő

    2008-05-01

    Full Text Available Touchscreen operator panels proved to be a convenient succesor for clasical operator panels for implementing human-machine interfaces (HMIs in programmable logic controllers (PLC systems. The paper introduces a new technique for HMIs design in such systems, based on the idea of touchscreens replication. This redundancy allow actions which are not possible within the menus and sub-menus of a single touchscreen. Its strenght is revealed especially in complex systems, where operators can easily be overwhelmed by the huge amount of process information. The technique was applied on a mill tube rolling installation. The results also proved an increase of system security and zero downtime for HMI maintenance activities.

  1. Vision Alignment System of Solar Cell Screen Printing Machine%太阳能硅片丝印机视觉定位系统

    Institute of Scientific and Technical Information of China (English)

    魏海滨; 朱跃红; 郎鹏; 郑海红; 张素枝

    2012-01-01

    Solar Cell Screen Printing Machine is the key equipment in solar cell production industry. How to improve the precision, quality and efficiency of screen printing is very important, the alignment is one of the key technologies. Introduce the typical application of precision alighnment based on computer vision in the solar cell screen printing, and illustrate the basic components, design principle and hardware selection of vision precision alignment system, give out the motional relationship between CCD optical system and UVWworktable and the alionment orocess.%太阳能硅片丝印机是太阳能电池生产行业中的关键设备,如何提高丝印精度、印刷质量和印刷效率成为研究重点,而对位技术则是其中的关键技术之一。介绍了基于机器视觉的精密对位技术在太阳能硅片丝印机中的典型应用,阐述了视觉精密定位系统的基本组成、设计原理及硬件选型,给出了CCD光学系统与UVW工作平台坐标系之间的各运动关系和对位过程。

  2. High-speed Robot Auto-sorting System Based on Machine Vision%高速机器人分拣系统机器视觉技术的研究

    Institute of Scientific and Technical Information of China (English)

    晏祖根; 李明; 徐克非; 孙小华; 闫志鹏; 孙智慧

    2014-01-01

    针对我国食品生产行业的实际需求,基于并联机器人、机器视觉等先进技术,构建了面向食品生产包装的高速机器人分拣系统,研究了输送带上运动食品的机器视觉定位算法,设计了运动食品分级与定位的机器视觉硬件系统,基于专业图像处理软件Sherlock,研发了自动分拣机器视觉软件系统,以提高我国食品生产效率、保证食品卫生、降低劳动强度。%In view of the actual demand of the food industry in China,the high-speed parallel robot auto-sorting system is constructed.The machine vision positioning algorithm of the moving food on the conveyor belt is studied.The machine vision hardware system for food sorting and positioning is designed.Based on second-ary development for Sherlock in VC programming environment,the machine vision software system is devel-oped.By applied the machine vision system,it can improve the production efficiency and reduce the labor’s intensity in our food and packaging industry.

  3. Optimization of process parameters on EN24 Tool steel using Taguchi technique in Electro-Discharge Machining (EDM)

    Science.gov (United States)

    Jeykrishnan, J.; Vijaya Ramnath, B.; Akilesh, S.; Pradeep Kumar, R. P.

    2016-09-01

    In the field of manufacturing sectors, electric discharge machining (EDM) is widely used because of its unique machining characteristics and high meticulousness which can't be done by other traditional machines. The purpose of this paper is to analyse the optimum machining parameter, to curtail the machining time with respect to high material removal rate (MRR) and low tool wear rate (TWR) by varying the parameters like current, pulse on time (Ton) and pulse off time (Toff). By conducting several dry runs using Taguchi technique of L9 orthogonal array (OA), optimized parameters were found using analysis of variance (ANOVA) and the error percentage can be validated and parameter contribution for MRR and TWR were found.

  4. Operational modal analysis on a VAWT in a large wind tunnel using stereo vision technique

    DEFF Research Database (Denmark)

    Najafi, Nadia; Schmidt Paulsen, Uwe

    2017-01-01

    This paper is about development and use of a research based stereo vision system for vibration and operational modal analysis on a parked, 1-kW, 3-bladed vertical axis wind turbine (VAWT), tested in a wind tunnel at high wind. Vibrations were explored experimentally by tracking small deflections ...... in picking very closely spaced modes. Finally, the uncertainty of the 3D displacement measurement was evaluated by applying a generalized method based on the law of error propagation, for a linear camera model of the stereo vision system.......This paper is about development and use of a research based stereo vision system for vibration and operational modal analysis on a parked, 1-kW, 3-bladed vertical axis wind turbine (VAWT), tested in a wind tunnel at high wind. Vibrations were explored experimentally by tracking small deflections...... of the markers on the structure with two cameras, and also numerically, to study structural vibrations in an overall objective to investigate challenges and to prove the capability of using stereo vision. Two high speed cameras provided displacement measurements at no wind speed interference. The displacement...

  5. Evaluating machine learning and statistical prediction techniques for landslide susceptibility modeling

    Science.gov (United States)

    Goetz, J. N.; Brenning, A.; Petschko, H.; Leopold, P.

    2015-08-01

    Statistical and now machine learning prediction methods have been gaining popularity in the field of landslide susceptibility modeling. Particularly, these data driven approaches show promise when tackling the challenge of mapping landslide prone areas for large regions, which may not have sufficient geotechnical data to conduct physically-based methods. Currently, there is no best method for empirical susceptibility modeling. Therefore, this study presents a comparison of traditional statistical and novel machine learning models applied for regional scale landslide susceptibility modeling. These methods were evaluated by spatial k-fold cross-validation estimation of the predictive performance, assessment of variable importance for gaining insights into model behavior and by the appearance of the prediction (i.e. susceptibility) map. The modeling techniques applied were logistic regression (GLM), generalized additive models (GAM), weights of evidence (WOE), the support vector machine (SVM), random forest classification (RF), and bootstrap aggregated classification trees (bundling) with penalized discriminant analysis (BPLDA). These modeling methods were tested for three areas in the province of Lower Austria, Austria. The areas are characterized by different geological and morphological settings. Random forest and bundling classification techniques had the overall best predictive performances. However, the performances of all modeling techniques were for the majority not significantly different from each other; depending on the areas of interest, the overall median estimated area under the receiver operating characteristic curve (AUROC) differences ranged from 2.9 to 8.9 percentage points. The overall median estimated true positive rate (TPR) measured at a 10% false positive rate (FPR) differences ranged from 11 to 15pp. The relative importance of each predictor was generally different between the modeling methods. However, slope angle, surface roughness and plan

  6. Survey on Various Gesture Recognition Techniques for Interfacing Machines Based on Ambient Intelligence

    Directory of Open Access Journals (Sweden)

    Harshith.C

    2010-12-01

    Full Text Available Gesture recognition is mainly apprehensive on analyzing the functionality of human wits. The main goal of gesture recognition is to create a system which can recognize specific human gestures and use them to convey information or for device control. Hand gestures provide a separate complementary modality to speech for expressing ones ideas. Information associated with hand gestures in a conversation is degree, discourse structure, spatial and temporal structure. The approaches present can be mainly divided intoData-Glove Based and Vision Based approaches. An important face feature point is the nose tip. Since nose is the highest protruding point from the face. Besides that, it is not affected by facial expressions. Another important function of the nose is that it is able to indicate the head pose. Knowledge of the nose location will enable us to align an unknown 3D face with those in a face database. Eye detection is divided into eye position detection and eye contour detection. Existing works in eye detection can be classified into two major categories: traditional image-based passive approaches and the active IR based approaches. The former uses intensity and shape of eyes for detection and the latter works on the assumption that eyes have a reflection under near IR illumination and produce bright/dark pupileffect. The traditional methods can be broadly classified into three categories: template based methods, appearance based methods and feature based methods. The purpose of this paper is to compare various human Gesture recognition systems for interfacing machines directly to human wits without any corporeal media in an ambient environment.

  7. A FIRST LOOK AT CREATING MOCK CATALOGS WITH MACHINE LEARNING TECHNIQUES

    Energy Technology Data Exchange (ETDEWEB)

    Xu Xiaoying; Ho, Shirley; Trac, Hy; Schneider, Jeff; Ntampaka, Michelle [McWilliams Center for Cosmology, Department of Physics, Carnegie Mellon University, 5000 Forbes Avenue, Pittsburgh, PA 15213 (United States); Poczos, Barnabas [School of Computer Science, Carnegie Mellon University, 5000 Forbes Avenue, Pittsburgh, PA 15213 (United States)

    2013-08-01

    We investigate machine learning (ML) techniques for predicting the number of galaxies (N{sub gal}) that occupy a halo, given the halo's properties. These types of mappings are crucial for constructing the mock galaxy catalogs necessary for analyses of large-scale structure. The ML techniques proposed here distinguish themselves from traditional halo occupation distribution (HOD) modeling as they do not assume a prescribed relationship between halo properties and N{sub gal}. In addition, our ML approaches are only dependent on parent halo properties (like HOD methods), which are advantageous over subhalo-based approaches as identifying subhalos correctly is difficult. We test two algorithms: support vector machines (SVM) and k-nearest-neighbor (kNN) regression. We take galaxies and halos from the Millennium simulation and predict N{sub gal} by training our algorithms on the following six halo properties: number of particles, M{sub 200}, {sigma}{sub v}, v{sub max}, half-mass radius, and spin. For Millennium, our predicted N{sub gal} values have a mean-squared error (MSE) of {approx}0.16 for both SVM and kNN. Our predictions match the overall distribution of halos reasonably well and the galaxy correlation function at large scales to {approx}5%-10%. In addition, we demonstrate a feature selection algorithm to isolate the halo parameters that are most predictive, a useful technique for understanding the mapping between halo properties and N{sub gal}. Lastly, we investigate these ML-based approaches in making mock catalogs for different galaxy subpopulations (e.g., blue, red, high M{sub star}, low M{sub star}). Given its non-parametric nature as well as its powerful predictive and feature selection capabilities, ML offers an interesting alternative for creating mock catalogs.

  8. A First Look at Creating Mock Catalogs with Machine Learning Techniques

    Science.gov (United States)

    Xu, Xiaoying; Ho, Shirley; Trac, Hy; Schneider, Jeff; Poczos, Barnabas; Ntampaka, Michelle

    2013-08-01

    We investigate machine learning (ML) techniques for predicting the number of galaxies (N gal) that occupy a halo, given the halo's properties. These types of mappings are crucial for constructing the mock galaxy catalogs necessary for analyses of large-scale structure. The ML techniques proposed here distinguish themselves from traditional halo occupation distribution (HOD) modeling as they do not assume a prescribed relationship between halo properties and N gal. In addition, our ML approaches are only dependent on parent halo properties (like HOD methods), which are advantageous over subhalo-based approaches as identifying subhalos correctly is difficult. We test two algorithms: support vector machines (SVM) and k-nearest-neighbor (kNN) regression. We take galaxies and halos from the Millennium simulation and predict N gal by training our algorithms on the following six halo properties: number of particles, M 200, σ v , v max, half-mass radius, and spin. For Millennium, our predicted N gal values have a mean-squared error (MSE) of ~0.16 for both SVM and kNN. Our predictions match the overall distribution of halos reasonably well and the galaxy correlation function at large scales to ~5%-10%. In addition, we demonstrate a feature selection algorithm to isolate the halo parameters that are most predictive, a useful technique for understanding the mapping between halo properties and N gal. Lastly, we investigate these ML-based approaches in making mock catalogs for different galaxy subpopulations (e.g., blue, red, high M star, low M star). Given its non-parametric nature as well as its powerful predictive and feature selection capabilities, ML offers an interesting alternative for creating mock catalogs.

  9. Prediction of activity type in preschool children using machine learning techniques.

    Science.gov (United States)

    Hagenbuchner, Markus; Cliff, Dylan P; Trost, Stewart G; Van Tuc, Nguyen; Peoples, Gregory E

    2015-07-01

    Recent research has shown that machine learning techniques can accurately predict activity classes from accelerometer data in adolescents and adults. The purpose of this study is to develop and test machine learning models for predicting activity type in preschool-aged children. Participants completed 12 standardised activity trials (TV, reading, tablet game, quiet play, art, treasure hunt, cleaning up, active game, obstacle course, bicycle riding) over two laboratory visits. Eleven children aged 3-6 years (mean age=4.8±0.87; 55% girls) completed the activity trials while wearing an ActiGraph GT3X+ accelerometer on the right hip. Activities were categorised into five activity classes: sedentary activities, light activities, moderate to vigorous activities, walking, and running. A standard feed-forward Artificial Neural Network and a Deep Learning Ensemble Network were trained on features in the accelerometer data used in previous investigations (10th, 25th, 50th, 75th and 90th percentiles and the lag-one autocorrelation). Overall recognition accuracy for the standard feed forward Artificial Neural Network was 69.7%. Recognition accuracy for sedentary activities, light activities and games, moderate-to-vigorous activities, walking, and running was 82%, 79%, 64%, 36% and 46%, respectively. In comparison, overall recognition accuracy for the Deep Learning Ensemble Network was 82.6%. For sedentary activities, light activities and games, moderate-to-vigorous activities, walking, and running recognition accuracy was 84%, 91%, 79%, 73% and 73%, respectively. Ensemble machine learning approaches such as Deep Learning Ensemble Network can accurately predict activity type from accelerometer data in preschool children. Copyright © 2014 Sports Medicine Australia. Published by Elsevier Ltd. All rights reserved.

  10. Application of Machine Learning Techniques to High-Dimensional Clinical Data to Forecast Postoperative Complications.

    Directory of Open Access Journals (Sweden)

    Paul Thottakkara

    Full Text Available To compare performance of risk prediction models for forecasting postoperative sepsis and acute kidney injury.Retrospective single center cohort study of adult surgical patients admitted between 2000 and 2010.50,318 adult patients undergoing major surgery.We evaluated the performance of logistic regression, generalized additive models, naïve Bayes and support vector machines for forecasting postoperative sepsis and acute kidney injury. We assessed the impact of feature reduction techniques on predictive performance. Model performance was determined using the area under the receiver operating characteristic curve, accuracy, and positive predicted value. The results were reported based on a 70/30 cross validation procedure where the data were randomly split into 70% used for training the model and the 30% for validation.The areas under the receiver operating characteristic curve for different models ranged between 0.797 and 0.858 for acute kidney injury and between 0.757 and 0.909 for severe sepsis. Logistic regression, generalized additive model, and support vector machines had better performance compared to Naïve Bayes model. Generalized additive models additionally accounted for non-linearity of continuous clinical variables as depicted in their risk patterns plots. Reducing the input feature space with LASSO had minimal effect on prediction performance, while feature extraction using principal component analysis improved performance of the models.Generalized additive models and support vector machines had good performance as risk prediction model for postoperative sepsis and AKI. Feature extraction using principal component analysis improved the predictive performance of all models.

  11. A hybrid stock trading framework integrating technical analysis with machine learning techniques

    Directory of Open Access Journals (Sweden)

    Rajashree Dash

    2016-03-01

    Full Text Available In this paper, a novel decision support system using a computational efficient functional link artificial neural network (CEFLANN and a set of rules is proposed to generate the trading decisions more effectively. Here the problem of stock trading decision prediction is articulated as a classification problem with three class values representing the buy, hold and sell signals. The CEFLANN network used in the decision support system produces a set of continuous trading signals within the range 0–1 by analyzing the nonlinear relationship exists between few popular technical indicators. Further the output trading signals are used to track the trend and to produce the trading decision based on that trend using some trading rules. The novelty of the approach is to engender the profitable stock trading decision points through integration of the learning ability of CEFLANN neural network with the technical analysis rules. For assessing the potential use of the proposed method, the model performance is also compared with some other machine learning techniques such as Support Vector Machine (SVM, Naive Bayesian model, K nearest neighbor model (KNN and Decision Tree (DT model.

  12. Controlling the Adhesion of Superhydrophobic Surfaces Using Electrolyte Jet Machining Techniques

    Science.gov (United States)

    Yang, Xiaolong; Liu, Xin; Lu, Yao; Zhou, Shining; Gao, Mingqian; Song, Jinlong; Xu, Wenji

    2016-04-01

    Patterns with controllable adhesion on superhydrophobic areas have various biomedical and chemical applications. Electrolyte jet machining technique (EJM), an electrochemical machining method, was firstly exploited in constructing dimples with various profiles on the superhydrophobic Al alloy surface using different processing parameters. Sliding angles of water droplets on those dimples firstly increased and then stabilized at a certain value with the increase of the processing time or the applied voltages of the EJM, indicating that surfaces with different adhesion force could be obtained by regulating the processing parameters. The contact angle hysteresis and the adhesion force that restricts the droplet from sliding off were investigated through experiments. The results show that the adhesion force could be well described using the classical Furmidge equation. On account of this controllable adhesion force, water droplets could either be firmly pinned to the surface, forming various patterns or slide off at designed tilting angles at specified positions on a superhydrophobic surface. Such dimples on superhydrophopbic surfaces can be applied in water harvesting, biochemical analysis and lab-on-chip devices.

  13. Controlling the Adhesion of Superhydrophobic Surfaces Using Electrolyte Jet Machining Techniques.

    Science.gov (United States)

    Yang, Xiaolong; Liu, Xin; Lu, Yao; Zhou, Shining; Gao, Mingqian; Song, Jinlong; Xu, Wenji

    2016-04-05

    Patterns with controllable adhesion on superhydrophobic areas have various biomedical and chemical applications. Electrolyte jet machining technique (EJM), an electrochemical machining method, was firstly exploited in constructing dimples with various profiles on the superhydrophobic Al alloy surface using different processing parameters. Sliding angles of water droplets on those dimples firstly increased and then stabilized at a certain value with the increase of the processing time or the applied voltages of the EJM, indicating that surfaces with different adhesion force could be obtained by regulating the processing parameters. The contact angle hysteresis and the adhesion force that restricts the droplet from sliding off were investigated through experiments. The results show that the adhesion force could be well described using the classical Furmidge equation. On account of this controllable adhesion force, water droplets could either be firmly pinned to the surface, forming various patterns or slide off at designed tilting angles at specified positions on a superhydrophobic surface. Such dimples on superhydrophopbic surfaces can be applied in water harvesting, biochemical analysis and lab-on-chip devices.

  14. Influence of Heartwood on Wood Density and Pulp Properties Explained by Machine Learning Techniques

    Directory of Open Access Journals (Sweden)

    Carla Iglesias

    2017-01-01

    Full Text Available The aim of this work is to develop a tool to predict some pulp properties e.g., pulp yield, Kappa number, ISO brightness (ISO 2470:2008, fiber length and fiber width, using the sapwood and heartwood proportion in the raw-material. For this purpose, Acacia melanoxylon trees were collected from four sites in Portugal. Percentage of sapwood and heartwood, area and the stem eccentricity (in N-S and E-W directions were measured on transversal stem sections of A. melanoxylon R. Br. The relative position of the samples with respect to the total tree height was also considered as an input variable. Different configurations were tested until the maximum correlation coefficient was achieved. A classical mathematical technique (multiple linear regression and machine learning methods (classification and regression trees, multi-layer perceptron and support vector machines were tested. Classification and regression trees (CART was the most accurate model for the prediction of pulp ISO brightness (R = 0.85. The other parameters could be predicted with fair results (R = 0.64–0.75 by CART. Hence, the proportion of heartwood and sapwood is a relevant parameter for pulping and pulp properties, and should be taken as a quality trait when assessing a pulpwood resource.

  15. Robust object tracking techniques for vision-based 3D motion analysis applications

    Science.gov (United States)

    Knyaz, Vladimir A.; Zheltov, Sergey Y.; Vishnyakov, Boris V.

    2016-04-01

    Automated and accurate spatial motion capturing of an object is necessary for a wide variety of applications including industry and science, virtual reality and movie, medicine and sports. For the most part of applications a reliability and an accuracy of the data obtained as well as convenience for a user are the main characteristics defining the quality of the motion capture system. Among the existing systems for 3D data acquisition, based on different physical principles (accelerometry, magnetometry, time-of-flight, vision-based), optical motion capture systems have a set of advantages such as high speed of acquisition, potential for high accuracy and automation based on advanced image processing algorithms. For vision-based motion capture accurate and robust object features detecting and tracking through the video sequence are the key elements along with a level of automation of capturing process. So for providing high accuracy of obtained spatial data the developed vision-based motion capture system "Mosca" is based on photogrammetric principles of 3D measurements and supports high speed image acquisition in synchronized mode. It includes from 2 to 4 technical vision cameras for capturing video sequences of object motion. The original camera calibration and external orientation procedures provide the basis for high accuracy of 3D measurements. A set of algorithms as for detecting, identifying and tracking of similar targets, so for marker-less object motion capture is developed and tested. The results of algorithms' evaluation show high robustness and high reliability for various motion analysis tasks in technical and biomechanics applications.

  16. Study on real-time registration in dual spectrum low level light night vision technique

    Science.gov (United States)

    Bai, Lian-fa; Zhang, Yi; Zhang, Chuang; Chen, Qian; Gu, Guo-hua

    2009-07-01

    In low level light (LLL) color night vision technology, dual spectrum images with respective special information were acquired, and target identification probability would be effectively improved through dual spectrum image fusion. Image registration is one of the key technologies during this process. Current dual spectrum image registration methods mainly include dual imaging channel common optical axis scheme and image characteristic pixel searching scheme. In dual imaging channel common optical axis scheme, additional prismatic optical components should be used, and large amount of radiative energy was wasted. In image characteristic pixel searching scheme, complicated arithmetic made it difficult for its real time realization. In this paper, dual channel dual spectrum LLL color night vision system structure feature and dual spectrum image characteristics was studied, dual spectrum image gray scale symbiotic matrix 2-dimensional histogram was analysed, and a real time image registration method including electronic digital shifting, pixel extension and extraction was put forward. By the analysis of spatial gray-scale relativity of fusion image, registration precision is quantitatively expressed. Emulation experiments indicate that this arithmetic is fast and exact for our dual channel dual spectrum image registration. This method was realized on dual spectrum LLL color night vision experimental apparatus based on Texas Instruments digital video processing device DM642.

  17. Vision and Motion Pictures.

    Science.gov (United States)

    Grambo, Gregory

    1998-01-01

    Presents activities on persistence of vision that involve students in a hands-on approach to the study of early methods of creating motion pictures. Students construct flip books, a Zoetrope, and an early movie machine. (DDR)

  18. Acoustic Biometric System Based on Preprocessing Techniques and Linear Support Vector Machines.

    Science.gov (United States)

    del Val, Lara; Izquierdo-Fuente, Alberto; Villacorta, Juan J; Raboso, Mariano

    2015-06-17

    Drawing on the results of an acoustic biometric system based on a MSE classifier, a new biometric system has been implemented. This new system preprocesses acoustic images, extracts several parameters and finally classifies them, based on Support Vector Machine (SVM). The preprocessing techniques used are spatial filtering, segmentation-based on a Gaussian Mixture Model (GMM) to separate the person from the background, masking-to reduce the dimensions of images-and binarization-to reduce the size of each image. An analysis of classification error and a study of the sensitivity of the error versus the computational burden of each implemented algorithm are presented. This allows the selection of the most relevant algorithms, according to the benefits required by the system. A significant improvement of the biometric system has been achieved by reducing the classification error, the computational burden and the storage requirements.

  19. Acoustic Biometric System Based on Preprocessing Techniques and Linear Support Vector Machines

    Directory of Open Access Journals (Sweden)

    Lara del Val

    2015-06-01

    Full Text Available Drawing on the results of an acoustic biometric system based on a MSE classifier, a new biometric system has been implemented. This new system preprocesses acoustic images, extracts several parameters and finally classifies them, based on Support Vector Machine (SVM. The preprocessing techniques used are spatial filtering, segmentation—based on a Gaussian Mixture Model (GMM to separate the person from the background, masking—to reduce the dimensions of images—and binarization—to reduce the size of each image. An analysis of classification error and a study of the sensitivity of the error versus the computational burden of each implemented algorithm are presented. This allows the selection of the most relevant algorithms, according to the benefits required by the system. A significant improvement of the biometric system has been achieved by reducing the classification error, the computational burden and the storage requirements.

  20. Hybrid machine learning technique for forecasting Dhaka stock market timing decisions.

    Science.gov (United States)

    Banik, Shipra; Khodadad Khan, A F M; Anwer, Mohammad

    2014-01-01

    Forecasting stock market has been a difficult job for applied researchers owing to nature of facts which is very noisy and time varying. However, this hypothesis has been featured by several empirical experiential studies and a number of researchers have efficiently applied machine learning techniques to forecast stock market. This paper studied stock prediction for the use of investors. It is always true that investors typically obtain loss because of uncertain investment purposes and unsighted assets. This paper proposes a rough set model, a neural network model, and a hybrid neural network and rough set model to find optimal buy and sell of a share on Dhaka stock exchange. Investigational findings demonstrate that our proposed hybrid model has higher precision than the single rough set model and the neural network model. We believe this paper findings will help stock investors to decide about optimal buy and/or sell time on Dhaka stock exchange.

  1. Acoustic Biometric System Based on Preprocessing Techniques and Linear Support Vector Machines

    Science.gov (United States)

    del Val, Lara; Izquierdo-Fuente, Alberto; Villacorta, Juan J.; Raboso, Mariano

    2015-01-01

    Drawing on the results of an acoustic biometric system based on a MSE classifier, a new biometric system has been implemented. This new system preprocesses acoustic images, extracts several parameters and finally classifies them, based on Support Vector Machine (SVM). The preprocessing techniques used are spatial filtering, segmentation—based on a Gaussian Mixture Model (GMM) to separate the person from the background, masking—to reduce the dimensions of images—and binarization—to reduce the size of each image. An analysis of classification error and a study of the sensitivity of the error versus the computational burden of each implemented algorithm are presented. This allows the selection of the most relevant algorithms, according to the benefits required by the system. A significant improvement of the biometric system has been achieved by reducing the classification error, the computational burden and the storage requirements. PMID:26091392

  2. Discussion on "Techniques for Massive-Data Machine Learning in Astronomy" by A. Gray

    CERN Document Server

    Ball, Nicholas M

    2011-01-01

    Astronomy is increasingly encountering two fundamental truths: (1) The field is faced with the task of extracting useful information from extremely large, complex, and high dimensional datasets; (2) The techniques of astroinformatics and astrostatistics are the only way to make this tractable, and bring the required level of sophistication to the analysis. Thus, an approach which provides these tools in a way that scales to these datasets is not just desirable, it is vital. The expertise required spans not just astronomy, but also computer science, statistics, and informatics. As a computer scientist and expert in machine learning, Alex's contribution of expertise and a large number of fast algorithms designed to scale to large datasets, is extremely welcome. We focus in this discussion on the questions raised by the practical application of these algorithms to real astronomical datasets. That is, what is needed to maximally leverage their potential to improve the science return? This is not a trivial task. W...

  3. Fast supersymmetry phenomenology at the Large Hadron Collider using machine learning techniques

    CERN Document Server

    Buckley, A; White, M J

    2011-01-01

    A pressing problem for supersymmetry (SUSY) phenomenologists is how to incorporate Large Hadron Collider search results into parameter fits designed to measure or constrain the SUSY parameters. Owing to the computational expense of fully simulating lots of points in a generic SUSY space to aid the calculation of the likelihoods, the limits published by experimental collaborations are frequently interpreted in slices of reduced parameter spaces. For example, both ATLAS and CMS have presented results in the Constrained Minimal Supersymmetric Model (CMSSM) by fixing two of four parameters, and generating a coarse grid in the remaining two. We demonstrate that by generating a grid in the full space of the CMSSM, one can interpolate between the output of an LHC detector simulation using machine learning techniques, thus obtaining a superfast likelihood calculator for LHC-based SUSY parameter fits. We further investigate how much training data is required to obtain usable results, finding that approximately 2000 po...

  4. Modeling, Control and Analyze of Multi-Machine Drive Systems using Bond Graph Technique

    Directory of Open Access Journals (Sweden)

    J. Belhadj

    2006-03-01

    Full Text Available In this paper, a system viewpoint method has been investigated to study and analyze complex systems using Bond Graph technique. These systems are multimachine multi-inverter based on Induction Machine (IM, well used in industries like rolling mills, textile, and railway traction. These systems are multi-domains, multi-scales time and present very strong internal and external couplings, with non-linearity characterized by a high model order. The classical study with analytic model is difficult to manipulate and it is limited to some performances. In this study, a “systemic approach” is presented to design these kinds of systems, using an energetic representation based on Bond Graph formalism. Three types of multimachine are studied with their control strategies. The modeling is carried out by Bond Graph and results are discussed to show the performances of this methodology

  5. Quantum-state anomaly detection for arbitrary errors using a machine-learning technique

    Science.gov (United States)

    Hara, Satoshi; Ono, Takafumi; Okamoto, Ryo; Washio, Takashi; Takeuchi, Shigeki

    2016-10-01

    The accurate detection of small deviations in given density matrice is important for quantum information processing, which is a difficult task because of the intrinsic fluctuation in density matrices reconstructed using a limited number of experiments. We previously proposed a method for decoherence error detection using a machine-learning technique [S. Hara, T. Ono, R. Okamoto, T. Washio, and S. Takeuchi, Phys. Rev. A 89, 022104 (2014), 10.1103/PhysRevA.89.022104]. However, the previous method is not valid when the errors are just changes in phase. Here, we propose a method that is valid for arbitrary errors in density matrices. The performance of the proposed method is verified using both numerical simulation data and real experimental data.

  6. Detection of denial-of-service attacks based on computer vision techniques

    NARCIS (Netherlands)

    Tan, Zhiyuan; Jamdagni, Aruna; He, Xiangjian; Nanda, Priyadarsi; Liu, Ren Ping; Hu, Jiankun

    2014-01-01

    Detection of Denial-of-Service (DoS) attacks has attracted researchers since 1990s. A variety of detection systems has been proposed to achieve this task. Unlike the existing approaches based on machine learning and statistical analysis, the proposed system treats traffic records as images and detec

  7. Development of Experimental Setup of Metal Rapid Prototyping Machine using Selective Laser Sintering Technique

    Science.gov (United States)

    Patil, S. N.; Mulay, A. V.; Ahuja, B. B.

    2016-08-01

    Unlike in the traditional manufacturing processes, additive manufacturing as rapid prototyping, allows designers to produce parts that were previously considered too complex to make economically. The shift is taking place from plastic prototype to fully functional metallic parts by direct deposition of metallic powders as produced parts can be directly used for desired purpose. This work is directed towards the development of experimental setup of metal rapid prototyping machine using selective laser sintering and studies the various parameters, which plays important role in the metal rapid prototyping using SLS technique. The machine structure in mainly divided into three main categories namely, (1) Z-movement of bed and table, (2) X-Y movement arrangement for LASER movements and (3) feeder mechanism. Z-movement of bed is controlled by using lead screw, bevel gear pair and stepper motor, which will maintain the accuracy of layer thickness. X-Y movements are controlled using timing belt and stepper motors for precise movements of LASER source. Feeder mechanism is then developed to control uniformity of layer thickness metal powder. Simultaneously, the study is carried out for selection of material. Various types of metal powders can be used for metal RP as Single metal powder, mixture of two metals powder, and combination of metal and polymer powder. Conclusion leads to use of mixture of two metals powder to minimize the problems such as, balling effect and porosity. Developed System can be validated by conducting various experiments on manufactured part to check mechanical and metallurgical properties. After studying the results of these experiments, various process parameters as LASER properties (as power, speed etc.), and material properties (as grain size and structure etc.) will be optimized. This work is mainly focused on the design and development of cost effective experimental setup of metal rapid prototyping using SLS technique which will gives the feel of

  8. Markerless gating for lung cancer radiotherapy based on machine learning techniques

    Energy Technology Data Exchange (ETDEWEB)

    Lin Tong; Li Ruijiang; Tang Xiaoli; Jiang, Steve B [Department of Radiation Oncology, University of California San Diego, La Jolla, CA 92093 (United States); Dy, Jennifer G [Department of Electrical and Computer Engineering, Northeastern University, Boston, MA 02115 (United States)], E-mail: sbjiang@ucsd.edu

    2009-03-21

    In lung cancer radiotherapy, radiation to a mobile target can be delivered by respiratory gating, for which we need to know whether the target is inside or outside a predefined gating window at any time point during the treatment. This can be achieved by tracking one or more fiducial markers implanted inside or near the target, either fluoroscopically or electromagnetically. However, the clinical implementation of marker tracking is limited for lung cancer radiotherapy mainly due to the risk of pneumothorax. Therefore, gating without implanted fiducial markers is a promising clinical direction. We have developed several template-matching methods for fluoroscopic marker-less gating. Recently, we have modeled the gating problem as a binary pattern classification problem, in which principal component analysis (PCA) and support vector machine (SVM) are combined to perform the classification task. Following the same framework, we investigated different combinations of dimensionality reduction techniques (PCA and four nonlinear manifold learning methods) and two machine learning classification methods (artificial neural networks-ANN and SVM). Performance was evaluated on ten fluoroscopic image sequences of nine lung cancer patients. We found that among all combinations of dimensionality reduction techniques and classification methods, PCA combined with either ANN or SVM achieved a better performance than the other nonlinear manifold learning methods. ANN when combined with PCA achieves a better performance than SVM in terms of classification accuracy and recall rate, although the target coverage is similar for the two classification methods. Furthermore, the running time for both ANN and SVM with PCA is within tolerance for real-time applications. Overall, ANN combined with PCA is a better candidate than other combinations we investigated in this work for real-time gated radiotherapy.

  9. Selection of machining datum and allocation of tolerance through tolerance charting technique

    Science.gov (United States)

    Thilak, Manoharan; Sivakumar, Karuppan; Jayaprakash, Govindharajalu

    2012-07-01

    Tolerance charting is an effective tool to determine the optimal allocation of working dimensions and working tolerances such that the blueprint dimensions and tolerances can be achieved to accomplish the cost objectives. The selection of machining datum and allocation of tolerances are critical in any machining process planning as they directly affect any setup methods/machine tools selection and machining time. This paper mainly focuses on the selection of optimum machining datums and machining tolerances simultaneously in process planning. A dynamic tolerance charting constraint scheme is developed and implemented in the optimization procedure. An optimization model is formulated for selecting machining datum and tolerances and implemented with an algorithm namely Elitist Non-Dominated Sorting Genetic Algorithm (NSGA-II).The computational results indicate that the proposed methodology is capable and robust in finding the optimal machining datum set and tolerances.

  10. Multivariate Time Series Forecasting of Crude Palm Oil Price Using Machine Learning Techniques

    Science.gov (United States)

    Kanchymalay, Kasturi; Salim, N.; Sukprasert, Anupong; Krishnan, Ramesh; Raba'ah Hashim, Ummi

    2017-08-01

    The aim of this paper was to study the correlation between crude palm oil (CPO) price, selected vegetable oil prices (such as soybean oil, coconut oil, and olive oil, rapeseed oil and sunflower oil), crude oil and the monthly exchange rate. Comparative analysis was then performed on CPO price forecasting results using the machine learning techniques. Monthly CPO prices, selected vegetable oil prices, crude oil prices and monthly exchange rate data from January 1987 to February 2017 were utilized. Preliminary analysis showed a positive and high correlation between the CPO price and soy bean oil price and also between CPO price and crude oil price. Experiments were conducted using multi-layer perception, support vector regression and Holt Winter exponential smoothing techniques. The results were assessed by using criteria of root mean square error (RMSE), means absolute error (MAE), means absolute percentage error (MAPE) and Direction of accuracy (DA). Among these three techniques, support vector regression(SVR) with Sequential minimal optimization (SMO) algorithm showed relatively better results compared to multi-layer perceptron and Holt Winters exponential smoothing method.

  11. Machine Learning Techniques for Optical Performance Monitoring from Directly Detected PDM-QAM Signals

    DEFF Research Database (Denmark)

    Thrane, Jakob; Wass, Jesper; Piels, Molly

    2017-01-01

    Linear signal processing algorithms are effective in dealing with linear transmission channel and linear signal detection, while the nonlinear signal processing algorithms, from the machine learning community, are effective in dealing with nonlinear transmission channel and nonlinear signal...... detection. In this paper, a brief overview of the various machine learning methods and their application in optical communication is presented and discussed. Moreover, supervised machine learning methods, such as neural networks and support vector machine, are experimentally demonstrated for in-band optical...

  12. Thread Recognition System Based on Machine Vision Technology%基于机器视觉技术的螺纹识别系统

    Institute of Scientific and Technical Information of China (English)

    景敏

    2013-01-01

    Thread angle identification is a common method to distinguish thread types. Traditional detection methods have many disadvantages such as low efficiency and high cost and gauges are easy to be abraded. The needs of high efficient development of modern industry are not met any more. CCD is used to obtain the basic im-age of thread. And the thread contour is analyzed through image smoothness, edge detection, binary image pro-duction and contour hunting. The thread angle parameters are measured and identified. The measurement meth-ods of thread angle parameter using machine vision are discussed. And a thread recognition system mainly based on the machine vision recognition technology and integrated visual sensing with image processing system is de-signed. The feasibility and correctness of the method is proved from theory and practice.%螺纹牙型角识别是区分螺纹种类的常用手段,传统检测手段效率低、量规易磨损、成本高,已不能满足现代工业高效发展的需求。利用CCD获取螺纹基本图像,并通过图像的平滑、边缘检测、二值化处理及轮廓提取,对螺纹轮廓进行分析,从中测量出螺纹的牙型角参数并进行识别。探讨了利用机器视觉对螺纹牙型角参数进行测量的方法,并设计出一套以机器视觉识别技术为核心的视觉传感和图像处理系统为一体的螺纹识别系统。从理论和实践上证实了该方法的可行性和准确性。

  13. Enhancement of vision systems based on runway detection by image processing techniques

    Science.gov (United States)

    Gulec, N.; Sen Koktas, N.

    2012-06-01

    An explicit way of facilitating approach and landing operations of fixed-wing aircraft in degraded visual environments is presenting a coherent image of the designated runway via vision systems and hence increasing the situational awareness of the flight crew. Combined vision systems, in general, aim to provide a clear view of the aircraft exterior to the pilots using information from databases and imaging sensors. This study presents a novel method that consists of image-processing and tracking algorithms, which utilize information from navigation systems and databases along with the images from daylight and infrared cameras, for the recognition and tracking of the designated runway through the approach and landing operation. Video data simulating the straight-in approach of an aircraft from an altitude of 5000 ft down to 100 ft is synthetically generated by a COTS tool. A diverse set of atmospheric conditions such as fog and low light levels are simulated in these videos. Detection and false alarm rates are used as the primary performance metrics. The results are presented in a format where the performance metrics are compared against the altitude of the aircraft. Depending on the visual environment and the source of the video, the performance metrics reach up to 98% for DR and down to 5% for FAR.

  14. Machine learning techniques in disease forecasting: a case study on rice blast prediction

    Directory of Open Access Journals (Sweden)

    Kapoor Amar S

    2006-11-01

    Full Text Available Abstract Background Diverse modeling approaches viz. neural networks and multiple regression have been followed to date for disease prediction in plant populations. However, due to their inability to predict value of unknown data points and longer training times, there is need for exploiting new prediction softwares for better understanding of plant-pathogen-environment relationships. Further, there is no online tool available which can help the plant researchers or farmers in timely application of control measures. This paper introduces a new prediction approach based on support vector machines for developing weather-based prediction models of plant diseases. Results Six significant weather variables were selected as predictor variables. Two series of models (cross-location and cross-year were developed and validated using a five-fold cross validation procedure. For cross-year models, the conventional multiple regression (REG approach achieved an average correlation coefficient (r of 0.50, which increased to 0.60 and percent mean absolute error (%MAE decreased from 65.42 to 52.24 when back-propagation neural network (BPNN was used. With generalized regression neural network (GRNN, the r increased to 0.70 and %MAE also improved to 46.30, which further increased to r = 0.77 and %MAE = 36.66 when support vector machine (SVM based method was used. Similarly, cross-location validation achieved r = 0.48, 0.56 and 0.66 using REG, BPNN and GRNN respectively, with their corresponding %MAE as 77.54, 66.11 and 58.26. The SVM-based method outperformed all the three approaches by further increasing r to 0.74 with improvement in %MAE to 44.12. Overall, this SVM-based prediction approach will open new vistas in the area of forecasting plant diseases of various crops. Conclusion Our case study demonstrated that SVM is better than existing machine learning techniques and conventional REG approaches in forecasting plant diseases. In this direction, we have also

  15. Three-Dimensional Robotic Vision System

    Science.gov (United States)

    Nguyen, Thinh V.

    1989-01-01

    Stereoscopy and motion provide clues to outlines of objects. Digital image-processing system acts as "intelligent" automatic machine-vision system by processing views from stereoscopic television cameras into three-dimensional coordinates of moving object in view. Epipolar-line technique used to find corresponding points in stereoscopic views. Robotic vision system analyzes views from two television cameras to detect rigid three-dimensional objects and reconstruct numerically in terms of coordinates of corner points. Stereoscopy and effects of motion on two images complement each other in providing image-analyzing subsystem with clues to natures and locations of principal features.

  16. A methodology for automated CPA extraction using liver biopsy image analysis and machine learning techniques.

    Science.gov (United States)

    Tsipouras, Markos G; Giannakeas, Nikolaos; Tzallas, Alexandros T; Tsianou, Zoe E; Manousou, Pinelopi; Hall, Andrew; Tsoulos, Ioannis; Tsianos, Epameinondas

    2017-03-01

    Collagen proportional area (CPA) extraction in liver biopsy images provides the degree of fibrosis expansion in liver tissue, which is the most characteristic histological alteration in hepatitis C virus (HCV). Assessment of the fibrotic tissue is currently based on semiquantitative staging scores such as Ishak and Metavir. Since its introduction as a fibrotic tissue assessment technique, CPA calculation based on image analysis techniques has proven to be more accurate than semiquantitative scores. However, CPA has yet to reach everyday clinical practice, since the lack of standardized and robust methods for computerized image analysis for CPA assessment have proven to be a major limitation. The current work introduces a three-stage fully automated methodology for CPA extraction based on machine learning techniques. Specifically, clustering algorithms have been employed for background-tissue separation, as well as for fibrosis detection in liver tissue regions, in the first and the third stage of the methodology, respectively. Due to the existence of several types of tissue regions in the image (such as blood clots, muscle tissue, structural collagen, etc.), classification algorithms have been employed to identify liver tissue regions and exclude all other non-liver tissue regions from CPA computation. For the evaluation of the methodology, 79 liver biopsy images have been employed, obtaining 1.31% mean absolute CPA error, with 0.923 concordance correlation coefficient. The proposed methodology is designed to (i) avoid manual threshold-based and region selection processes, widely used in similar approaches presented in the literature, and (ii) minimize CPA calculation time. Copyright © 2016 Elsevier Ireland Ltd. All rights reserved.

  17. Demonstration of a semi-autonomous hybrid brain-machine interface using human intracranial EEG, eye tracking, and computer vision to control a robotic upper limb prosthetic.

    Science.gov (United States)

    McMullen, David P; Hotson, Guy; Katyal, Kapil D; Wester, Brock A; Fifer, Matthew S; McGee, Timothy G; Harris, Andrew; Johannes, Matthew S; Vogelstein, R Jacob; Ravitz, Alan D; Anderson, William S; Thakor, Nitish V; Crone, Nathan E

    2014-07-01

    To increase the ability of brain-machine interfaces (BMIs) to control advanced prostheses such as the modular prosthetic limb (MPL), we are developing a novel system: the Hybrid Augmented Reality Multimodal Operation Neural Integration Environment (HARMONIE). This system utilizes hybrid input, supervisory control, and intelligent robotics to allow users to identify an object (via eye tracking and computer vision) and initiate (via brain-control) a semi-autonomous reach-grasp-and-drop of the object by the MPL. Sequential iterations of HARMONIE were tested in two pilot subjects implanted with electrocorticographic (ECoG) and depth electrodes within motor areas. The subjects performed the complex task in 71.4% (20/28) and 67.7% (21/31) of trials after minimal training. Balanced accuracy for detecting movements was 91.1% and 92.9%, significantly greater than chance accuracies (p system improvements implemented for the second subject. Our hybrid-BMI design prevented all but one baseline false positive from initiating the system. The novel approach demonstrated in this proof-of-principle study, using hybrid input, supervisory control, and intelligent robotics, addresses limitations of current BMIs.

  18. DEVELOPMENT OF A MACHINE VISION SYSTEM FOR WEED DETECTION DURING BOTH OF OFF-SEASON AND IN-SEASON IN BROADACRE NO-TILLAGE CROPPING LANDS

    Directory of Open Access Journals (Sweden)

    Huajian Liu

    2014-01-01

    Full Text Available More than half of the Australian cropping land is no-tillage and weed control within continuous no-tillage agricultural cropping area is becoming more and more difficult. A major problem is that the heavy herbicide usage causes some of more prolific weeds becoming more resistant to the regular herbicides and therefore more powerful and more expensive options are being pursued. To overcome such problems with aiming at the reduction of herbicide usage, this proposed research focuses on developing a machine vision system which can detect and mapping weeds or do spot spray. The weed detection methods described in this study include three aspects which are image acquisition, a new green plant detection algorithm using hybrid spectral indices and a new inter-row weed detection method taking the advantage of the location of the crop rows. The developed method could detect the weeds both during the non-growing summer period and also within the growing season until the canopy of the crop has closed. The design of the methods focuses on overcoming the challenges of the complex no-tillage background, the faster image acquisition speed and quicker processing time for real-time spot spray. The experiment results show that the proposed method are more suitable for the weed detection in the no-tillage background than the existing methods and could be used as a powerful tool for the weed control.

  19. Designing of cigarette detection system based on machine vision%基于机器视觉的烟支检测系统的设计

    Institute of Scientific and Technical Information of China (English)

    章磊; 李耀; 刘光徽

    2012-01-01

    According to the loose-ends and tobacco shortage problems in cigarette packaging line. This paper proposes a software and hardware design of cigarette detection system based on machine vision, gives the critical circuit and software processes of system, and analyzes problems in practical test. The system realizes the real-time images acquisition by OV7620 and FIFO cache, uses the image processing technology in cigarette image analysis,and chieves the automatic detection of the loose-ends and tobacco shortage.%针对卷烟包装线上出现的空头和缺支问题,提出了一种基于机器视觉技术的烟支在线检测系统软硬件设计,给出了关键的电路原理图及软件流程,并对实际测试中的一些问题进行了分析.系统利用OV7620及FIFO缓存技术实现了图像的实时采集,并采用数字图像处理的方法对烟支图像进行分析,实现了空头及缺支的自动检测.

  20. SURFACE QUALITY INSPECTION OF CERAMIC TILES BY MACHINE VISION%陶瓷砖表面质量视觉检测系统研究

    Institute of Scientific and Technical Information of China (English)

    李庆利; 郭彩玲; 张向红

    2011-01-01

    重点介绍了机器视觉技术在陶瓷砖表面质量检测中的应用.系统采用面阵摄象机作为测量工具,应用方向算子进行对目标边缘的定位和跟踪,以便获得完整、精确、封闭的目标边缘.实现了对陶瓷砖的边直度、直角度、缺边和缺角等项目的非接触检测.%In this paper, an on-line machine vision system for surface quality inspection of ceramic tiles is introduced, which grabs the images scanned by area-array cameras. The image processing algorithm which uses direction masks is used to locate the edge points exactly. The surface quality of ceramic tiles is measured by the real-time, high precision and non-contact method, which can detect straightness of sides, deviation from rectangularity, rough edge, chip and so on.

  1. STUDY ON SURFACE QUALITY OF CERAMIC TILES BY MACHINE VISION%陶瓷砖表面质量视觉检测系统研究

    Institute of Scientific and Technical Information of China (English)

    李庆利; 郭彩玲; 张向红

    2011-01-01

    In this paper an on-line machine vision system for surface quality of ceramic tiles is introduced, which grabs the images scanned by area-array camera.The image processing algorithm which uses direction masks is used to locate the edge points exactly.The surface quality of ceramic tiles is measured with the real-time, high precision and non-contact method, which includes straightness of sides,deviation from rectangularity, rough edge,chip and so on.%重点介绍了机器视觉技术在陶瓷砖表面质量检测中的应用.系统采用面阵摄像机作为测量工具,应用方向算子进行对目标边缘的定位和跟踪,以便获得完整、精确、封闭的目标边缘.实现了对陶瓷砖的边直度、直角度、缺边和缺角等项目的非接触检测.

  2. Analysed potential of big data and supervised machine learning techniques in effectively forecasting travel times from fused data

    Directory of Open Access Journals (Sweden)

    Ivana Šemanjski

    2015-12-01

    Full Text Available Travel time forecasting is an interesting topic for many ITS services. Increased availability of data collection sensors increases the availability of the predictor variables but also highlights the high processing issues related to this big data availability. In this paper we aimed to analyse the potential of big data and supervised machine learning techniques in effectively forecasting travel times. For this purpose we used fused data from three data sources (Global Positioning System vehicles tracks, road network infrastructure data and meteorological data and four machine learning techniques (k-nearest neighbours, support vector machines, boosting trees and random forest. To evaluate the forecasting results we compared them in-between different road classes in the context of absolute values, measured in minutes, and the mean squared percentage error. For the road classes with the high average speed and long road segments, machine learning techniques forecasted travel times with small relative error, while for the road classes with the small average speeds and segment lengths this was a more demanding task. All three data sources were proven itself to have a high impact on the travel time forecast accuracy and the best results (taking into account all road classes were achieved for the k-nearest neighbours and random forest techniques.

  3. Low Vision

    Science.gov (United States)

    ... HHS USAJobs Home > Statistics and Data > Low Vision Low Vision Low Vision Defined: Low Vision is defined as the ... Ethnicity 2010 U.S. Age-Specific Prevalence Rates for Low Vision by Age, and Race/Ethnicity Table for ...

  4. Phase segmentation of X-ray computer tomography rock images using machine learning techniques: an accuracy and performance study

    Science.gov (United States)

    Chauhan, Swarup; Rühaak, Wolfram; Anbergen, Hauke; Kabdenov, Alen; Freise, Marcus; Wille, Thorsten; Sass, Ingo

    2016-07-01

    Performance and accuracy of machine learning techniques to segment rock grains, matrix and pore voxels from a 3-D volume of X-ray tomographic (XCT) grayscale rock images was evaluated. The segmentation and classification capability of unsupervised (k-means, fuzzy c-means, self-organized maps), supervised (artificial neural networks, least-squares support vector machines) and ensemble classifiers (bragging and boosting) were tested using XCT images of andesite volcanic rock, Berea sandstone, Rotliegend sandstone and a synthetic sample. The averaged porosity obtained for andesite (15.8 ± 2.5 %), Berea sandstone (16.3 ± 2.6 %), Rotliegend sandstone (13.4 ± 7.4 %) and the synthetic sample (48.3 ± 13.3 %) is in very good agreement with the respective laboratory measurement data and varies by a factor of 0.2. The k-means algorithm is the fastest of all machine learning algorithms, whereas a least-squares support vector machine is the most computationally expensive. Metrics entropy, purity, mean square root error, receiver operational characteristic curve and 10 K-fold cross-validation were used to determine the accuracy of unsupervised, supervised and ensemble classifier techniques. In general, the accuracy was found to be largely affected by the feature vector selection scheme. As it is always a trade-off between performance and accuracy, it is difficult to isolate one particular machine learning algorithm which is best suited for the complex phase segmentation problem. Therefore, our investigation provides parameters that can help in selecting the appropriate machine learning techniques for phase segmentation.

  5. Taxi Time Prediction at Charlotte Airport Using Fast-Time Simulation and Machine Learning Techniques

    Science.gov (United States)

    Lee, Hanbong

    2016-01-01

    Accurate taxi time prediction is required for enabling efficient runway scheduling that can increase runway throughput and reduce taxi times and fuel consumptions on the airport surface. Currently NASA and American Airlines are jointly developing a decision-support tool called Spot and Runway Departure Advisor (SARDA) that assists airport ramp controllers to make gate pushback decisions and improve the overall efficiency of airport surface traffic. In this presentation, we propose to use Linear Optimized Sequencing (LINOS), a discrete-event fast-time simulation tool, to predict taxi times and provide the estimates to the runway scheduler in real-time airport operations. To assess its prediction accuracy, we also introduce a data-driven analytical method using machine learning techniques. These two taxi time prediction methods are evaluated with actual taxi time data obtained from the SARDA human-in-the-loop (HITL) simulation for Charlotte Douglas International Airport (CLT) using various performance measurement metrics. Based on the taxi time prediction results, we also discuss how the prediction accuracy can be affected by the operational complexity at this airport and how we can improve the fast time simulation model before implementing it with an airport scheduling algorithm in a real-time environment.

  6. Evaluating machine-learning techniques for recruitment forecasting of seven North East Atlantic fish species

    KAUST Repository

    Fernandes, José Antonio

    2015-01-01

    The effect of different factors (spawning biomass, environmental conditions) on recruitment is a subject of great importance in the management of fisheries, recovery plans and scenario exploration. In this study, recently proposed supervised classification techniques, tested by the machine-learning community, are applied to forecast the recruitment of seven fish species of North East Atlantic (anchovy, sardine, mackerel, horse mackerel, hake, blue whiting and albacore), using spawning, environmental and climatic data. In addition, the use of the probabilistic flexible naive Bayes classifier (FNBC) is proposed as modelling approach in order to reduce uncertainty for fisheries management purposes. Those improvements aim is to improve probability estimations of each possible outcome (low, medium and high recruitment) based in kernel density estimation, which is crucial for informed management decision making with high uncertainty. Finally, a comparison between goodness-of-fit and generalization power is provided, in order to assess the reliability of the final forecasting models. It is found that in most cases the proposed methodology provides useful information for management whereas the case of horse mackerel is an example of the limitations of the approach. The proposed improvements allow for a better probabilistic estimation of the different scenarios, i.e. to reduce the uncertainty in the provided forecasts.

  7. Online laboratory evaluation of seeding-machine application by an acoustic technique

    Energy Technology Data Exchange (ETDEWEB)

    Karimi, H.; Navid, H.; Mahmoudi, A.

    2015-07-01

    Researchers and planter manufacturers have been working closely to develop an automated system for evaluating performance of seeding. In the present study, an innovative use of acoustic signal for laboratory evaluation of seeding-machine application is described. Seed detection technique of the proposed system was based on a rising voltage value that a microphone sensed in each impaction of seeds to a steel plate. Online determining of seed spacing was done with a script which was written in MATLAB software. To evaluate the acoustic system with desired seed spacing, a testing rig was designed. Seeds of wheat, corn and pelleted tomato were used as experimental material. Typical seed patterns were positioned manually on a belt stand with different spacing patterns. When the belt was running, the falling seeds from the end point of the belt impacted to the steel plate, and their acoustic signal was sensed by the microphone. In each impact, data was processed and spacing between the seeds was automatically obtained. Coefficient of determination of gathered data from the belt system and the corresponding seeds spacing measured with the acoustic system in all runs was about 0.98. This strong correlation indicates that the acoustic system worked well in determining the seeds spacing. (Author)

  8. Predicting Software Faults in Large Space Systems using Machine Learning Techniques

    Directory of Open Access Journals (Sweden)

    Bhekisipho Twala

    2011-07-01

    Full Text Available Recently, the use of machine learning (ML algorithms has proven to be of great practical value in solving a variety of engineering problems including the prediction of failure, fault, and defect-proneness as the space system software becomes complex. One of the most active areas of recent research in ML has been the use of ensemble classifiers. How ML techniques (or classifiers could be used to predict software faults in space systems, including many aerospace systems is shown, and further use ensemble individual classifiers by having them vote for the most popular class to improve system software fault-proneness prediction. Benchmarking results on four NASA public datasets show the Naive Bayes classifier as more robust software fault prediction while most ensembles with a decision tree classifier as one of its components achieve higher accuracy rates.Defence Science Journal, 2011, 61(4, pp.306-316, DOI:http://dx.doi.org/10.14429/dsj.61.1088

  9. Machine Learning Techniques Applied to Sensor Data Correction in Building Technologies

    Energy Technology Data Exchange (ETDEWEB)

    Smith, Matt K [ORNL; Castello, Charles C [ORNL; New, Joshua Ryan [ORNL

    2013-01-01

    Since commercial and residential buildings account for nearly half of the United States' energy consumption, making them more energy-efficient is a vital part of the nation's overall energy strategy. Sensors play an important role in this research by collecting data needed to analyze performance of components, systems, and whole-buildings. Given this reliance on sensors, ensuring that sensor data are valid is a crucial problem. Solutions being researched are machine learning techniques, namely: artificial neural networks and Bayesian Networks. Types of data investigated in this study are: (1) temperature; (2) humidity; (3) refrigerator energy consumption; (4) heat pump liquid pressure; and (5) water flow. These data are taken from Oak Ridge National Laboratory's (ORNL) ZEBRAlliance research project which is composed of four single-family homes in Oak Ridge, TN. Results show that for the temperature, humidity, pressure, and flow sensors, data can mostly be predicted with root-mean-square error (RMSE) of less than 10% of the respective sensor's mean value. Results for the energy sensor are not as good; RMSE are centered about 100% of the mean value and are often well above 200%. Bayesian networks have RSME of less than 5% of the respective sensor's mean value, but took substantially longer to train.

  10. Estimating gypsum equirement under no-till based on machine learning technique

    Directory of Open Access Journals (Sweden)

    Alaine Margarete Guimarães

    Full Text Available Chemical stratification occurs under no-till systems, including pH, considering that higher levels are formed from the soil surface towards the deeper layers. The subsoil acidity is a limiting factor of the yield. Gypsum has been suggested when subsoil acidity limits the crops root growth, i.e., when the calcium (Ca level is low and/or the aluminum (Al level is toxic in the subsoil layers. However, there are doubts about the more efficient methods to estimate the gypsum requirement. This study was carried out to develop numerical models to estimate the gypsum requirement in soils under no-till system by the use of Machine Learning techniques. Computational analyses of the dataset were made applying the M5'Rules algorithm, based on regression models. The dataset comprised of soil chemical properties collected from experiments under no-till that received gypsum rates on the soil surface, throughout eight years after the application, in Southern Brazil. The results showed that the numerical models generated by rule induction M5'Rules algorithm were positively useful contributing for estimate the gypsum requirements under no-till. The models showed that Ca saturation in the effective cation exchange capacity (ECEC was a more important attribute than Al saturation to estimate gypsum requirement in no-till soils.

  11. Modelling and analysing track cycling Omnium performances using statistical and machine learning techniques.

    Science.gov (United States)

    Ofoghi, Bahadorreza; Zeleznikow, John; Dwyer, Dan; Macmahon, Clare

    2013-01-01

    This article describes the utilisation of an unsupervised machine learning technique and statistical approaches (e.g., the Kolmogorov-Smirnov test) that assist cycling experts in the crucial decision-making processes for athlete selection, training, and strategic planning in the track cycling Omnium. The Omnium is a multi-event competition that will be included in the summer Olympic Games for the first time in 2012. Presently, selectors and cycling coaches make decisions based on experience and intuition. They rarely have access to objective data. We analysed both the old five-event (first raced internationally in 2007) and new six-event (first raced internationally in 2011) Omniums and found that the addition of the elimination race component to the Omnium has, contrary to expectations, not favoured track endurance riders. We analysed the Omnium data and also determined the inter-relationships between different individual events as well as between those events and the final standings of riders. In further analysis, we found that there is no maximum ranking (poorest performance) in each individual event that riders can afford whilst still winning a medal. We also found the required times for riders to finish the timed components that are necessary for medal winning. The results of this study consider the scoring system of the Omnium and inform decision-making toward successful participation in future major Omnium competitions.

  12. The PLC Control of Vision Detection Machine for Ceramic Sleeve Surface Defects%陶瓷套圈表面质量机器视觉检测系统

    Institute of Scientific and Technical Information of China (English)

    张磊; 陈红; 范维浩

    2011-01-01

    本文在分析陶瓷套圈表面质量机器视觉检测机的系统组成和自动检测工作流程基础上,设计出检测机的气动驱动系统和PLC集成电气控制系统,给出PLC控制程序流程.通过PLC集成机器视觉、气动驱动和步进电机驱动控制系统,实现陶瓷套圈外圆表面缺陷机器视觉检测自动化,自动检测节拍达到2秒/件.%This paper firstly analyzes the mechanical system principle and technological process of the vision detection machine for ceramic sleeve surface quality inspection, and then designs a pneumatic drive system and PLC control system for the machine finally programs a PLC control program to implement the integrating control of machine vision, pneumatic drive system and stepping motor. The automatic cycle time of vision detection implements is 2 sec / piece.

  13. Current breathomics-a review on data pre-processing techniques and machine learning in metabolomics breath analysis

    DEFF Research Database (Denmark)

    Smolinska, A.; Hauschild, A. C.; Fijten, R. R. R.

    2014-01-01

    been extensively developed. Yet, the application of machine learning methods for fingerprinting VOC profiles in the breathomics is still in its infancy. Therefore, in this paper, we describe the current state of the art in data pre-processing and multivariate analysis of breathomics data. We start...... different conditions (e.g. disease stage, treatment). Independently of the utilized analytical method, the most important question, 'which VOCs are discriminatory?', remains the same. Answers can be given by several modern machine learning techniques (multivariate statistics) and, therefore, are the focus...

  14. Advancing Research in Second Language Writing through Computational Tools and Machine Learning Techniques: A Research Agenda

    Science.gov (United States)

    Crossley, Scott A.

    2013-01-01

    This paper provides an agenda for replication studies focusing on second language (L2) writing and the use of natural language processing (NLP) tools and machine learning algorithms. Specifically, it introduces a range of the available NLP tools and machine learning algorithms and demonstrates how these could be used to replicate seminal studies…

  15. Advancing Research in Second Language Writing through Computational Tools and Machine Learning Techniques: A Research Agenda

    Science.gov (United States)

    Crossley, Scott A.

    2013-01-01

    This paper provides an agenda for replication studies focusing on second language (L2) writing and the use of natural language processing (NLP) tools and machine learning algorithms. Specifically, it introduces a range of the available NLP tools and machine learning algorithms and demonstrates how these could be used to replicate seminal studies…

  16. Technique of performing construction works by machines with hybrid: manual and remote control

    Directory of Open Access Journals (Sweden)

    Sevryugina Nadezhda

    2017-01-01

    Full Text Available The article discusses issues dealing with efficiency of construction work mechanization. It offers a mathematical model for assessment of mutual influence between the members of the ‘construction site-machine-operator’ system triad, that can give a quantitative assessment of how the efficiency of a technological task varies with more comprehensive use of operational capacities of the machine, while lower effect that limiting parameters of production environment and technical condition of the machine have on the operator. The article contains a constructive remote control solution for upgrade of the base machine. It describes the conditions for using the machines with hybrid: manual and remote control at construction sites. There is also an imitation model of operator’s scanning pattern and data experimental research that prove the efficiency of remotely controlled technological operations. The article proves that lower psychological load on the operator and better comfort contribute to positive economic effect and higher quality of the construction process.

  17. A vision-based driver nighttime assistance and surveillance system based on intelligent image sensing techniques and a heterogamous dual-core embedded system architecture.

    Science.gov (United States)

    Chen, Yen-Lin; Chiang, Hsin-Han; Chiang, Chuan-Yen; Liu, Chuan-Ming; Yuan, Shyan-Ming; Wang, Jenq-Haur

    2012-01-01

    This study proposes a vision-based intelligent nighttime driver assistance and surveillance system (VIDASS system) implemented by a set of embedded software components and modules, and integrates these modules to accomplish a component-based system framework on an embedded heterogamous dual-core platform. Therefore, this study develops and implements computer vision and sensing techniques of nighttime vehicle detection, collision warning determination, and traffic event recording. The proposed system processes the road-scene frames in front of the host car captured from CCD sensors mounted on the host vehicle. These vision-based sensing and processing technologies are integrated and implemented on an ARM-DSP heterogamous dual-core embedded platform. Peripheral devices, including image grabbing devices, communication modules, and other in-vehicle control devices, are also integrated to form an in-vehicle-embedded vision-based nighttime driver assistance and surveillance system.

  18. Machine vision: an incremental learning system based on features derived using fast Gabor transforms for the identification of textural objects

    Science.gov (United States)

    Clark, Richard M.; Adjei, Osei; Johal, Harpal

    2001-11-01

    This paper proposes a fast, effective and also very adaptable incremental learning system for identifying textures based on features extracted from Gabor space. The Gabor transform is a useful technique for feature extraction since it exhibits properties that are similar to biologically visual sensory systems such as those found in the mammalian visual cortex. Although two-dimensional Gabor filters have been applied successfully to a variety of tasks such as text segmentation, object detection and fingerprint analysis, the work of this paper extends previous work by incorporating incremental learning to facilitate easier training. The proposed system transforms textural images into Gabor space and a non-linear threshold function is then applied to extract feature vectors that bear signatures of the textural images. The mean and variance of each training group is computed followed by a technique that uses the Kohonen network to cluster these features. The centers of these clusters form the basis of an incremental learning paradigm that allows new information to be integrated into the existing knowledge. A number of experiments are conducted for real-time identification or discrimination of textural images.

  19. [Influence of milking technique, milking hygiene and environmental hygiene parameters on the microbial contamination of milking machines].

    Science.gov (United States)

    Feldmann, M; Zimmermann, A; Hoedemaker, M

    2006-07-01

    It was the aim of this study to investigate the effect of various factors of the milking technique, milking hygiene and environment on microbial contamination of the milking machine. In 31 dairy herds, the degree of bacterial contamination was examined by taking swabs at four locations (teat cup liner, claw, short and long milk tube) before the milking procedure was started using a standardized protocol (DIN ISO 6887-1:1999). Furthermore, the total germ count was determined in the first milk entering the bulk tank as well as in the bulk tank milk following milking. For each farm, the quality of the milking process and the condition of the milking machine as well as of various environmental factors were recorded. A subjective evaluation of the status of the milking cluster or other parts of the milking machine ("good" or "moderate-poor") gave more information about bacterial contamination than the determination of age and type of material used. A temperature of the rinsing water of teat cleaning before milking or of postmilking teat disinfection did not affect the contamination of the milking machine and the bulk tank milk with environmental bacteria. Furthermore, type of bedding material affected bacterial contamination of milking clusters and bulk tank milk. In conclusion, our results suggest that the microbial contamination of the milking machine is not only influenced by the sanitation pro-

  20. The Bottle of Beverage Label Detection Device Based on Machine Vision%基于机器视觉的饮料瓶标签检测设备

    Institute of Scientific and Technical Information of China (English)

    张树君; 辛莹莹; 陈大千

    2014-01-01

    In the packaging testing industry, consumers pay attention to the quality of the products.How to realize the bottles and efficient full label detection is the important problems facing with the beverage industry. Basing on the above problem, The company developed to test the beverage bottle label of special equipment, the control system by TM258LD42DT,the main characteristic is to be able to elaborate testing (for example the tag of beverage bottle high or low, the tag such as crease, labels), and the equipment of detection have the high efficiency (its detection precision can be achieved 4 mm × 4 mm). This machine has the following functions, for example, the machine-vision detection for tag,the historical data display,the abnormal situation alarm and taking out of the unqualified bottle. And these functions used in the high-speed automatic production line , replace the artificial detection, improve the quality of the testing efficiency and testing,provide the basis for developing the more superior performance model.%在包装检测行业,产品的质量引起了广大消费者的关注。如何实现饮料瓶高效的全标签检测是饮料行业面临的重要问题。基于上述问题,研发了对饮料瓶标签进行检测的专用设备,其控制系统采用的是TM258LD42DT,主要特点是能够精细的检测缺陷(如饮料瓶上标签的高低、标签的破损、标签的折皱等现象),而且设备的检测效率高(其检测精度可达到4 mm×4 mm)。它是集机器视觉的标签检测、历史数据显示、异常情况报警和不合格瓶子的剔除等功能为一体,运用在高速自动化生产线上,代替了人工检测,提高了检测的效率和检测的质量,为开发性能更优越的机型提供基础。

  1. Laser machining of advanced materials

    CERN Document Server

    Dahotre, Narendra B

    2011-01-01

    Advanced materialsIntroductionApplicationsStructural ceramicsBiomaterials CompositesIntermetallicsMachining of advanced materials IntroductionFabrication techniquesMechanical machiningChemical Machining (CM)Electrical machiningRadiation machining Hybrid machiningLaser machiningIntroductionAbsorption of laser energy and multiple reflectionsThermal effectsLaser machining of structural ceramicsIntrodu

  2. Automatic Defect Inspection of PCB Bare Board Based on Machine Vision%基于机器视觉PCB裸板缺陷自动检测研究

    Institute of Scientific and Technical Information of China (English)

    刘百芬; 李海文; 张姝颖; 林德欣

    2014-01-01

    AppIying to the method of reference comparison to automatic defect inspection of PCB bare board based on machine vision.Camera captures muItipIe standard PCB image and caIcuIate its average gray get standard circuit board image tn the same position,image registration compIeted by standard PCB image under test PCB image's corner detection and cor-ner registration,adopting to standard PCB image under test PCB image adopt gray-scaIe transformation,fiItering,binarization, XOR and other image processing respectiveIy to detect the position of the defect area.%运用参考比较法对机器视觉PCB裸板缺陷检测进行了研究。在相机摄像头下同一位置采集多幅标准PCB图像累加求平均值得到标准电路板图像,运用Harris角点算法进行标准电路板图像和待测电路板图像的配准,分别对标准电路板图像和待测电路板图像进行灰度变换、中值滤波、二值化、异或等图像处理检测出缺陷区域,然后通过形态学消除伪缺陷,实验证明,该检测方法有较高的准确率。

  3. Application of machine vision in smartphone based on OpenCV%基于OpenCV的机器视觉在智能手机中的应用

    Institute of Scientific and Technical Information of China (English)

    何鹏; 王连鹏; 楚艳红

    2011-01-01

    For the features of competing mainly in software in the smart phone market, the machine vision technology is introduced to the smart phone based on embedded Linux. The hand signal recognition control application procedure based on OpenCV is studied and realized. Part of the decision started music player with the hand signal to regularly to carry on the discussion, and the program expansion interfaces can implement more different gestures to control the different actions. The experiment indicated that this system meets the realtime processing need, and the movement is stable. This feature of contactless controlling smart phone are both practical and avant-garde, and it makes the smart phones based on Linux more attractive, more broad prospects for development.%针对智能手机市场竞争中主要力拼软件的特点,将机器视觉技术引入以Linux为操作系统的智能手机中,基于OpenCV研究并实现了手势识别控制应用程序.系统决策实现的部分是以手势来启动音乐播放程序进行讨论,而且程序留有扩展接口可以实现更多不同的手势来控制不同的操作.实验结果表明,该系统满足实时处理需求,运行稳定,这种非接触式控制智能手机操作的功能既实用又前卫,使Linux操作系统的智能手机更具吸引力,发展前景更广阔.

  4. 基于机器视觉的点餐自动提示器设计%Design for automatic ordering prompter based on machine vision

    Institute of Scientific and Technical Information of China (English)

    陈善为; 余建安; 邵梦甜; 李萍; 王小梅

    2015-01-01

    According to blinding consumption and unhealthy diet, this thesis puts forward the automatic ordering prompter system based on machine vision;Starting with the key software and hardware technology from the composition of this system, based on the Design of light source, the selection of lens and image acquisition card and image processing control system integrated, this thesis analyzes the working principle of the system, designs the principle structure of the system and prototype designs;Finally, through the analysis of the data from the practice application, showing that the design is helpful to realize the expected goal of the scientific meal and civilized dining and showing that the design has a certain application prospect on the premise of cost control.%针对在外就餐中的盲目消费,不健康饮食等问题,提出基于机器视觉的点餐自动提示器系统;从组成系统的关键软硬件技术入手,基于光源设计、镜头及图像采集卡的选择、图像处理控制系统的设计集成,分析了系统的工作原理,设计了系统的原理结构,并在此基础上进行了原型设计;最后通过对原型系统实践应用得到的数据分析,表明该设计有助于实现科学点餐,文明就餐的预期目标,在成本得到进一步控制的前提下具有一定的应用前景。

  5. Automatic Detecting a Bunch of Cash Based on Machine Vision Systems%基于机器视觉的智能卡把系统

    Institute of Scientific and Technical Information of China (English)

    张海宁; 许飞; 冯晓岗

    2012-01-01

    卡把是指对捆钱的把数进行数目的核定.目前的卡把操作是人工的,不仅费时,而且可能出现误判.基于机器视觉的卡把系统通过摄像机取像,将被摄取的目标信号转换成图像信号,并将此图像信号传送给专用的图像处理系统,抽取图像的特征,进而根据判别的结果来控制智能设备的执行相应的操作.本系统能够自动进行智能卡把操作,并对不符合规范的钱捆进行报警和处理.不仅提高了卡把速度,而且极大降低了卡把的误判率.%Detecting a bunch of cash is refers to approve the number of money. Recently, this operation is manual, which not only takes time, but also appears a miscalculation. Automatic detecting a bunch of cash based on machine vision system gets the picture through the camera, makes the target signal convert into image signal, and takes the image signals lo the dedicated image processing system, extracts the image characteristics, and then controls the equipment to do the corresponding operation according to the result of discrimination. Tliis system can be automatically detected a bunch of cash, and alarms and handles money which is not up to standard, which not only improves the speed, and greatly reduces the false rate.

  6. Machine Learning Techniques for the Detection of Shockable Rhythms in Automated External Defibrillators

    Science.gov (United States)

    Irusta, Unai; Morgado, Eduardo; Aramendi, Elisabete; Ayala, Unai; Wik, Lars; Kramer-Johansen, Jo; Eftestøl, Trygve; Alonso-Atienza, Felipe

    2016-01-01

    Early recognition of ventricular fibrillation (VF) and electrical therapy are key for the survival of out-of-hospital cardiac arrest (OHCA) patients treated with automated external defibrillators (AED). AED algorithms for VF-detection are customarily assessed using Holter recordings from public electrocardiogram (ECG) databases, which may be different from the ECG seen during OHCA events. This study evaluates VF-detection using data from both OHCA patients and public Holter recordings. ECG-segments of 4-s and 8-s duration were analyzed. For each segment 30 features were computed and fed to state of the art machine learning (ML) algorithms. ML-algorithms with built-in feature selection capabilities were used to determine the optimal feature subsets for both databases. Patient-wise bootstrap techniques were used to evaluate algorithm performance in terms of sensitivity (Se), specificity (Sp) and balanced error rate (BER). Performance was significantly better for public data with a mean Se of 96.6%, Sp of 98.8% and BER 2.2% compared to a mean Se of 94.7%, Sp of 96.5% and BER 4.4% for OHCA data. OHCA data required two times more features than the data from public databases for an accurate detection (6 vs 3). No significant differences in performance were found for different segment lengths, the BER differences were below 0.5-points in all cases. Our results show that VF-detection is more challenging for OHCA data than for data from public databases, and that accurate VF-detection is possible with segments as short as 4-s. PMID:27441719

  7. 采棉机视觉导航路线图像检测方法%Detection for navigation route for cotton harvester based on machine vision

    Institute of Scientific and Technical Information of China (English)

    李景彬; 陈兵旗; 刘阳; 查涛

    2013-01-01

    Auto-navigation has a great significance in increasing the operating quality and production efficiency of agriculture machinery, such as improving the working environment and security situation for workers, reducing the labor intensity, etc. The vision navigation has many technical advantages that it can adapt to the complicated field of the operating environment, has wide detection range and has rich and complete information. It is the research focus in the field of agriculture machinery auto-navigation. How to extract routes fast, accurately, and effectively in the natural environment is the key in vision navigation. The vision navigation routes’ detect of a Cotton-picker is the main premise to achieve its auto-navigation. There are many difficulties in detecting the operation routes of a cotton-picker in the field. For example, during harvest, there are many kinds of target features like stalks, cotton, bolls, leaves, weeds in the cotton field and its outside region. In addition, a little cotton may be left on the stalks in the harvested region when we use machine to pick. This paper puts forward the detection algorithms of the operation routes of a cotton-picker, the edge and the end of the cotton field by analyzing the different color features of the harvested region, the un-harvested region, the outside region, and the end of the field. First, we used the difference of color components, such as 3B-R-G, |R-G|, |R-B| and |G-B| to extract the target features of the inner and outside of the cotton field respectively, and smooth the image using the moving average method by the set length. Then by finding the rose critical point of the crest that is based on the lowest trough point to the un-harvested region, and connecting with the line detect result of the previous frame, we determine the alternate point group of a straight line transform. At last, we extracted the navigation routes based on Passing a Known Point Hough Transform (PKPHT). The test proves that

  8. Results of error correction techniques applied on two high accuracy coordinate measuring machines

    Energy Technology Data Exchange (ETDEWEB)

    Pace, C.; Doiron, T.; Stieren, D.; Borchardt, B.; Veale, R. (Sandia National Labs., Albuquerque, NM (USA); National Inst. of Standards and Technology, Gaithersburg, MD (USA))

    1990-01-01

    The Primary Standards Laboratory at Sandia National Laboratories (SNL) and the Precision Engineering Division at the National Institute of Standards and Technology (NIST) are in the process of implementing software error correction on two nearly identical high-accuracy coordinate measuring machines (CMMs). Both machines are Moore Special Tool Company M-48 CMMs which are fitted with laser positioning transducers. Although both machines were manufactured to high tolerance levels, the overall volumetric accuracy was insufficient for calibrating standards to the levels both laboratories require. The error mapping procedure was developed at NIST in the mid 1970's on an earlier but similar model. The error mapping procedure was originally very complicated and did not make any assumptions about the rigidness of the machine as it moved, each of the possible error motions was measured at each point of the error map independently. A simpler mapping procedure was developed during the early 1980's which assumed rigid body motion of the machine. This method has been used to calibrate lower accuracy machines with a high degree of success and similar software correction schemes have been implemented by many CMM manufacturers. The rigid body model has not yet been used on highly repeatable CMMs such as the M48. In this report we present early mapping data for the two M48 CMMs. The SNL CMM was manufactured in 1985 and has been in service for approximately four years, whereas the NIST CMM was delivered in early 1989. 4 refs., 5 figs.

  9. Design on special robot used for colliery based on machine vision and independent suspension system%基于机器视觉和独立悬挂系统的煤矿特种机器人设计

    Institute of Scientific and Technical Information of China (English)

    赵建伟; 班钰; 王俊懿

    2016-01-01

    由于煤矿井下煤尘存在爆炸性、煤层中溢出的瓦斯也具有威胁性,所以采煤工作环境十分恶劣和危险。为此利用 LabVIEW 和 IMAQ Vision 构建了基于彩色图像二值化的机器视觉图像处理算法,提出了一种基于机器视觉和独立悬挂系统的煤矿特种机器人。该机器人四轮能够独立运动,同时兼备灵活的探测抓取和越障能力,应用于煤矿勘探以及利用视觉进行物体识别方面,并通过抓球和爬楼梯试验验证了该机构的可行性。%In view of severe and dangerous surroundings of coal mining operation, the paper applied LabVIEW and IMAQ Vision to construct the machine vision image processing algorithm based on color image binarization, and proposed a special robot used for colliery based on machine vision and independent suspension system. The four wheels of the robot moved independently, and the robot possessed flexible grasping ability and obstacle climbing ability. It could be applied to colliery exploration and object recognition via vision. Finally, the robot proved feasible via ball grasping and stair climbing test.

  10. In vitro biological characterization of macroporous 3D Bonelike structures prepared through a 3D machining technique

    Energy Technology Data Exchange (ETDEWEB)

    Laranjeira, M.S.; Dias, A.G. [INEB - Instituto de Engenharia Biomedica, Divisao de Biomateriais, Universidade do Porto, Rua do Campo Alegre, 823, 4150-180 Porto (Portugal); Santos, J.D. [INEB - Instituto de Engenharia Biomedica, Divisao de Biomateriais, Universidade do Porto, Rua do Campo Alegre, 823, 4150-180 Porto (Portugal); Universidade do Porto, Faculdade de Engenharia, Departamento de Engenharia Metalurgica e Materiais, Rua Dr. Roberto Frias, 4200-465 Porto - Portugal (Portugal); Fernandes, M.H., E-mail: mhrf@portugalmail.pt [Universidade do Porto, Faculdade de Medicina Dentaria, Laboratorio de Farmacologia e Biocompatibilidade Celular, Rua Dr. Manuel Pereira da Silva, 4200-392 Porto (Portugal)

    2009-04-30

    3D bioactive macroporous structures were prepared using a 3D machining technique. A virtual 3D structure model was created and a computer numerically controlled (CNC) milling device machined Bonelike samples. The resulting structures showed a reproducible macroporosity and interconnective structure. Macropores size after sintering was approximately 2000 {mu}m. In vitro testing using human bone marrow stroma showed that cells were able to adhere and proliferate on 3D structures surface and migrate into all macropore channels. In addition, these cells were able to differentiate, since mineralized globular structures associated with cell layer were identified. Results obtained showed that 3D structures of Bonelike successfully allow cell migration into all macropores, and allow human bone marrow stromal cells to proliferate and differentiate. This innovative technique may be considered as a step-forward preparation for 3D interconnective macroporous structures that allow bone ingrowth while maintaining mechanical integrity.

  11. Modelling and Calibration Technique of Laser Triangulation Sensors for Integration in Robot Arms and Articulated Arm Coordinate Measuring Machines

    Directory of Open Access Journals (Sweden)

    Juan J. Aguilar

    2009-09-01

    Full Text Available A technique for intrinsic and extrinsic calibration of a laser triangulation sensor (LTS integrated in an articulated arm coordinate measuring machine (AACMM is presented in this paper. After applying a novel approach to the AACMM kinematic parameter identification problem, by means of a single calibration gauge object, a one-step calibration method to obtain both intrinsic―laser plane, CCD sensor and camera geometry―and extrinsic parameters related to the AACMM main frame has been developed. This allows the integration of LTS and AACMM mathematical models without the need of additional optimization methods after the prior sensor calibration, usually done in a coordinate measuring machine (CMM before the assembly of the sensor in the arm. The experimental tests results for accuracy and repeatability show the suitable performance of this technique, resulting in a reliable, quick and friendly calibration method for the AACMM final user. The presented method is also valid for sensor integration in robot arms and CMMs.

  12. Modelling and calibration technique of laser triangulation sensors for integration in robot arms and articulated arm coordinate measuring machines.

    Science.gov (United States)

    Santolaria, Jorge; Guillomía, David; Cajal, Carlos; Albajez, José A; Aguilar, Juan J

    2009-01-01

    A technique for intrinsic and extrinsic calibration of a laser triangulation sensor (LTS) integrated in an articulated arm coordinate measuring machine (AACMM) is presented in this paper. After applying a novel approach to the AACMM kinematic parameter identification problem, by means of a single calibration gauge object, a one-step calibration method to obtain both intrinsic-laser plane, CCD sensor and camera geometry-and extrinsic parameters related to the AACMM main frame has been developed. This allows the integration of LTS and AACMM mathematical models without the need of additional optimization methods after the prior sensor calibration, usually done in a coordinate measuring machine (CMM) before the assembly of the sensor in the arm. The experimental tests results for accuracy and repeatability show the suitable performance of this technique, resulting in a reliable, quick and friendly calibration method for the AACMM final user. The presented method is also valid for sensor integration in robot arms and CMMs.

  13. Vision-Based Finger Detection, Tracking, and Event Identification Techniques for Multi-Touch Sensing and Display Systems

    Directory of Open Access Journals (Sweden)

    Yang-Lang Chang

    2011-07-01

    Full Text Available This study presents efficient vision-based finger detection, tracking, and event identification techniques and a low-cost hardware framework for multi-touch sensing and display applications. The proposed approach uses a fast bright-blob segmentation process based on automatic multilevel histogram thresholding to extract the pixels of touch blobs obtained from scattered infrared lights captured by a video camera. The advantage of this automatic multilevel thresholding approach is its robustness and adaptability when dealing with various ambient lighting conditions and spurious infrared noises. To extract the connected components of these touch blobs, a connected-component analysis procedure is applied to the bright pixels acquired by the previous stage. After extracting the touch blobs from each of the captured image frames, a blob tracking and event recognition process analyzes the spatial and temporal information of these touch blobs from consecutive frames to determine the possible touch events and actions performed by users. This process also refines the detection results and corrects for errors and occlusions caused by noise and errors during the blob extraction process. The proposed blob tracking and touch event recognition process includes two phases. First, the phase of blob tracking associates the motion correspondence of blobs in succeeding frames by analyzing their spatial and temporal features. The touch event recognition process can identify meaningful touch events based on the motion information of touch blobs, such as finger moving, rotating, pressing, hovering, and clicking actions. Experimental results demonstrate that the proposed vision-based finger detection, tracking, and event identification system is feasible and effective for multi-touch sensing applications in various operational environments and conditions.

  14. New smart readout technique performing edge detection designed to control vision sensors dataflow

    Science.gov (United States)

    Amhaz, Hawraa; Sicard, Gilles

    2012-03-01

    In this paper, a new readout strategy for CMOS image sensors is presented. It aims to overcome the excessive output dataflow bottleneck; this challenge is becoming more and more crucial along with the technology miniaturization. This strategy is based on the spatial redundancies suppression. It leads the sensor to perform edge detection and eventually provide binary image. One of the main advantages of this readout technique compared to other techniques, existing in the literature, is that it does not affect the in-pixel circuitry. This means that all the analogue processing circuitry is implemented outside the pixel, which keeps the pixel area and Fill Factor unchanged. The main analogue block used in this technique is an event detector developed and designed in the CMOS 0.35μm technology from Austria Micro Systems. The simulation results of this block as well as the simulation results of a test bench composed of several pixels and column amplifiers using this readout mode show the capability of this readout mode to reduce dataflow by controlling the ADCs. We must mention that this readout strategy is applicable on sensors that use a linear operating pixel element as well as for those based on logarithmic operating pixels. This readout technique is emulated by a MATLAB model which gives an idea about the expected functionalities and dataflow reduction rates (DRR). Emulation results are shown lately by giving the pre and post processed images as well as the DRR. This last cited does not have a fix value since it depends on the spatial frequency of the filmed scenes and the chosen threshold value.

  15. Modelling and Calibration Technique of Laser Triangulation Sensors for Integration in Robot Arms and Articulated Arm Coordinate Measuring Machines

    OpenAIRE

    Aguilar, Juan J.; Albajez, José A.; Carlos Cajal; David Guillomía; Jorge Santolaria

    2009-01-01

    A technique for intrinsic and extrinsic calibration of a laser triangulation sensor (LTS) integrated in an articulated arm coordinate measuring machine (AACMM) is presented in this paper. After applying a novel approach to the AACMM kinematic parameter identification problem, by means of a single calibration gauge object, a one-step calibration method to obtain both intrinsic―laser plane, CCD sensor and camera geometry―and extrinsic parameters related to the AACMM main frame has been develope...

  16. The Smart Aerial Release Machine, a Universal System for Applying the Sterile Insect Technique

    Science.gov (United States)

    Mubarqui, Ruben Leal; Perez, Rene Cano; Kladt, Roberto Angulo; Lopez, Jose Luis Zavala; Parker, Andrew; Seck, Momar Talla; Sall, Baba; Bouyer, Jérémy

    2014-01-01

    Background Beyond insecticides, alternative methods to control insect pests for agriculture and vectors of diseases are needed. Management strategies involving the mass-release of living control agents have been developed, including genetic control with sterile insects and biological control with parasitoids, for which aerial release of insects is often required. Aerial release in genetic control programmes often involves the use of chilled sterile insects, which can improve dispersal, survival and competitiveness of sterile males. Currently available means of aerially releasing chilled fruit flies are however insufficiently precise to ensure homogeneous distribution at low release rates and no device is available for tsetse. Methodology/Principal Findings Here we present the smart aerial release machine, a new design by the Mubarqui Company, based on the use of vibrating conveyors. The machine is controlled through Bluetooth by a tablet with Android Operating System including a completely automatic guidance and navigation system (MaxNav software). The tablet is also connected to an online relational database facilitating the preparation of flight schedules and automatic storage of flight reports. The new machine was compared with a conveyor release machine in Mexico using two fruit flies species (Anastrepha ludens and Ceratitis capitata) and we obtained better dispersal homogeneity (% of positive traps, p<0.001) for both species and better recapture rates for Anastrepha ludens (p<0.001), especially at low release densities (<1500 per ha). We also demonstrated that the machine can replace paper boxes for aerial release of tsetse in Senegal. Conclusions/Significance This technology limits damages to insects and allows a large range of release rates from 10 flies/km2 for tsetse flies up to 600 000 flies/km2 for fruit flies. The potential of this machine to release other species like mosquitoes is discussed. Plans and operating of the machine are provided to allow its

  17. The smart aerial release machine, a universal system for applying the sterile insect technique.

    Directory of Open Access Journals (Sweden)

    Ruben Leal Mubarqui

    Full Text Available Beyond insecticides, alternative methods to control insect pests for agriculture and vectors of diseases are needed. Management strategies involving the mass-release of living control agents have been developed, including genetic control with sterile insects and biological control with parasitoids, for which aerial release of insects is often required. Aerial release in genetic control programmes often involves the use of chilled sterile insects, which can improve dispersal, survival and competitiveness of sterile males. Currently available means of aerially releasing chilled fruit flies are however insufficiently precise to ensure homogeneous distribution at low release rates and no device is available for tsetse.Here we present the smart aerial release machine, a new design by the Mubarqui Company, based on the use of vibrating conveyors. The machine is controlled through Bluetooth by a tablet with Android Operating System including a completely automatic guidance and navigation system (MaxNav software. The tablet is also connected to an online relational database facilitating the preparation of flight schedules and automatic storage of flight reports. The new machine was compared with a conveyor release machine in Mexico using two fruit flies species (Anastrepha ludens and Ceratitis capitata and we obtained better dispersal homogeneity (% of positive traps, p<0.001 for both species and better recapture rates for Anastrepha ludens (p<0.001, especially at low release densities (<1500 per ha. We also demonstrated that the machine can replace paper boxes for aerial release of tsetse in Senegal.This technology limits damages to insects and allows a large range of release rates from 10 flies/km2 for tsetse flies up to 600,000 flies/km2 for fruit flies. The potential of this machine to release other species like mosquitoes is discussed. Plans and operating of the machine are provided to allow its use worldwide.

  18. Online grading method for tissue culture seedlings ofSpathiphyllum floribundum based on machine vision%基于机器视觉的白掌组培苗在线分级方法

    Institute of Scientific and Technical Information of China (English)

    杨意; 初麒; 杨艳丽; 张祥接; 徐祥朋; 辜松

    2016-01-01

    白掌在观叶类花卉中占有很大比例,其育苗多采用组织栽培法,且组培苗生产具有规模化。为提高成苗出苗品质,需要在组培苗炼苗前对其分级,而目前常用分级法不能有效解决自然状态下水平放置的白掌组培苗存在的叶片扭曲和重叠问题,因此该文提出一种基于机器视觉实现白掌组培苗在线分级的方法,通过对自然状态下水平放置的白掌组培苗的叶片面积、苗高、地径以及投影面积的分析,得到其投影面积与叶片面积呈线性关系,相关度为0.9344;投影面积与地径呈多项式函数关系,相关性为0.9067,故确定组培苗投影面积和苗高为实际生产中的分级指标。该文采用基于颜色模板匹配算法测量组培苗投影面积,得到的叶片面积和地径与实际叶片面积和地径的变异系数相对误差分别为0.35%和7.95%;利用最小外接矩形法(MBR,minimum bounding rectangle)测量苗高,得到的苗高和实际苗高变异系数相对误差为1.44%。通过整机分级试验发现在输送间距为0.25 m,输送速度为0.5 m/s,分级级别为3级的条件下,该分级装置的分级成功率可达96%,对应生产率为7200株/h。%At present, most of young plants ofSpathiphyllum floribundum are breeding by the technique of tissue culture. Due to absence of grading machine specially designed for primary-growth plants that is small, irregular and young, the grading of tissue culture seedlings are normally handled manually. In this paper, we proposed an automated online grading method for Spathiphyllum floribundum tissue culture seedlings based on the technique of machine vision. SinceSpathiphyllum floribundum is a foliage flower, the leaf area is one of the most important parameters in grading, along with seedling height and diameter. Direct measurement not only would do damage to young plant because of its tenderness, but also the manpower productivity

  19. Moving beyond regression techniques in cardiovascular risk prediction: applying machine learning to address analytic challenges.

    Science.gov (United States)

    Goldstein, Benjamin A; Navar, Ann Marie; Carter, Rickey E

    2016-07-19

    Risk prediction plays an important role in clinical cardiology research. Traditionally, most risk models have been based on regression models. While useful and robust, these statistical methods are limited to using a small number of predictors which operate in the same way on everyone, and uniformly throughout their range. The purpose of this review is to illustrate the use of machine-learning methods for development of risk prediction models. Typically presented as black box approaches, most machine-learning methods are aimed at solving particular challenges that arise in data analysis that are not well addressed by typical regression approaches. To illustrate these challenges, as well as how different methods can address them, we consider trying to predicting mortality after diagnosis of acute myocardial infarction. We use data derived from our institution's electronic health record and abstract data on 13 regularly measured laboratory markers. We walk through different challenges that arise in modelling these data and then introduce different machine-learning approaches. Finally, we discuss general issues in the application of machine-learning methods including tuning parameters, loss functions, variable importance, and missing data. Overall, this review serves as an introduction for those working on risk modelling to approach the diffuse field of machine learning.

  20. Application of Assistive Computer Vision Methods to Oyama Karate Techniques Recognition

    Directory of Open Access Journals (Sweden)

    Tomasz Hachaj

    2015-09-01

    Full Text Available In this paper we propose a novel algorithm that enables online actions segmentation and classification. The algorithm enables segmentation from an incoming motion capture (MoCap data stream, sport (or karate movement sequences that are later processed by classification algorithm. The segmentation is based on Gesture Description Language classifier that is trained with an unsupervised learning algorithm. The classification is performed by continuous density forward-only hidden Markov models (HMM classifier. Our methodology was evaluated on a unique dataset consisting of MoCap recordings of six Oyama karate martial artists including multiple champion of Kumite Knockdown Oyama karate. The dataset consists of 10 classes of actions and included dynamic actions of stands, kicks and blocking techniques. Total number of samples was 1236. We have examined several HMM classifiers with various number of hidden states and also Gaussian mixture model (GMM classifier to empirically find the best setup of the proposed method in our dataset. We have used leave-one-out cross validation. The recognition rate of our methodology differs between karate techniques and is in the range of 81% ± 15% even to 100%. Our method is not limited for this class of actions but can be easily adapted to any other MoCap-based actions. The description of our approach and its evaluation are the main contributions of this paper. The results presented in this paper are effects of pioneering research on online karate action classification.

  1. Development of the railway freight log scaling system based on machine vision%基于机器视觉的铁路货运木材检尺系统开发

    Institute of Scientific and Technical Information of China (English)

    王纪武; 高伟杰; 廖方波; 李建勇

    2012-01-01

    利用三角测量原理,采用图像处理技术,解决了基于机器视觉木材检尺技术的难点问题.实验结果表明:开发的检尺系统可直接确定目标面到相机镜头间的距离,并且能够与机器视觉检尺无缝融合,与其他测距传感器相比,该系统成本低,不仅适合单根等小批量的木材检尺要求,而且适合大批量的铁路货运木材的检尺工作.%The conventional log scaling, which is done with a tape measure by manual operation in order to get higher accuracy, is carried out by more workers working hard for a longer time. It is difficult 10 realize systematic and scientific management. Based on machine vision, a new log scaling system is developed. The distance between the log-end and camera is calculated directly by the image processing with a point laser according to the principle of triangulation. Moreover, the distance calculation can be done with other image processing simultaneously. With this technique, the developed log scaling system can be used not only for scaling a single log one by one, but also for scaling bundles of logs at the same time.

  2. A High Performance Space Vector Modulation - Direct Torque Controlled Induction Machine Drive based on Stator Flux Orientation Technique

    Directory of Open Access Journals (Sweden)

    BELMADANI, B.

    2009-06-01

    Full Text Available This paper proposes the design and implementation of a novel direct torque controlled induction machine drive system. The control system enjoys the advantages of stator vector control and conventional direct torque control and avoids some of the implementation difficulties of either of the two control methods. The stator vector control principal is used to keep constant the amplitude of stator flux vector at rated value, and to develop the relationship between the machine torque and the rotating speed of the stator flux vector. Thus, the machine torque can be regulated to generate the stator angular speed, which becomes a command signal and permits to overcome the problem of its estimation. Furthermore, with the combined control methods, the reference stator voltage vector can be generated and proportional-integral controllers and space vector modulation technique can be used to obtain fixed switching frequency and low torque ripple. Simulation experiments results indicate that, with the proposed scheme, a precise control of the stator flux and machine torque can be achieved. Compared to conventional direct torque control, presented method is easily implemented, and the steady performances of ripples of both torque and flux are considerably improved.

  3. PSO-based support vector machine with cuckoo search technique for clinical disease diagnoses.

    Science.gov (United States)

    Liu, Xiaoyong; Fu, Hui

    2014-01-01

    Disease diagnosis is conducted with a machine learning method. We have proposed a novel machine learning method that hybridizes support vector machine (SVM), particle swarm optimization (PSO), and cuckoo search (CS). The new method consists of two stages: firstly, a CS based approach for parameter optimization of SVM is developed to find the better initial parameters of kernel function, and then PSO is applied to continue SVM training and find the best parameters of SVM. Experimental results indicate that the proposed CS-PSO-SVM model achieves better classification accuracy and F-measure than PSO-SVM and GA-SVM. Therefore, we can conclude that our proposed method is very efficient compared to the previously reported algorithms.

  4. PSO-Based Support Vector Machine with Cuckoo Search Technique for Clinical Disease Diagnoses

    Directory of Open Access Journals (Sweden)

    Xiaoyong Liu

    2014-01-01

    Full Text Available Disease diagnosis is conducted with a machine learning method. We have proposed a novel machine learning method that hybridizes support vector machine (SVM, particle swarm optimization (PSO, and cuckoo search (CS. The new method consists of two stages: firstly, a CS based approach for parameter optimization of SVM is developed to find the better initial parameters of kernel function, and then PSO is applied to continue SVM training and find the best parameters of SVM. Experimental results indicate that the proposed CS-PSO-SVM model achieves better classification accuracy and F-measure than PSO-SVM and GA-SVM. Therefore, we can conclude that our proposed method is very efficient compared to the previously reported algorithms.

  5. ADAPTING HYBRID MACHINE TRANSLATION TECHNIQUES FOR CROSS-LANGUAGE TEXT RETRIEVAL SYSTEM

    Directory of Open Access Journals (Sweden)

    P. ISWARYA

    2017-03-01

    Full Text Available This research work aims in developing Tamil to English Cross - language text retrieval system using hybrid machine translation approach. The hybrid machine translation system is a combination of rule based and statistical based approaches. In an existing word by word translation system there are lot of issues and some of them are ambiguity, Out-of-Vocabulary words, word inflections, and improper sentence structure. To handle these issues, proposed architecture is designed in such a way that, it contains Improved Part-of-Speech tagger, machine learning based morphological analyser, collocation based word sense disambiguation procedure, semantic dictionary, and tense markers with gerund ending rules, and two pass transliteration algorithm. From the experimental results it is clear that the proposed Tamil Query based translation system achieves significantly better translation quality over existing system, and reaches 95.88% of monolingual performance.

  6. Accuracy comparison among different machine learning techniques for detecting malicious codes

    Science.gov (United States)

    Narang, Komal

    2016-03-01

    In this paper, a machine learning based model for malware detection is proposed. It can detect newly released malware i.e. zero day attack by analyzing operation codes on Android operating system. The accuracy of Naïve Bayes, Support Vector Machine (SVM) and Neural Network for detecting malicious code has been compared for the proposed model. In the experiment 400 benign files, 100 system files and 500 malicious files have been used to construct the model. The model yields the best accuracy 88.9% when neural network is used as classifier and achieved 95% and 82.8% accuracy for sensitivity and specificity respectively.

  7. Engagement techniques and playing level impact the biomechanical demands on rugby forwards during machine-based scrummaging.

    Science.gov (United States)

    Preatoni, Ezio; Stokes, Keith A; England, Michael E; Trewartha, Grant

    2015-04-01

    This cross-sectional study investigated the factors that may influence the physical loading on rugby forwards performing a scrum by studying the biomechanics of machine-based scrummaging under different engagement techniques and playing levels. 34 forward packs from six playing levels performed repetitions of five different types of engagement techniques against an instrumented scrum machine under realistic training conditions. Applied forces and body movements were recorded in three orthogonal directions. The modification of the engagement technique altered the load acting on players. These changes were in a similar direction and of similar magnitude irrespective of the playing level. Reducing the dynamics of the initial engagement through a fold-in procedure decreased the peak compression force, the peak downward force and the engagement speed in excess of 30%. For example, peak compression (horizontal) forces in the professional teams changed from 16.5 (baseline technique) to 8.6 kN (fold-in procedure). The fold-in technique also reduced the occurrence of combined high forces and head-trunk misalignment during the absorption of the impact, which was used as a measure of potential hazard, by more than 30%. Reducing the initial impact did not decrease the ability of the teams to produce sustained compression forces. De-emphasising the initial impact against the scrum machine decreased the mechanical stresses acting on forward players and may benefit players' welfare by reducing the hazard factors that may induce chronic degeneration of the spine. Published by the BMJ Publishing Group Limited. For permission to use (where not already granted under a licence) please go to http://group.bmj.com/group/rights-licensing/permissions.

  8. Applying machine learning and image feature extraction techniques to the problem of cerebral aneurysm rupture

    Directory of Open Access Journals (Sweden)

    Steren Chabert

    2017-01-01

    Full Text Available Cerebral aneurysm is a cerebrovascular disorder characterized by a bulging in a weak area in the wall of an artery that supplies blood to the brain. It is relevant to understand the mechanisms leading to the apparition of aneurysms, their growth and, more important, leading to their rupture. The purpose of this study is to study the impact on aneurysm rupture of the combination of different parameters, instead of focusing on only one factor at a time as is frequently found in the literature, using machine learning and feature extraction techniques. This discussion takes relevance in the context of the complex decision that the physicians have to take to decide which therapy to apply, as each intervention bares its own risks, and implies to use a complex ensemble of resources (human resources, OR, etc. in hospitals always under very high work load. This project has been raised in our actual working team, composed of interventional neuroradiologist, radiologic technologist, informatics engineers and biomedical engineers, from Valparaiso public Hospital, Hospital Carlos van Buren, and from Universidad de Valparaíso – Facultad de Ingeniería and Facultad de Medicina. This team has been working together in the last few years, and is now participating in the implementation of an “interdisciplinary platform for innovation in health”, as part of a bigger project leaded by Universidad de Valparaiso (PMI UVA1402. It is relevant to emphasize that this project is made feasible by the existence of this network between physicians and engineers, and by the existence of data already registered in an orderly manner, structured and recorded in digital format. The present proposal arises from the description in nowadays literature that the actual indicators, whether based on morphological description of the aneurysm, or based on characterization of biomechanical factor or others, these indicators were shown not to provide sufficient information in order

  9. Inter- and intraspecific diversity in Cistus L. (Cistaceae) seeds, analysed with computer vision techniques.

    Science.gov (United States)

    Lo Bianco, M; Grillo, O; Cañadas, E; Venora, G; Bacchetta, G

    2017-03-01

    This work aims to discriminate among different species of the genus Cistus, using seed parameters and following the scientific plant names included as accepted in The Plant List. Also, the intraspecific phenotypic differentiation of C. creticus, through comparison with three subspecies (C. creticus subsp. creticus, C. c. subsp. eriocephalus and C. c. subsp. corsicus), as well as the interpopulation variability among five C. creticus subsp. eriocephalus populations was evaluated. Seed mean weight and 137 morphocolorimetric quantitative variables, describing shape, size, colour and textural seed traits, were measured using image analysis techniques. Measured data were analysed applying step-wise linear discriminant analysis. An overall cross-validated classification performance of 80.6% was recorded at species level. With regard to C. creticus, as case study, percentages of correct discrimination of 96.7% and 99.6% were achieved at intraspecific and interpopulation levels, respectively. In this classification model, the relevance of the colorimetric and textural descriptive features was highlighted, as well as the seed mean weight, which was the most discriminant feature at specific and intraspecific level. These achievements proved the ability of the image analysis system as highly diagnostic for systematic purposes and confirm that seeds in the genus Cistus have important diagnostic value. © 2016 German Botanical Society and The Royal Botanical Society of the Netherlands.

  10. Computer vision for real-time orbital operations. Center directors discretionary fund

    Science.gov (United States)

    Vinz, F. L.; Brewster, L. L.; Thomas, L. D.

    1984-01-01

    Machine vision research is examined as it relates to the NASA Space Station program and its associated Orbital Maneuvering Vehicle (OMV). Initial operation of OMV for orbital assembly, docking, and servicing are manually controlled from the ground by means of an on board TV camera. These orbital operations may be accomplished autonomously by machine vision techniques which use the TV camera as a sensing device. Classical machine vision techniques are described. An alternate method is developed and described which employs a syntactic pattern recognition scheme. It has the potential for substantial reduction of computing and data storage requirements in comparison to the Two-Dimensional Fast Fourier Transform (2D FFT) image analysis. The method embodies powerful heuristic pattern recognition capability by identifying image shapes such as elongation, symmetry, number of appendages, and the relative length of appendages.

  11. Polynomial Transfer Lot Sizing Techniques for Batch Processing on Consecutive Machines

    Science.gov (United States)

    1989-09-01

    batch, while still specifying sizable batches? Goldratt , the developer of OPT (Optimized Production Technology) [7; 12, pp. 692-715; 101, answered this...and Jeffrey L Rummel, Batching to Minimize Flow Times on One Machine, Management Science, 33, #6, 1987, pp. 784-799. [71 Goldratt , Eliyahu and Robert

  12. Novel pose measurement for agricultural vehicle guided by machine vision%视觉导航农用车辆相对位姿测量新方法

    Institute of Scientific and Technical Information of China (English)

    周俊; 姬长英; 刘成良

    2006-01-01

    农田环境中农作物大多呈近似直线的行垄分布特点,农用车辆自主视觉导航时通常利用这些景物特征作为跟踪目标.提出了一种计算车辆相对于跟踪目标位姿的新型方法,首先分析了传统算法中存在的计算量大、忽视图像平面中各像素权重不同等缺陷,而后依据跟踪路径局部线性模型假设,详细地推导了算法过程.基于视觉导航原型车辆的试验结果表明,与人工测量值相比,横向距离和航向角的误差均值都等于零,标准差分别为3cm 和0.62deg.%Some agricultural tasks consist of applying chemical fertilizer to crops, but the products are often applied throughout the field in most cases, which cause pollution of water and possible chemical residues. In order to apply the products selectively and reduce the quantity of application, an autonomous vehicle can be used. Generally, this kind of vehicle follows the crop rows autonomously in the field where plants are arranged in rows, so its pose relative to crop row is important for tracking algorithm to work. With the machine vision, a novel method to calculate this pose was demonstrated, which could adapt to the complex characteristics of field environment excellently. First, some shortcomings involved in the conventional measuring method were analyzed carefully, such as processing time being long, pixel weight in the digital image being ignored and so on. With the local linear model of the tracked crop row then the algorithm was deduced at full length. Finally, based on the prototype of autonomous agricultural vehicle, the experiment was carried out, and it was shown that compared with the manual measurement the standard deviation of offset was 3 cm and of heading angle 0.62 deg while without any fixed displacement.

  13. Application of OpenCV in machine vision detection system of cigarette case%OpenCV在条烟视觉检测系统中的应用

    Institute of Scientific and Technical Information of China (English)

    周传宏; 陈郭宝; 王怀虎; 康少博

    2011-01-01

    The cigarette case detection system is a special device, which is installed on the production line in front of packing machine to detect the appearance defect.The system can detect cigarette case in real-time and remove the defective products in time with characteristics of high-speed, efficient and accurate, which is an auto-detection system covering mechanic, optical, electric, computer and communications as well as other technologies.The application of the system greatly improves the automation of the tobacco company;In addition, it also can reduce the labor intensity,improve the working environment and ensure the product quality.The algorithm for common detection items of cigarette case was designed and the open source computer vision library OpenCV of Intel Company was used to improve the detection speed and accuracy in it.%条烟视觉检测系统是安装在条包生产线上,在条包装箱之前,对其外观缺陷进行检测的一种专用设备.系统可以对生产线上的条包进行实时检测,并及时剔除外观有缺陷的产品,具有高速、高效、准确的优点.该检测系统是集机械、光电、计算机、通信等技术为一体自动化检测系统,系统具有高速、高效、准确的优点,它的使用不仅可以提高烟草生产企业的自动化程度,还可以降低工人的劳动程度,改善工作环境,保证产品的质量.对常见条烟检测项进行了算法设计,并利用某公司的开源计算机视觉库OpenCV进行算法实现,提高了检测速度和精度.

  14. A child's vision.

    Science.gov (United States)

    Nye, Christina

    2014-06-01

    Implementing standard vision screening techniques in the primary care practice is the most effective means to detect children with potential vision problems at an age when the vision loss may be treatable. A critical period of vision development occurs in the first few weeks of life; thus, it is imperative that serious problems are detected at this time. Although it is not possible to quantitate an infant's vision, evaluating ocular health appropriately can mean the difference between sight and blindness and, in the case of retinoblastoma, life or death.

  15. Machining process influence on the chip form and surface roughness by neuro-fuzzy technique

    Science.gov (United States)

    Anicic, Obrad; Jović, Srđan; Aksić, Danilo; Skulić, Aleksandar; Nedić, Bogdan

    2017-04-01

    The main aim of the study was to analyze the influence of six machining parameters on the chip shape formation and surface roughness as well during turning of Steel 30CrNiMo8. Three components of cutting forces were used as inputs together with cutting speed, feed rate, and depth of cut. It is crucial for the engineers to use optimal machining parameters to get the best results or to high control of the machining process. Therefore, there is need to find the machining parameters for the optimal procedure of the machining process. Adaptive neuro-fuzzy inference system (ANFIS) was used to estimate the inputs influence on the chip shape formation and surface roughness. According to the results, the cutting force in direction of the depth of cut has the highest influence on the chip form. The testing error for the cutting force in direction of the depth of cut has testing error 0.2562. This cutting force determines the depth of cut. According to the results, the depth of cut has the highest influence on the surface roughness. Also the depth of cut has the highest influence on the surface roughness. The testing error for the cutting force in direction of the depth of cut has testing error 5.2753. Generally the depth of cut and the cutting force which provides the depth of cut are the most dominant factors for chip forms and surface roughness. Any small changes in depth of cut or in cutting force which provide the depth of cut could drastically affect the chip form or surface roughness of the working material.

  16. The application of machine learning techniques as an adjunct to clinical decision making in alcohol dependence treatment.

    Science.gov (United States)

    Connor, J P; Symons, M; Feeney, G F X; Young, R McD; Wiles, J

    2007-01-01

    With few exceptions, research in the addictive sciences has relied on linear statistics and methodologies. Addiction involves a complex array of nonlinear behaviors. This study applies two machine learning techniques, Bayesian and decision tree classifiers, in the assessment of outcome of an alcohol dependence treatment program. These nonlinear approaches are compared to a standard linear analysis. Seventy-three alcohol-dependent subjects undertaking a 12-week cognitive-behavioral therapy (CBT) program and 66 subjects undertaking an identical program but also prescribed the relapse prevention agent Acamprosate were employed in this study. Demographic, alcohol use, dependence severity, craving, health-related quality of life, and psychological measures at baseline were used to predict abstinence at 12 weeks. Decision trees had a 77% predictive accuracy across both data sets, Bayesian networks 73%, and discriminant analysis 42%. Combined with clinical experience, machine learning approaches offer promise in understanding the complex relationships that underlie treatment outcome for abstinence-based alcohol treatment programs.

  17. Research on workpiece Sorting Technology of Industrial Robot Based on Machine Vision%基于机器视觉的工业机器人工件分拣技术研究

    Institute of Scientific and Technical Information of China (English)

    甘伟

    2014-01-01

    工业机器人广泛应用于自动化生产线上完成工件搬运、上下料等操作,机器视觉的引入增加机器人了的灵活性和智能化。本文对基于视觉的工业机器人工件分拣的技术进行研究,机器视觉系统对传送带上进入工作区的工件进行图像采集,根据图像信息提取工件特征参数,识别出工件类型,并判断出工件所处的位置姿态,最后控制机器人执行抓取。经过实验表明本系统工作可靠,提高了自动化生产线效率和柔性。%Industrial robots have been widely used on industrial production line to complete a variety of operations such as workpiece handling,loading and unloading,etc.The machine vision can improve robotic lfexibilities and Intel igence.This paper introduces researching machine vision for the workpiece Sorting technology of industrial robot. When workpiece enter the sorting operation area,machine vision system capture image information to extract the feature parameters of workpiece, recognize the workpiece types,colour,size,and to judge the position and posture of workpiece,and ifnal y control the robot to implement the sorting action.Experiments prove the system work reliability,improve the efifciency and lfexibility of the automatic production line.

  18. 机器视觉辅助的插头锥套式无人机自主空中加油仿真%Machine Vision Aided Simulation of Probe and Drogue Unmanned Aerial Vehicle Autonomous Aerial Refueling

    Institute of Scientific and Technical Information of China (English)

    王旭峰; 董新民; 孔星炜

    2013-01-01

    In order to precisely obtain the relative pose between probe and drogue during unmanned aerial vehicle (UAV) autonomous aerial refueling docking,a machine vision aided simulation scheme of probe-drogue UAV autonomous aerial refueling is proposed.Based on the machine vision,the recognition and tracking algorithm of refueling drogue is investigated.The relative pose between UAV and refueling drogue is estimated by using of Kalman filter algorithm.Experiment results show that the machine vision aided image processing algorithm can recognize and track refueling drogue precisely and the convergence of relative pose errors estimated by the filter algorithm is proved,fulfilling the demand of probe-drogue UAV autonomous aerial refueling.%为准确获取无人机自主空中加油对接阶段受油插头与加油锥套的相对位姿信息,提出一种机器视觉辅助的插头锥套式无人机自主空中加油方案.研究了机器视觉识别跟踪加油锥套的算法,利用卡尔曼滤波算法估计无人机与加油锥套的相对位姿.实验结果表明:机器视觉图像处理算法可精确识别跟踪加油锥套,滤波器估计的相对位姿误差收敛速度较快,满足插头锥套式无人机自主空中加油的需要.

  19. IMPROVEMENT IN VISION FOLLOWING CATARACT SURGERY: A COMPARISON OF PHACOEMULSIFICATION AND SMALL INCISION CATARACT SURGERY (SICS TECHNIQUES

    Directory of Open Access Journals (Sweden)

    Abraham

    2016-03-01

    Full Text Available INTRODUCTION Phacoemulsification is the method of choice in most of the western nations and tertiary care ophthalmology centres in India, while manual small incision cataract surgery (MSICS is the surgical technique preferred by most of the ophthalmic surgeons working in smaller centres. Many studies have indicated that the MSICS technique is preferable for smaller centres, especially in developing countries, as the duration of surgery and requirement of equipment tends to be much smaller. This study was aimed at comparing the outcomes of MSICS and phacoemulsification surgeries carried out over a period of three months at a tertiary care hospital in South India. MATERIALS AND METHODS Patients diagnosed to have age related cataract and undergoing surgery in this institution were included in the study. The choice of surgical intervention was based on the preference of the operating surgeon and choice of the patient. The patients were followed up at the end of one week on their review visit to the outpatient department of the hospital. The incidence of postoperative complications was enquired, apart from measurement of visual acuity and corneal diameters. RESULTS A total of 106 participants were included in the study. Eighty percent of the patients who underwent phacoemulsification had some improvement in vision, while 81.9% of the participants in the MSICS group showed improvement, (p-0.825, only one participant had a complication related to the surgery, and he belonged to the MSICS group. The changes in K1 (p-0.547 and K2 (p-0.698 corneal diameters during surgery was also not significantly different between the groups. CONCLUSIONS It was observed that MSICS and phacoemulsification procedures have similar outcomes when used at a tertiary care teaching hospital in South India. A large multicentric Randomised Control Trial (RCT is warranted to compare the outcomes of the two surgical procedures and the cost-effectiveness of each, before concrete

  20. Operator functional state classification using least-square support vector machine based recursive feature elimination technique.

    Science.gov (United States)

    Yin, Zhong; Zhang, Jianhua

    2014-01-01

    This paper proposed two psychophysiological-data-driven classification frameworks for operator functional states (OFS) assessment in safety-critical human-machine systems with stable generalization ability. The recursive feature elimination (RFE) and least square support vector machine (LSSVM) are combined and used for binary and multiclass feature selection. Besides typical binary LSSVM classifiers for two-class OFS assessment, two multiclass classifiers based on multiclass LSSVM-RFE and decision directed acyclic graph (DDAG) scheme are developed, one used for recognizing the high mental workload and fatigued state while the other for differentiating overloaded and base-line states from the normal states. Feature selection results have revealed that different dimensions of OFS can be characterized by specific set of psychophysiological features. Performance comparison studies show that reasonable high and stable classification accuracy of both classification frameworks can be achieved if the RFE procedure is properly implemented and utilized.

  1. Applying a Machine Learning Technique to Classification of Japanese Pressure Patterns

    Directory of Open Access Journals (Sweden)

    H Kimura

    2009-04-01

    Full Text Available In climate research, pressure patterns are often very important. When a climatologists need to know the days of a specific pressure pattern, for example "low pressure in Western areas of Japan and high pressure in Eastern areas of Japan (Japanese winter-type weather," they have to visually check a huge number of surface weather charts. To overcome this problem, we propose an automatic classification system using a support vector machine (SVM, which is a machine-learning method. We attempted to classify pressure patterns into two classes: "winter type" and "non-winter type". For both training datasets and test datasets, we used the JRA-25 dataset from 1981 to 2000. An experimental evaluation showed that our method obtained a greater than 0.8 F-measure. We noted that variations in results were based on differences in training datasets.

  2. Forecasting the NOK/USD Exchange Rate with Machine Learning Techniques

    OpenAIRE

    Theophilos Papadimitriou; Periklis Gogas; Vasilios Plakandaras

    2013-01-01

    In this paper, we approximate the empirical findings of Papadamou and Markopoulos (2012) on the NOK/USD exchange rate under a Machine Learning (ML) framework. By applying Support Vector Regression (SVR) on a general monetary exchange rate model and a Dynamic Evolving Neuro-Fuzzy Inference System (DENFIS) to extract model structure, we test for the validity of popular monetary exchange rate models. We reach to mixed results since the coefficient sign of interest rate differential is in favor o...

  3. Application of Machine Learning Techniques for Amplitude and Phase Noise Characterization

    DEFF Research Database (Denmark)

    Zibar, Darko; de Carvalho, Luis Henrique Hecker; Piels, Molly

    2015-01-01

    In this paper, tools from machine learning community, such as Bayesian filtering and expectation maximization parameter estimation, are presented and employed for laser amplitude and phase noise characterization. We show that phase noise estimation based on Bayesian filtering outperforms...... conventional time-domain approach in the presence of moderate measurement noise. Additionally, carrier synchronization based on Bayesian filtering, in combination with expectation maximization, is demonstrated for the first time experimentally....

  4. Technique for optimal placement of transducers for fault detection in rotating machines

    OpenAIRE

    2013-01-01

    Online fault detection and diagnosis of rotating machinery requires a number of transducers that can be significantly expensive for industrial processes. The sensitivity of various transducers and their appropriate positioning are dependent on different types of fault conditions. It is critical to formulate a method to systematically determine the effectiveness of transducer locations for monitoring the condition of a machine. In this article, number of independent sources analysis is used as...

  5. Virtual Machine-level Software Transactional Memory: Principles, Techniques, and Implementation

    Science.gov (United States)

    2015-08-13

    VM-managed environment. ByteSTM is built by modifying Jikes RVM [3], a Java research virtual machine implemented in Java , using the optimizing...project have been publicly released as open-source software and research papers published at international conferences. In the following we summarize them... Research (AFOSR)/ RTC Arlington, Virginia 22203 Air Force Research Laboratory Air Force Materiel Command REPORT DOCUMENTATION PAGE Form Approved OMB No

  6. A New Technique: Research and industrial application of a novel compound permanent magnet synchronous machine

    Institute of Scientific and Technical Information of China (English)

    Cheng-zhi FAN; Ming-xing HUANG; Yun-yue YE

    2009-01-01

    We propose a novel kind of compound permanent magnet synchronous machine (CPMSM), which is applicable in low-speed and high-torque situations. We first explain the structure of the CPMSM. Based on theoretically deducing the calculation formulae of the CPMSM electromagnetic parameters, we analyze the operating characteristics of the CPMSM, and obtain the power-angle curves and working curves. The no-load magnetic field distribution and the cogging torque are analyzed by applying the finite element method of three-dimensional (3D) magnetic fields, to determine the no-load leakage coefficient and the wave0form of the cogging torque. Furthermore, the optimal parameters of the permanent magnet for reducing the cogging torque are determined. An important application target of the CPMSM is in direct-drive pumping units. We have installed and tested a directdrive pumping unit in an existing oil well. Test results show that the power consumption of the direct-drive pumping unit driven by CPMSM is 6 1. 1% of that of the beam-pumping unit, and that the floor space and weight are only 50% of those of a beam-pumping unit. The noise output does not exceed 58 dB in a range of 1 m around the machine when the machine is 1.5 m from the ground.

  7. A data-based technique for monitoring of wound rotor induction machines: A simulation study

    Directory of Open Access Journals (Sweden)

    Fouzi Harrou

    2016-09-01

    Full Text Available Detecting faults induction machines is crucial for a safe operation of these machines. The aim of this paper is to present a statistical fault detection methodology for the detection of faults in three-phase wound rotor induction machines (WRIM. The proposed fault detection approach is based on the use of principal components analysis (PCA. However, conventional PCA-based detection indices, such as the T2 and the Q statistics, are not well suited to detect small faults because these indices only use information from the most recent available samples. Detection of small faults is one of the most crucial and challenging tasks in the area of fault detection and diagnosis. In this paper, a new statistical system monitoring strategy is proposed for detecting changes resulting from small shifts in several variables associated with WRIM. The proposed approach combines modeling using PCA modeling with the exponentially weighted moving average (EWMA control scheme. In the proposed approach, EWMA control scheme is applied on the ignored principal components to detect the presence of faults. The performance of the proposed method is compared with those of the traditional PCA-based fault detection indices. The simulation results clearly show the effectiveness of the proposed method over the conventional ones, especially in the presence of faults with small magnitudes.

  8. A data-based technique for monitoring of wound rotor induction machines: A simulation study

    KAUST Repository

    Harrou, Fouzi

    2016-05-09

    Detecting faults induction machines is crucial for a safe operation of these machines. The aim of this paper is to present a statistical fault detection methodology for the detection of faults in three-phase wound rotor induction machines (WRIM). The proposed fault detection approach is based on the use of principal components analysis (PCA). However, conventional PCA-based detection indices, such as the T2T2 and the Q statistics, are not well suited to detect small faults because these indices only use information from the most recent available samples. Detection of small faults is one of the most crucial and challenging tasks in the area of fault detection and diagnosis. In this paper, a new statistical system monitoring strategy is proposed for detecting changes resulting from small shifts in several variables associated with WRIM. The proposed approach combines modeling using PCA modeling with the exponentially weighted moving average (EWMA) control scheme. In the proposed approach, EWMA control scheme is applied on the ignored principal components to detect the presence of faults. The performance of the proposed method is compared with those of the traditional PCA-based fault detection indices. The simulation results clearly show the effectiveness of the proposed method over the conventional ones, especially in the presence of faults with small magnitudes.

  9. Estimating Fractional Shrub Cover Using Simulated EnMAP Data: A Comparison of Three Machine Learning Regression Techniques

    Directory of Open Access Journals (Sweden)

    Marcel Schwieder

    2014-04-01

    Full Text Available Anthropogenic interventions in natural and semi-natural ecosystems often lead to substantial changes in their functioning and may ultimately threaten ecosystem service provision. It is, therefore, necessary to monitor these changes in order to understand their impacts and to support management decisions that help ensuring sustainability. Remote sensing has proven to be a valuable tool for these purposes, and especially hyperspectral sensors are expected to provide valuable data for quantitative characterization of land change processes. In this study, simulated EnMAP data were used for mapping shrub cover fractions along a gradient of shrub encroachment, in a study region in southern Portugal. We compared three machine learning regression techniques: Support Vector Regression (SVR; Random Forest Regression (RF; and Partial Least Squares Regression (PLSR. Additionally, we compared the influence of training sample size on the prediction performance. All techniques showed reasonably good results when trained with large samples, while SVR always outperformed the other algorithms. The best model was applied to produce a fractional shrub cover map for the whole study area. The predicted patterns revealed a gradient of shrub cover between regions affected by special agricultural management schemes for nature protection and areas without land use incentives. Our results highlight the value of EnMAP data in combination with machine learning regression techniques for monitoring gradual land change processes.

  10. From analog timers to the era of machine learning: The case of the transient hot-wire technique

    Science.gov (United States)

    Assael, Yannis M.; Antoniadis, Konstantinos D.; Assael, Marc J.

    2017-07-01

    In this work, we demonstrate how interdisciplinary knowledge can provide solutions to elusive challenges and advance science. As an example, we used the application of the THW in the measurement of the thermal conductivity of solids. To obtain a solution of the equations by FEM, about 10 h were required. By employing tools from the field of machine learning and computer science like a) automating the manual pipeline using a custom framework, b) using efficiently, Bayesian Optimisation to estimate the optimal thermal properties value, and c) applying further task specific optimisations, this time was reduced to 3 min, which is acceptable, and thus the technique can be easier used.

  11. Pleiades Visions

    Science.gov (United States)

    Whitehouse, M.

    2016-01-01

    Pleiades Visions (2012) is my new musical composition for organ that takes inspiration from traditional lore and music associated with the Pleiades (Seven Sisters) star cluster from Australian Aboriginal, Native American, and Native Hawaiian cultures. It is based on my doctoral dissertation research incorporating techniques from the fields of ethnomusicology and cultural astronomy; this research likely represents a new area of inquiry for both fields. This large-scale work employs the organ's vast sonic resources to evoke the majesty of the night sky and the expansive landscapes of the homelands of the above-mentioned peoples. Other important themes in Pleiades Visions are those of place, origins, cosmology, and the creation of the world.

  12. Lambda Vision

    Science.gov (United States)

    Czajkowski, Michael

    2014-06-01

    There is an explosion in the quantity and quality of IMINT data being captured in Intelligence Surveillance and Reconnaissance (ISR) today. While automated exploitation techniques involving computer vision are arriving, only a few architectures can manage both the storage and bandwidth of large volumes of IMINT data and also present results to analysts quickly. Lockheed Martin Advanced Technology Laboratories (ATL) has been actively researching in the area of applying Big Data cloud computing techniques to computer vision applications. This paper presents the results of this work in adopting a Lambda Architecture to process and disseminate IMINT data using computer vision algorithms. The approach embodies an end-to-end solution by processing IMINT data from sensors to serving information products quickly to analysts, independent of the size of the data. The solution lies in dividing up the architecture into a speed layer for low-latent processing and a batch layer for higher quality answers at the expense of time, but in a robust and fault-tolerant way. This approach was evaluated using a large corpus of IMINT data collected by a C-130 Shadow Harvest sensor over Afghanistan from 2010 through 2012. The evaluation data corpus included full motion video from both narrow and wide area field-of-views. The evaluation was done on a scaled-out cloud infrastructure that is similar in composition to those found in the Intelligence Community. The paper shows experimental results to prove the scalability of the architecture and precision of its results using a computer vision algorithm designed to identify man-made objects in sparse data terrain.

  13. Technique to reduce the shaft torque stress at an induction machine

    Directory of Open Access Journals (Sweden)

    Adrian Tulbure

    2005-10-01

    Full Text Available For the active attenuation at load stress in the drive shaft, the control system should receive as input signal the instantaneous shaft torque value. In this context an intelligent observer for shaft tongue of mains operatea induction machine, which is able to responding by variation of LIF (Load Input Function[1] must be developed. Extensive computer simulation prove the effectiveness of the proposed solution. In order to obtain a practical validation, the stimulated regulator has been designed and tested in the Institute of Electrical Engineering in Clausthal/Germany [2]. This paper contains following parts: Developing the mathematical model, Practical realisation, Simulations and measurements, Evaluating the control solutions and Conclusions.

  14. Research on Key Techniques of Condition Monitoring and Fault Diagnosing Systems of Machine Groups

    Institute of Scientific and Technical Information of China (English)

    WANG Yan-kai; LIAO Ming-fu; WANG Si-ji

    2005-01-01

    This paper describes the development of the condition monitoring and fault diagnosing system of a group of rotating machinery. The data management is performed by means of double redundant data bases stored simultaneously in both the analyzing server and monitoring client. In this way, high reliability of the storage of data is guaranteed. Condensation of trend data releases much space resource of the hard disk. Diagnosing strategies orientated to different typical faults of rotating machinery are developed and incorporated into the system. Experimental verification shows that the system is suitable and effective for condition monitoring and fault diagnosing for a rotating machine group.

  15. A Novel Diagnostic Technique to Study the Ageing of Rotating Machine Insulation: The

    OpenAIRE

    2000-01-01

    Rotating machine insulation ageing has been the subject of intensive research over the years. In this paper, model stator bars are investigated with the aid a Partial Discharge (PD) detector. The maximum PD magnitude is recorded as the applied voltage increases and as it decreases. The resulting “hysteresis curve” indicates whether the stator bar is in a “good” or “bad” condition, i.e. it indicates its “state of health”. The proposed method has certain advantages over other methods since it r...

  16. Beam Coupling Impedance Localization Technique Validation and Measurements in the CERN Machines

    CERN Document Server

    Biancacci, N; Argyropoulos, T; Bartosik, H; Calaga, R; Cornelis, K; Gilardoni, S; Métral, E; Mounet, N; Papaphilippou, Y; Persichelli, S; Rumolo, G; Salvant, B; Sterbini, G; Tomàs, R; Wasef, R; Migliorati, M; Palumbo, L

    2013-01-01

    The beam coupling impedance could lead to limitations in beam brightness and quality, and therefore it needs accurate quantification and continuous monitoring in order to detect and mitigate high impedance sources. In the CERN machines, for example, kickers and collimators are expected to be important contributors to the total imaginary part of the transverse impedance. In order to detect the other sources, a beam based measurement was developed: from the variation of betatron phase beating with intensity, it is possible to detect the locations of main impedance sources. In this work we present the application of the method with beam measurements in the CERN PS, SPS and LHC.

  17. 基于机器视觉的电子器件在线检测分选系统%Design of Online Detecting and Sorting System for Electronic Devices Based on Machine Vision

    Institute of Scientific and Technical Information of China (English)

    李啸宇

    2011-01-01

    针对一种声表面波滤波电子器件,设计了基于机器视觉的电子器件在线检测分选系统,详细论述了机器视觉系统的硬件组成和工作原理.在搭建系统硬件平台后,采用Qt应用程序框架,结合OpenCV开发了一套电子器件在线检测分选系统软件.经过大量实验和长时间实际生产运行表明,该系统检测速度快、识别准确率高、成本预算低,完全满足现代工业在线检测的需要.%In order to design a kind of electronic devices of surface acoustic wave filter, it describes a online detecting and sorting system based on machine vision, shows the detail about the hardware components and operating principle of the machine vision system. Based on hardware platform of the system, it develops the system software with Qt application framework and OpenCV library. Lots of experiments and a long period of running show that the system costs low and sorts devices with high detection speed and identification rate, the whole system is able to fully meet the needs of modern industrial online detection.

  18. Conveyor Belt Surface Image Correction and Fault Detection Algorithm Research Based on Machine Vision%基于机器视觉的输送带图像校正和故障检测算法研究

    Institute of Scientific and Technical Information of China (English)

    2016-01-01

    为了消除基于机器视觉的输送带故障在线监测系统中采集图像的不均匀光照影响,提高图像质量,检测出图像中的故障区域,提出了一种基于机器视觉的输送带图像校正和故障检测算法.该算法首先采用Butterworth低通滤波器对图像滤波,结合Retinex理论计算估计真实图像的背景,对图像进行灰度校正,得到校正后的图像;然后将机器视觉与生物视觉相结合,利用PCNN算法,对采集的图像进行检测,检测出故障区域.实验结果表明,算法能有效校正输送带表面图像,清晰检测出故障区域,具有很高的应用价值.%For the purposes of eliminating the influence of the non-uniform illumination which was used in on-line fault moni-toring system of conveyor belt based on machine vision and improved the quality of detected image and detecting the fault area of the image, a new kind of detection algorithm based on machine vision was proposed which can be used to realize the image correc-tion and fault detection of conveyor belt. The proposed algorithm firstly implemented the low-pass filtering of the acquired images by using Butterworth low-pass filter, then established estimated background model of the non-uniform illumination based on Retinex theory. Gray scale of the image can be amended evenly. Lastly, by using the combination the machine vision with biological vision and PNCC theory, the defected area of collected surface image of the conveyor belt was detected. Experimental results show that the proposed algorithm can be effectively used to correct the uneven gray scale of the surface images and detect the defected area of the surface image. It proves that this proposed algorithm has very high application value in mine belt conveyor supervision system.

  19. 基于机器视觉的水稻秧苗图像分割%Machine vision based segmentation algorithm for rice seedling

    Institute of Scientific and Technical Information of China (English)

    袁加红; 朱德泉; 孙丙宇; 孙磊; 武立权; 宋宇; 蒋锐

    2016-01-01

    水稻秧苗的识别是水稻插秧机自主导航系统的关键内容之一.针对插秧机机器视觉导航中稻田图像秧苗与背景分割问题,建立了基于RGB(红绿蓝)颜色空间的秧苗表面颜色模型.通过颜色特征对秧苗图像进行处理,使用Photoshop软件获取秧苗部分和背景R,G,B分量值;通过对G-R值与G-B值的分析统计,发现两者之间存在分界关系:各自的权重与各分量的乘积之和为某个定值;为方便分析,选取权值a,b为0.5,即ExG因子,采用Otsu法获取定值最佳值,最大程度分割出目标和背景.与适合于大多数绿色作物的传统RGB法进行比较,并采用分割质量因子和算法运算时间作为评判标准,分析各算法的综合性能.试验发现,ExG因子结合Otsu分割法分割效果相对理想、稳定性更高,而且耗时更短.%The recognition of rice seedling is one of the significant parts of autonomous guidance for rice transplan-ting. Considering the segmentation of seedlings and remainder based on machine vision system, a simple dichromatic reflection model was established in RGB color space, which represented that the seedling could be recognized by u-sing its color feature. The values of R, G, B components of seedlings and remainder were obtained in Photoshop soft-ware respectively and analyzed statistically in order to get the relation between them. In order to simplify the compu-ting process, the weight values of a and b were set as 0. 5, ExG index and Otsu method (ExG+Otsu method) which could obtain the optimal threshold were combined to distinguish the seedlings and remainder well. The RGB method and previous ExG+Otsu method were carried out to compare their performance intuitively. Their comprehensive per-formance was evaluated with segmentation quality factor and time consuming. The results have proved that the latter for segmenting was more efficient, highly stable and timesaving.

  20. Basic design principles of colorimetric vision systems

    Science.gov (United States)

    Mumzhiu, Alex M.

    1998-10-01

    Color measurement is an important part of overall production quality control in textile, coating, plastics, food, paper and other industries. The color measurement instruments such as colorimeters and spectrophotometers, used for production quality control have many limitations. In many applications they cannot be used for a variety of reasons and have to be replaced with human operators. Machine vision has great potential for color measurement. The components for color machine vision systems, such as broadcast quality 3-CCD cameras, fast and inexpensive PCI frame grabbers, and sophisticated image processing software packages are available. However the machine vision industry has only started to approach the color domain. The few color machine vision systems on the market, produced by the largest machine vision manufacturers have very limited capabilities. A lack of understanding that a vision based color measurement system could fail if it ignores the basic principles of colorimetry is the main reason for the slow progress of color vision systems. the purpose of this paper is to clarify how color measurement principles have to be applied to vision systems and how the electro-optical design features of colorimeters have to be modified in order to implement them for vision systems. The subject of this presentation far exceeds the limitations of a journal paper so only the most important aspects will be discussed. An overview of the major areas of applications for colorimetric vision system will be discussed. Finally, the reasons why some customers are happy with their vision systems and some are not will be analyzed.

  1. Scheduling and sequencing in four machines robotic cell: Application of genetic algorithm and enumeration techniques

    Directory of Open Access Journals (Sweden)

    M.M.S. Abdulkader

    2013-09-01

    Full Text Available The introduction of robotic cells to manufacturing systems improved the efficiency, productivity and reliability of the system. The main objective of the scheduling problem of multi-item multi-machine robotic cells is the identification of the optimum robot cycle/s and jobs sequencing for certain processing conditions which yield the higher possible production rate. The objective of this work is to solve the scheduling problem in four-machine blocking robotic cells producing identical and different part types while minimizing the cycle time. A genetic algorithm is developed to find the parts sequence that minimizes the robot-moves cycle time for each robot cycle. The results showed that the developed genetic algorithm yields competitive results compared to the results of the full enumeration of all possible parts sequences. The results show also that the ratio between the average processing time of all parts and the robot travel time determines the cycle having the optimal robot-moves.

  2. An alternative hybrid evolutionary technique focused on allocating machines and sequencing operations

    Directory of Open Access Journals (Sweden)

    Mariano Frutos

    2016-09-01

    Full Text Available We present here a hybrid algorithm for the Flexible Job-Shop Scheduling Problem (FJSSP. This problem involves the optimal use of resources in a flexible production environment in which each operation can be carried out by more than a single machine. Our algorithm allocates, in a first step, the machines to operations and in a second stage it sequences them by integrating a Multi-Objective Evolutionary Algorithm (MOEA and a path-dependent search algorithm (Multi-Objective Simulated Annealing, which is enacted at the genetic phase of the procedure. The joint interaction of those two components yields a very efficient procedure for solving the FJSSP. An important step in the development of the algorithm was the selection of the right MOEA. Candidates were tested on problems of low, medium and high complexity. Further analyses showed the relevance of the search algorithm in the hybrid structure. Finally, comparisons with other algorithms in the literature indicate that the performance of our alternative is good.

  3. Automatic segmentation of airway tree based on local intensity filter and machine learning technique in 3D chest CT volume.

    Science.gov (United States)

    Meng, Qier; Kitasaka, Takayuki; Nimura, Yukitaka; Oda, Masahiro; Ueno, Junji; Mori, Kensaku

    2017-02-01

    Airway segmentation plays an important role in analyzing chest computed tomography (CT) volumes for computerized lung cancer detection, emphysema diagnosis and pre- and intra-operative bronchoscope navigation. However, obtaining a complete 3D airway tree structure from a CT volume is quite a challenging task. Several researchers have proposed automated airway segmentation algorithms basically based on region growing and machine learning techniques. However, these methods fail to detect the peripheral bronchial branches, which results in a large amount of leakage. This paper presents a novel approach for more accurate extraction of the complex airway tree. This proposed segmentation method is composed of three steps. First, Hessian analysis is utilized to enhance the tube-like structure in CT volumes; then, an adaptive multiscale cavity enhancement filter is employed to detect the cavity-like structure with different radii. In the second step, support vector machine learning will be utilized to remove the false positive (FP) regions from the result obtained in the previous step. Finally, the graph-cut algorithm is used to refine the candidate voxels to form an integrated airway tree. A test dataset including 50 standard-dose chest CT volumes was used for evaluating our proposed method. The average extraction rate was about 79.1 % with the significantly decreased FP rate. A new method of airway segmentation based on local intensity structure and machine learning technique was developed. The method was shown to be feasible for airway segmentation in a computer-aided diagnosis system for a lung and bronchoscope guidance system.

  4. Vision Lab

    Data.gov (United States)

    Federal Laboratory Consortium — The Vision Lab personnel perform research, development, testing and evaluation of eye protection and vision performance. The lab maintains and continues to develop...

  5. Vision Screening

    Science.gov (United States)

    ... of Prematurity Strabismus Stye (defined) Vision Screening Vision Screening Recommendations Loading... Most Common Searches Adult Strabismus Amblyopia Cataract Conjunctivitis Corneal Abrasions Dilating Eye ...

  6. Support vector machines applications

    CERN Document Server

    Guo, Guodong

    2014-01-01

    Support vector machines (SVM) have both a solid mathematical background and good performance in practical applications. This book focuses on the recent advances and applications of the SVM in different areas, such as image processing, medical practice, computer vision, pattern recognition, machine learning, applied statistics, business intelligence, and artificial intelligence. The aim of this book is to create a comprehensive source on support vector machine applications, especially some recent advances.

  7. Artificial vision.

    Science.gov (United States)

    Zarbin, M; Montemagno, C; Leary, J; Ritch, R

    2011-09-01

    A number treatment options are emerging for patients with retinal degenerative disease, including gene therapy, trophic factor therapy, visual cycle inhibitors (e.g., for patients with Stargardt disease and allied conditions), and cell transplantation. A radically different approach, which will augment but not replace these options, is termed neural prosthetics ("artificial vision"). Although rewiring of inner retinal circuits and inner retinal neuronal degeneration occur in association with photoreceptor degeneration in retinitis pigmentosa (RP), it is possible to create visually useful percepts by stimulating retinal ganglion cells electrically. This fact has lead to the development of techniques to induce photosensitivity in cells that are not light sensitive normally as well as to the development of the bionic retina. Advances in artificial vision continue at a robust pace. These advances are based on the use of molecular engineering and nanotechnology to render cells light-sensitive, to target ion channels to the appropriate cell type (e.g., bipolar cell) and/or cell region (e.g., dendritic tree vs. soma), and on sophisticated image processing algorithms that take advantage of our knowledge of signal processing in the retina. Combined with advances in gene therapy, pathway-based therapy, and cell-based therapy, "artificial vision" technologies create a powerful armamentarium with which ophthalmologists will be able to treat blindness in patients who have a variety of degenerative retinal diseases.

  8. Machine Learning Techniques for Single Nucleotide Polymorphism—Disease Classification Models in Schizophrenia

    Directory of Open Access Journals (Sweden)

    Cristian R. Munteanu

    2010-07-01

    Full Text Available Single nucleotide polymorphisms (SNPs can be used as inputs in disease computational studies such as pattern searching and classification models. Schizophrenia is an example of a complex disease with an important social impact. The multiple causes of this disease create the need of new genetic or proteomic patterns that can diagnose patients using biological information. This work presents a computational study of disease machine learning classification models using only single nucleotide polymorphisms at the HTR2A and DRD3 genes from Galician (Northwest Spain schizophrenic patients. These classification models establish for the first time, to the best knowledge of the authors, a relationship between the sequence of the nucleic acid molecule and schizophrenia (Quantitative Genotype – Disease Relationships that can automatically recognize schizophrenia DNA sequences and correctly classify between 78.3–93.8% of schizophrenia subjects when using datasets which include simulated negative subjects and a linear artificial neural network.

  9. A Capacitive Displacement Sensing Technique for Early Detection of Unbalanced Loads in a Washing Machine

    Directory of Open Access Journals (Sweden)

    Karthik Tiruthani

    2009-11-01

    Full Text Available Horizontal axis washing machines are water and energy efficient and becoming popular in the USA. Unlike a vertical axis washer, these do not have an agitator and depend solely on tumbling for the agitation of laundry during the wash cycle. However, due to the constant shifting of laundry during washing, the load distribution is often unbalanced during the high speed spin cycle. We present a displacement-based sensing method to detect unbalance early while the spin rate (rpm is well below the resonance frequency so that corrective actions may be taken prior to the high speed spin cycle. Experimental and analytical characterizations of the sensor configuration are presented. Results show that the displacement sensor is more appropriate than an accelerometer for this application and offer the potential for a simple, reliable, low cost detection of unbalance.

  10. Analysis and design of machine learning techniques evolutionary solutions for regression, prediction, and control problems

    CERN Document Server

    Stalph, Patrick

    2014-01-01

    Manipulating or grasping objects seems like a trivial task for humans, as these are motor skills of everyday life. Nevertheless, motor skills are not easy to learn for humans and this is also an active research topic in robotics. However, most solutions are optimized for industrial applications and, thus, few are plausible explanations for human learning. The fundamental challenge, that motivates Patrick Stalph, originates from the cognitive science: How do humans learn their motor skills? The author makes a connection between robotics and cognitive sciences by analyzing motor skill learning using implementations that could be found in the human brain – at least to some extent. Therefore three suitable machine learning algorithms are selected – algorithms that are plausible from a cognitive viewpoint and feasible for the roboticist. The power and scalability of those algorithms is evaluated in theoretical simulations and more realistic scenarios with the iCub humanoid robot. Convincing results confirm the...

  11. Correlation technique and least square support vector machine combine for frequency domain based ECG beat classification.

    Science.gov (United States)

    Dutta, Saibal; Chatterjee, Amitava; Munshi, Sugata

    2010-12-01

    The present work proposes the development of an automated medical diagnostic tool that can classify ECG beats. This is considered an important problem as accurate, timely detection of cardiac arrhythmia can help to provide proper medical attention to cure/reduce the ailment. The proposed scheme utilizes a cross-correlation based approach where the cross-spectral density information in frequency domain is used to extract suitable features. A least square support vector machine (LS-SVM) classifier is developed utilizing the features so that the ECG beats are classified into three categories: normal beats, PVC beats and other beats. This three-class classification scheme is developed utilizing a small training dataset and tested with an enormous testing dataset to show the generalization capability of the scheme. The scheme, when employed for 40 files in the MIT/BIH arrhythmia database, could produce high classification accuracy in the range 95.51-96.12% and could outperform several competing algorithms.

  12. Automation of printing machine

    OpenAIRE

    Sušil, David

    2016-01-01

    Bachelor thesis is focused on the automation of the printing machine and comparing the two types of printing machines. The first chapter deals with the history of printing, typesettings, printing techniques and various kinds of bookbinding. The second chapter describes the difference between sheet-fed printing machines and offset printing machines, the difference between two representatives of rotary machines, technological process of the products on these machines, the description of the mac...

  13. Survey on Various Gesture Recognition Techniques for Interfacing Machines Based on Ambient Intelligence

    CERN Document Server

    C, Harshith; Ravindran, Manoj; Srikanth, M V V N S; Lakshmikhanth, Naveen; 10.5121/ijcses.2010.1203

    2010-01-01

    Gesture recognition is mainly apprehensive on analyzing the functionality of human wits. The main goal of gesture recognition is to create a system which can recognize specific human gestures and use them to convey information or for device control. Hand gestures provide a separate complementary modality to speech for expressing ones ideas. Information associated with hand gestures in a conversation is degree,discourse structure, spatial and temporal structure. The approaches present can be mainly divided into Data-Glove Based and Vision Based approaches. An important face feature point is the nose tip. Since nose is the highest protruding point from the face. Besides that, it is not affected by facial expressions.Another important function of the nose is that it is able to indicate the head pose. Knowledge of the nose location will enable us to align an unknown 3D face with those in a face database. Eye detection is divided into eye position detection and eye contour detection. Existing works in eye detectio...

  14. Detecting Faults in Southern California using Computer-Vision Techniques and Uninhabited Aerial Vehicle Synthetic Aperture Radar (UAVSAR) Interferometry

    Science.gov (United States)

    Barba, M.; Rains, C.; von Dassow, W.; Parker, J. W.; Glasscoe, M. T.

    2013-12-01

    Knowing the location and behavior of active faults is essential for earthquake hazard assessment and disaster response. In Interferometric Synthetic Aperture Radar (InSAR) images, faults are revealed as linear discontinuities. Currently, interferograms are manually inspected to locate faults. During the summer of 2013, the NASA-JPL DEVELOP California Disasters team contributed to the development of a method to expedite fault detection in California using remote-sensing technology. The team utilized InSAR images created from polarimetric L-band data from NASA's Uninhabited Aerial Vehicle Synthetic Aperture Radar (UAVSAR) project. A computer-vision technique known as 'edge-detection' was used to automate the fault-identification process. We tested and refined an edge-detection algorithm under development through NASA's Earthquake Data Enhanced Cyber-Infrastructure for Disaster Evaluation and Response (E-DECIDER) project. To optimize the algorithm we used both UAVSAR interferograms and synthetic interferograms generated through Disloc, a web-based modeling program available through NASA's QuakeSim project. The edge-detection algorithm detected seismic, aseismic, and co-seismic slip along faults that were identified and compared with databases of known fault systems. Our optimization process was the first step toward integration of the edge-detection code into E-DECIDER to provide decision support for earthquake preparation and disaster management. E-DECIDER partners that will use the edge-detection code include the California Earthquake Clearinghouse and the US Department of Homeland Security through delivery of products using the Unified Incident Command and Decision Support (UICDS) service. Through these partnerships, researchers, earthquake disaster response teams, and policy-makers will be able to use this new methodology to examine the details of ground and fault motions for moderate to large earthquakes. Following an earthquake, the newly discovered faults can

  15. Vision Based Autonomous Robotic Control for Advanced Inspection and Repair

    Science.gov (United States)

    Wehner, Walter S.

    2014-01-01

    The advanced inspection system is an autonomous control and analysis system that improves the inspection and remediation operations for ground and surface systems. It uses optical imaging technology with intelligent computer vision algorithms to analyze physical features of the real-world environment to make decisions and learn from experience. The advanced inspection system plans to control a robotic manipulator arm, an unmanned ground vehicle and cameras remotely, automatically and autonomously. There are many computer vision, image processing and machine learning techniques available as open source for using vision as a sensory feedback in decision-making and autonomous robotic movement. My responsibilities for the advanced inspection system are to create a software architecture that integrates and provides a framework for all the different subsystem components; identify open-source algorithms and techniques; and integrate robot hardware.

  16. Improving the Performance of Machine Learning Based Multi Attribute Face Recognition Algorithm Using Wavelet Based Image Decomposition Technique

    Directory of Open Access Journals (Sweden)

    S. Sakthivel

    2011-01-01

    Full Text Available Problem statement: Recognizing a face based attributes is an easy task for a human to perform; it is closely automated and requires little mental effort. A computer, on the other hand, has no innate ability to recognize a face or a facial feature and must be programmed with an algorithm to do so. Generally, to recognize a face, different kinds of the facial features were used separately or in a combined manner. In the previous work, we have developed a machine learning based multi attribute face recognition algorithm and evaluated it different set of weights to each input attribute and performance wise it is low compared to proposed wavelet decomposition technique. Approach: In this study, wavelet decomposition technique has been applied as a preprocessing technique to enhance the input face images in order to reduce the loss of classification performance due to changes in facial appearance. The Experiment was specifically designed to investigate the gain in robustness against illumination and facial expression changes. Results: In this study, a wavelet based image decomposition technique has been proposed to enhance the performance by 8.54 percent of the previously designed system. Conclusion: The proposed model has been tested on face images with difference in expression and illumination condition with a dataset obtained from face image databases from Olivetti Research Laboratory.

  17. Using Commercially Available Techniques to Make Organic Chemistry Representations Tactile and More Accessible to Students with Blindness or Low Vision

    Science.gov (United States)

    Supalo, Cary A.; Kennedy, Sean H.

    2014-01-01

    Organic chemistry courses can present major obstacles to access for students with blindness or low vision (BLV). In recent years, efforts have been made to represent organic chemistry concepts in tactile forms for blind students. These methodologies are described in this manuscript. Further work being done at Illinois State University is also…

  18. Using Commercially Available Techniques to Make Organic Chemistry Representations Tactile and More Accessible to Students with Blindness or Low Vision

    Science.gov (United States)

    Supalo, Cary A.; Kennedy, Sean H.

    2014-01-01

    Organic chemistry courses can present major obstacles to access for students with blindness or low vision (BLV). In recent years, efforts have been made to represent organic chemistry concepts in tactile forms for blind students. These methodologies are described in this manuscript. Further work being done at Illinois State University is also…

  19. Using Adaptive Tools and Techniques to Teach a Class of Students Who Are Blind or Low-Vision

    Science.gov (United States)

    Supalo, Cary A.; Mallouk, Thomas E.; Amorosi, Christeallia; Lanouette, James; Wohlers, H. David; McEnnis, Kathleen

    2009-01-01

    A brief overview of the 2007 National Federation of the Blind-Jernigan Institute Youth Slam Chemistry Track, a course of study within a science camp that provided firsthand experimental experience to 200 students who are blind and low-vision, is given. For many of these students, this was their first hands-on experience with laboratory chemistry.…

  20. Multivariate Cross-Classification: Applying machine learning techniques to characterize abstraction in neural representations

    Directory of Open Access Journals (Sweden)

    Jonas eKaplan

    2015-03-01

    Full Text Available Here we highlight an emerging trend in the use of machine learning classifiers to test for abstraction across patterns of neural activity. When a classifier algorithm is trained on data from one cognitive context, and tested on data from another, conclusions can be drawn about the role of a given brain region in representing information that abstracts across those cognitive contexts. We call this kind of analysis Multivariate Cross-Classification (MVCC, and review several domains where it has recently made an impact. MVCC has been important in establishing correspondences among neural patterns across cognitive domains, including motor-perception matching and cross-sensory matching. It has been used to test for similarity between neural patterns evoked by perception and those generated from memory. Other work has used MVCC to investigate the similarity of representations for semantic categories across different kinds of stimulus presentation, and in the presence of different cognitive demands. We use these examples to demonstrate the power of MVCC as a tool for investigating neural abstraction and discuss some important methodological issues related to its application.

  1. Hand Gesture recognition and classification by Discriminant and Principal Component Analysis using Machine Learning techniques

    Directory of Open Access Journals (Sweden)

    Sauvik Das Gupta

    2012-12-01

    Full Text Available This paper deals with the recognition of different hand gestures through machine learning approaches and principal component analysis. A Bio-Medical signal amplifier is built after doing a software simulation with the help of NI Multisim. At first a couple of surface electrodes are used to obtain the Electro-Myo-Gram (EMG signals from the hands. These signals from the surface electrodes have to be amplified with the help of the Bio-Medical Signal amplifier. The Bio-Medical Signal amplifier used is basically an Instrumentation amplifier made with the help of IC AD 620.The output from the Instrumentation amplifier is then filtered with the help of a suitable Band-Pass Filter. The output from the Band Pass filter is then fed to an Analog to Digital Converter (ADC which in this case is the NI USB 6008.The data from the ADC is then fed into a suitable algorithm which helps in recognition of the different hand gestures. The algorithm analysis is done in MATLAB. The results shown in this paper show a close to One-hundred per cent (100% classification result for three given hand gestures.

  2. submitter Studies of CMS data access patterns with machine learning techniques

    CERN Document Server

    De Luca, Silvia

    This thesis presents a study of the Grid data access patterns in distributed analysis in the CMS experiment at the LHC accelerator. This study ranges from the deep analysis of the historical patterns of access to the most relevant data types in CMS, to the exploitation of a supervised Machine Learning classification system to set-up a machinery able to eventually predict future data access patterns - i.e. the so-called dataset “popularity” of the CMS datasets on the Grid - with focus on specific data types. All the CMS workflows run on the Worldwide LHC Computing Grid (WCG) computing centers (Tiers), and in particular the distributed analysis systems sustains hundreds of users and applications submitted every day. These applications (or “jobs”) access different data types hosted on disk storage systems at a large set of WLCG Tiers. The detailed study of how this data is accessed, in terms of data types, hosting Tiers, and different time periods, allows to gain precious insight on storage occupancy ove...

  3. Feature extraction and classification for EEG signals using wavelet transform and machine learning techniques.

    Science.gov (United States)

    Amin, Hafeez Ullah; Malik, Aamir Saeed; Ahmad, Rana Fayyaz; Badruddin, Nasreen; Kamel, Nidal; Hussain, Muhammad; Chooi, Weng-Tink

    2015-03-01

    This paper describes a discrete wavelet transform-based feature extraction scheme for the classification of EEG signals. In this scheme, the discrete wavelet transform is applied on EEG signals and the relative wavelet energy is calculated in terms of detailed coefficients and the approximation coefficients of the last decomposition level. The extracted relative wavelet energy features are passed to classifiers for the classification purpose. The EEG dataset employed for the validation of the proposed method consisted of two classes: (1) the EEG signals recorded during the complex cognitive task--Raven's advance progressive metric test and (2) the EEG signals recorded in rest condition--eyes open. The performance of four different classifiers was evaluated with four performance measures, i.e., accuracy, sensitivity, specificity and precision values. The accuracy was achieved above 98 % by the support vector machine, multi-layer perceptron and the K-nearest neighbor classifiers with approximation (A4) and detailed coefficients (D4), which represent the frequency range of 0.53-3.06 and 3.06-6.12 Hz, respectively. The findings of this study demonstrated that the proposed feature extraction approach has the potential to classify the EEG signals recorded during a complex cognitive task by achieving a high accuracy rate.

  4. Prediction of Driver's Intention of Lane Change by Augmenting Sensor Information Using Machine Learning Techniques.

    Science.gov (United States)

    Kim, Il-Hwan; Bong, Jae-Hwan; Park, Jooyoung; Park, Shinsuk

    2017-06-10

    Driver assistance systems have become a major safety feature of modern passenger vehicles. The advanced driver assistance system (ADAS) is one of the active safety systems to improve the vehicle control performance and, thus, the safety of the driver and the passengers. To use the ADAS for lane change control, rapid and correct detection of the driver's intention is essential. This study proposes a novel preprocessing algorithm for the ADAS to improve the accuracy in classifying the driver's intention for lane change by augmenting basic measurements from conventional on-board sensors. The information on the vehicle states and the road surface condition is augmented by using an artificial neural network (ANN) models, and the augmented information is fed to a support vector machine (SVM) to detect the driver's intention with high accuracy. The feasibility of the developed algorithm was tested through driving simulator experiments. The results show that the classification accuracy for the driver's intention can be improved by providing an SVM model with sufficient driving information augmented by using ANN models of vehicle dynamics.

  5. Machine learning techniques in searches for tbar th in the h → bbar b decay channel

    Science.gov (United States)

    Santos, R.; Nguyen, M.; Webster, J.; Ryu, S.; Adelman, J.; Chekanov, S.; Zhou, J.

    2017-04-01

    Study of the production of pairs of top quarks in association with a Higgs boson is one of the primary goals of the Large Hadron Collider over the next decade, as measurements of this process may help us to understand whether the uniquely large mass of the top quark plays a special role in electroweak symmetry breaking. Higgs bosons decay predominantly to bbar b, yielding signatures for the signal that are similar to tbar t + jets with heavy flavor. Though particularly challenging to study due to the similar kinematics between signal and background events, such final states (tbar t bbar b) are an important channel for studying the top quark Yukawa coupling. This paper presents a systematic study of machine learning (ML) methods for detecting tbar th in the h → bbar b decay channel. Among the eight ML methods tested, we show that two models, extreme gradient boosted trees and neural network models, outperform alternative methods. We further study the effectiveness of ML algorithms by investigating the impact of feature set and data size, as well as the structure of the models. While extended feature set and larger training sets expectedly lead to improvement of performance, shallow models deliver comparable or better performance than their deeper counterparts. Our study suggests that ensembles of trees and neurons, not necessarily deep, work effectively for the problem of tbar th detection.

  6. Content-Based Image Retrieval Using Support Vector Machine in digital image processing techniques

    Directory of Open Access Journals (Sweden)

    G.V.Hari Prasad

    2012-04-01

    Full Text Available The rapid growth of computer technologies and the ad-vent of the World Wide Web have increased the amount and the complexity of multimedia information. A content-based image retrieval (CBIR system has been developed as an ef-ficient image retrieval tool, whereby the user can provide their query to the system to allow it to retrieve the user’s desired image from the image database. However, the tradi-tional relevance feedback of CBIR has some limitations that will decrease the performance of the CBIR system, such as the imbalance oftraining-set problem, classification prob-lem, limited information from user problem, and insuffi-cient trainingset problem. Therefore, in this study, we pro-posed an enhanced relevance-feedback method to support the user query based on the representative image selection and weight ranking of the images retrieved. The support vector machine (SVM has been used to support the learn-ing process to reduce the semantic gap between the user and the CBIR system. From these experiments, the proposed learning method has enabled users to improve their search results based on the performance of CBIR system. In addi-tion, the experiments also proved that by solving the imbal-ance training set issue, the performance of CBIR could be improved.

  7. Automated Classification of Heritage Buildings for As-Built Bim Using Machine Learning Techniques

    Science.gov (United States)

    Bassier, M.; Vergauwen, M.; Van Genechten, B.

    2017-08-01

    Semantically rich three dimensional models such as Building Information Models (BIMs) are increasingly used in digital heritage. They provide the required information to varying stakeholders during the different stages of the historic buildings life cyle which is crucial in the conservation process. The creation of as-built BIM models is based on point cloud data. However, manually interpreting this data is labour intensive and often leads to misinterpretations. By automatically classifying the point cloud, the information can be proccesed more effeciently. A key aspect in this automated scan-to-BIM process is the classification of building objects. In this research we look to automatically recognise elements in existing buildings to create compact semantic information models. Our algorithm efficiently extracts the main structural components such as floors, ceilings, roofs, walls and beams despite the presence of significant clutter and occlusions. More specifically, Support Vector Machines (SVM) are proposed for the classification. The algorithm is evaluated using real data of a variety of existing buildings. The results prove that the used classifier recognizes the objects with both high precision and recall. As a result, entire data sets are reliably labelled at once. The approach enables experts to better document and process heritage assets.

  8. Comparison of Feature Selection Techniques in Machine Learning for Anatomical Brain MRI in Dementia.

    Science.gov (United States)

    Tohka, Jussi; Moradi, Elaheh; Huttunen, Heikki

    2016-07-01

    We present a comparative split-half resampling analysis of various data driven feature selection and classification methods for the whole brain voxel-based classification analysis of anatomical magnetic resonance images. We compared support vector machines (SVMs), with or without filter based feature selection, several embedded feature selection methods and stability selection. While comparisons of the accuracy of various classification methods have been reported previously, the variability of the out-of-training sample classification accuracy and the set of selected features due to independent training and test sets have not been previously addressed in a brain imaging context. We studied two classification problems: 1) Alzheimer's disease (AD) vs. normal control (NC) and 2) mild cognitive impairment (MCI) vs. NC classification. In AD vs. NC classification, the variability in the test accuracy due to the subject sample did not vary between different methods and exceeded the variability due to different classifiers. In MCI vs. NC classification, particularly with a large training set, embedded feature selection methods outperformed SVM-based ones with the difference in the test accuracy exceeding the test accuracy variability due to the subject sample. The filter and embedded methods produced divergent feature patterns for MCI vs. NC classification that suggests the utility of the embedded feature selection for this problem when linked with the good generalization performance. The stability of the feature sets was strongly correlated with the number of features selected, weakly correlated with the stability of classification accuracy, and uncorrelated with the average classification accuracy.

  9. A Real-Time Interference Monitoring Technique for GNSS Based on a Twin Support Vector Machine Method.

    Science.gov (United States)

    Li, Wutao; Huang, Zhigang; Lang, Rongling; Qin, Honglei; Zhou, Kai; Cao, Yongbin

    2016-03-04

    Interferences can severely degrade the performance of Global Navigation Satellite System (GNSS) receivers. As the first step of GNSS any anti-interference measures, interference monitoring for GNSS is extremely essential and necessary. Since interference monitoring can be considered as a classification problem, a real-time interference monitoring technique based on Twin Support Vector Machine (TWSVM) is proposed in this paper. A TWSVM model is established, and TWSVM is solved by the Least Squares Twin Support Vector Machine (LSTWSVM) algorithm. The interference monitoring indicators are analyzed to extract features from the interfered GNSS signals. The experimental results show that the chosen observations can be used as the interference monitoring indicators. The interference monitoring performance of the proposed method is verified by using GPS L1 C/A code signal and being compared with that of standard SVM. The experimental results indicate that the TWSVM-based interference monitoring is much faster than the conventional SVM. Furthermore, the training time of TWSVM is on millisecond (ms) level and the monitoring time is on microsecond (μs) level, which make the proposed approach usable in practical interference monitoring applications.

  10. A Real-Time Interference Monitoring Technique for GNSS Based on a Twin Support Vector Machine Method

    Directory of Open Access Journals (Sweden)

    Wutao Li

    2016-03-01

    Full Text Available Interferences can severely degrade the performance of Global Navigation Satellite System (GNSS receivers. As the first step of GNSS any anti-interference measures, interference monitoring for GNSS is extremely essential and necessary. Since interference monitoring can be considered as a classification problem, a real-time interference monitoring technique based on Twin Support Vector Machine (TWSVM is proposed in this paper. A TWSVM model is established, and TWSVM is solved by the Least Squares Twin Support Vector Machine (LSTWSVM algorithm. The interference monitoring indicators are analyzed to extract features from the interfered GNSS signals. The experimental results show that the chosen observations can be used as the interference monitoring indicators. The interference monitoring performance of the proposed method is verified by using GPS L1 C/A code signal and being compared with that of standard SVM. The experimental results indicate that the TWSVM-based interference monitoring is much faster than the conventional SVM. Furthermore, the training time of TWSVM is on millisecond (ms level and the monitoring time is on microsecond (μs level, which make the proposed approach usable in practical interference monitoring applications.

  11. 机器视觉在钢化玻璃缺陷检测中的应用研究%Application and Research of Machine Vision in Tempered Glass Defect Inspection

    Institute of Scientific and Technical Information of China (English)

    杨杰; 卢盛林; 赵晓芳

    2013-01-01

      在总结引发钢化玻璃产生自爆的原因的基础上,针对有缺陷玻璃和无缺陷玻璃的光学特性差异性和人工检测缺陷玻璃的局限性等问题,提出采用机器视觉技术对钢化玻璃的缺陷进行检测。首先分析了钢化玻璃缺陷检测的光学基本原理,然后给出了缺陷检测系统的基本结构设计,最后探讨了针对钢化玻璃自爆的机器视觉检测系统的技术要点。试验结果表明,利用机器视觉技术能够快速、可靠、准确地检测出含有缺陷的钢化玻璃,从而避免其在使用中出现自爆。%Based on the study of several facts that may cause self-broken of tempered glass,a machine vision inspection technology is a-dopted to detect the defects of the tempered glass for the limitations of manual inspection of defects in glass and the optical characteristic differences of defect glass and defect-free glass. First analyze the basic optical principles of the tempered glass defect detection,and then give the basic structure design of the defect detection system,finally the technical points of the machine vision inspection system in view of the self-broken of tempered glass are also mentioned. The test results show that the use of machine vision technology can detect the de-fects rapidly,reliably and accurately,avoiding the self-broken in using.

  12. Translation of Untranslatable Words — Integration of Lexical Approximation and Phrase-Table Extension Techniques into Statistical Machine Translation

    Science.gov (United States)

    Paul, Michael; Arora, Karunesh; Sumita, Eiichiro

    This paper proposes a method for handling out-of-vocabulary (OOV) words that cannot be translated using conventional phrase-based statistical machine translation (SMT) systems. For a given OOV word, lexical approximation techniques are utilized to identify spelling and inflectional word variants that occur in the training data. All OOV words in the source sentence are then replaced with appropriate word variants found in the training corpus, thus reducing the number of OOV words in the input. Moreover, in order to increase the coverage of such word translations, the SMT translation model is extended by adding new phrase translations for all source language words that do not have a single-word entry in the original phrase-table but only appear in the context of larger phrases. The effectiveness of the proposed methods is investigated for the translation of Hindi to English, Chinese, and Japanese.

  13. Uncertainty quantification and integration of machine learning techniques for predicting acid rock drainage chemistry: a probability bounds approach.

    Science.gov (United States)

    Betrie, Getnet D; Sadiq, Rehan; Morin, Kevin A; Tesfamariam, Solomon

    2014-08-15

    Acid rock drainage (ARD) is a major pollution problem globally that has adversely impacted the environment. Identification and quantification of uncertainties are integral parts of ARD assessment and risk mitigation, however previous studies on predicting ARD drainage chemistry have not fully addressed issues of uncertainties. In this study, artificial neural networks (ANN) and support vector machine (SVM) are used for the prediction of ARD drainage chemistry and their predictive uncertainties are quantified using probability bounds analysis. Furthermore, the predictions of ANN and SVM are integrated using four aggregation methods to improve their individual predictions. The results of this study showed that ANN performed better than SVM in enveloping the observed concentrations. In addition, integrating the prediction of ANN and SVM using the aggregation methods improved the predictions of individual techniques.

  14. High Classification Rates for Continuous Cow Activity Recognition using Low-cost GPS Positioning Sensors and Standard Machine Learning Techniques

    DEFF Research Database (Denmark)

    Godsk, Torben; Kjærgaard, Mikkel Baun

    2011-01-01

    In precision livestock farming, spotting cows in need of extra attention due to health or welfare issues are essential, since the time a farmer can devote to each animal is decreasing due to growing herd sizes and increasing efficiency demands. Often, the symptoms of health and welfare state...... activities. By preprocessing the raw cow position data, we obtain high classification rates using standard machine learning techniques to recognize cow activities. Our objectives were to (i) determine to what degree it is possible to robustly recognize cow activities from GPS positioning data, using low......-cost GPS receivers; and (ii) determine which types of activities can be classified, and what robustness to expect within the different classes. To provide data for this study low-cost GPS receivers were mounted on 14 dairy cows on grass for a day while they were observed from a distance...

  15. Reverse engineering smart card malware using side channel analysis with machine learning techniques

    CSIR Research Space (South Africa)

    Djonon Tsague, Hippolyte

    2016-12-01

    Full Text Available by evaluating its power consumption only. Besides well-studied methods from side channel analysis, we apply a combination of dimensionality reduction techniques in the form of PCA and LDA models to compress the large amount of data generated while preserving...

  16. Evolving techniques of diagnosis. Toward establishment of new paradigm for human machine cooperation

    Energy Technology Data Exchange (ETDEWEB)

    Kitamura, Masaharu; Takahashi, Makoto [Tohoku Univ., Sendai (Japan). Faculty of Engineering; Kanamoto, Shigeru; Saeki, Akira; Washio, Takashi; Ohga, Yukiharu; Furuta, Kazuo; Yoshikawa, Shinji

    1998-09-01

    By monitoring equipments of a plant and state of a process, the diagnostic technique to detect a sign of abnormality properly to identify its reason has often been advanced on a lot of researches in various industrial fields containing atomic force. Some fundamental studies expected for such diagnostic technique to play an important role to keep and improve operational safety of a nuclear plant have been conducted since early period of the nuclear reaction development, but their contents are evolved and changed rapidly, in recent. The technique on the diagnosis was related closely to a statistical analysis method on signal fluctuation component, so-called reactor noise analysis method in early 1980s, but technical innovation step of their recent advancement were remarkable by introduction of new techniques such as chaos theory, wavelet analysis, model base application of expert system, artificial intelligence, and so on at middle of 1980s. And, when diagnosing in the field of atomic force, owing to be required for much high ability, studies on a multi method integration system considered complementary application of a plurality of technical methods and a cooperative method between human and mechanical intelligences, are also forwarded actively faster than those in other industrial areas. In this paper, in each important item, its technical nature and present state of its application to diagnosis are described with their future technical view. (G.K.)

  17. Vision models for 3D surfaces

    Science.gov (United States)

    Mitra, Sunanda

    1992-11-01

    Different approaches to computational stereo to represent human stereo vision have been developed over the past two decades. The Marr-Poggio theory of human stereo vision is probably the most widely accepted model of the human stereo vision. However, recently developed motion stereo models which use a sequence of images taken by either a moving camera or a moving object provide an alternative method of achieving multi-resolution matching without the use of Laplacian of Gaussian operators. While using image sequences, the baseline between two camera positions for a image pair is changed for the subsequent image pair so as to achieve different resolution for each image pair. Having different baselines also avoids the inherent occlusion problem in stereo vision models. The advantage of using multi-resolution images acquired by camera positioned at different baselines over those acquired by LOG operators is that one does not have to encounter spurious edges often created by zero-crossings in the LOG operated images. Therefore in designing a computer vision system, a motion stereo model is more appropriate than a stereo vision model. However, in some applications where only a stereo pair of images are available, recovery of 3D surfaces of natural scenes are possible in a computationally efficient manner by using cepstrum matching and regularization techniques. Section 2 of this paper describes a motion stereo model using multi-scale cepstrum matching for the detection of disparity between image pairs in a sequence of images and subsequent recovery of 3D surfaces from depth-map obtained by a non convergent triangulation technique. Section 3 presents a 3D surface recovery technique from a stereo pair using cepstrum matching for disparity detection and cubic B-splines for surface smoothing. Section 4 contains the results of 3D surface recovery using both of the techniques mentioned above. Section 5 discusses the merit of 2D cepstrum matching and cubic B

  18. Classification and Ranking of Fermi LAT Gamma-ray Sources from the 3FGL Catalog using Machine Learning Techniques

    CERN Document Server

    Parkinson, P M Saz; Yu, P L H; Salvetti, D; Marelli, M; Falcone, A D

    2016-01-01

    We apply a number of statistical and machine learning techniques to classify and rank gamma-ray sources from the Third Fermi Large Area Telescope (LAT) Source Catalog (3FGL), according to their likelihood of falling into the two major classes of gamma-ray emitters: pulsars (PSR) or Active Galactic Nuclei (AGN). Using 1904 3FGL sources that have been identified/associated with AGN (1738) and PSR (166), we train (using 70% of our sample) and test (using 30%) our algorithms and find that the best overall accuracy (>96%) is obtained with the Random Forest (RF) technique, while using a logistic regression (LR) algorithm results in only marginally lower accuracy. We apply the same techniques on a sub-sample of 142 known gamma-ray pulsars to classify them into two major subcategories: young (YNG) and millisecond pulsars (MSP). Once more, the RF algorithm has the best overall accuracy (~90%), while a boosted LR analysis comes a close second. We apply our two best models (RF and LR) to the entire 3FGL catalog, providi...

  19. A new volumetric CT machine for dental imaging based on the cone-beam technique: preliminary results

    Energy Technology Data Exchange (ETDEWEB)

    Mozzo, P. [Dept. of Medical Physics, University Hospital, Verona (Italy); Procacci, C.; Tacconi, A.; Tinazzi Martini, P.; Bergamo Andreis, I.A. [Dept. of Radiology, University Hospital, Verona (Italy)

    1998-12-01

    The objective of this paper is to present a new type of volumetric CT which uses the cone-beam technique instead of traditional fan-beam technique. The machine is dedicated to the dento-maxillo-facial imaging, particularly for planning in the field of implantology. The main characteristics of the unit are presented with reference to the technical parameters as well as the software performance. Images obtained are reported as various 2D sections of a volume reconstruction. Also, measurements of the geometric accuracy and the radiation dose absorbed by the patient are obtained using specific phantoms. Absorbed dose is compared with that given off by spiral CT. Geometric accuracy, evaluated with reference to various reconstruction modalities and different spatial orientations, is 0.8-1 % for width measurements and 2.2 % for height measurements. Radiation dose absorbed during the scan shows different profiles in central and peripheral axes. As regards the maximum value of the central profile, dose from the new unit is approximately one sixth that of traditional spiral CT. The new system appears to be very promising in dento-maxillo-facial imaging and, due to the good ratio between performance and low cost, together with low radiation dose, very interesting in view of large-scale use of the CT technique in such diagnostic applications. (orig.) With 10 figs., 3 tabs., 15 refs.

  20. Early vision and focal attention

    Science.gov (United States)

    Julesz, Bela

    1991-07-01

    At the thirty-year anniversary of the introduction of the technique of computer-generated random-dot stereograms and random-dot cinematograms into psychology, the impact of the technique on brain research and on the study of artificial intelligence is reviewed. The main finding-that stereoscopic depth perception (stereopsis), motion perception, and preattentive texture discrimination are basically bottom-up processes, which occur without the help of the top-down processes of cognition and semantic memory-greatly simplifies the study of these processes of early vision and permits the linking of human perception with monkey neurophysiology. Particularly interesting are the unexpected findings that stereopsis (assumed to be local) is a global process, while texture discrimination (assumed to be a global process, governed by statistics) is local, based on some conspicuous local features (textons). It is shown that the top-down process of "shape (depth) from shading" does not affect stereopsis, and some of the models of machine vision are evaluated. The asymmetry effect of human texture discrimination is discussed, together with recent nonlinear spatial filter models and a novel extension of the texton theory that can cope with the asymmetry problem. This didactic review attempts to introduce the physicist to the field of psychobiology and its problems-including metascientific problems of brain research, problems of scientific creativity, the state of artificial intelligence research (including connectionist neural networks) aimed at modeling brain activity, and the fundamental role of focal attention in mental events.