Sample records for ladar based object

  1. Ground target detection based on discrete cosine transform and Rényi entropy for imaging ladar

    Xu, Yuannan; Chen, Weili; Li, Junwei; Dong, Yanbing


    The discrete cosine transform (DCT) due to its excellent properties that the images can be represented in spatial/spatial-frequency domains, has been applied in sequence data analysis and image fusion. For intensity and range images of ladar, through the DCT using one dimension window, the statistical property of Rényi entropy for images is studied. We also analyzed the change of Rényi entropy's statistical property in the ladar intensity and range images when the man-made objects appear. From this foundation, a novel method for generating saliency map based on DCT and Rényi entropy is proposed. After that, ground target detection is completed when the saliency map is segmented using a simple and convenient threshold method. For the ladar intensity and range images, experimental results show the proposed method can effectively detect the military vehicles from complex earth background with low false alarm.

  2. Synthetic aperture ladar based on a MOPAW laser

    Turbide, Simon; Marchese, Linda; Bergeron, Alain; Desbiens, Louis; Paradis, Patrick


    Long range land surveillance is a critical need in numerous military and civilian security applications, such as threat detection, terrain mapping and disaster prevention. A key technology for land surveillance, synthetic aperture radar (SAR) continues to provide high resolution radar images in all weather conditions from remote distances. State of the art SAR systems based on dual-use satellites are capable of providing ground resolutions of one meter; while their airborne counterparts obtain resolutions of 10 cm. Certain land surveillance applications such as subsidence monitoring, landslide hazard prediction and tactical target tracking could benefit from improved resolution. The ultimate limitation to the achievable resolution of any imaging system is its wavelength. State-of-the-art SAR systems are approaching this limit. The natural extension to improve resolution is to thus decrease the wavelength, i.e. design a synthetic aperture system in a different wavelength regime. One such system offering the potential for vastly improved resolution is Synthetic Aperture Ladar (SAL). This system operates at infrared wavelengths, ten thousand times smaller radar wavelengths. This paper presents a SAL platform based on the INO Master Oscillator with Programmable Amplitude Waveform (MOPAW) laser that has a wavelength sweep of Δλ=1.22 nm, a pulse repetition rate up to 1 kHz and up to 200 μJ per pulse. The results for SAL 2D imagery at a range of 10 m are presented, indicating a reflectance sensibility of 8 %, ground-range and azimuth resolution of 1.7 mm and 0.84 mm respectively.

  3. Advances in ground vehicle-based LADAR for standoff detection of road-side hazards

    Hollinger, Jim; Vessey, Alyssa; Close, Ryan; Middleton, Seth; Williams, Kathryn; Rupp, Ronald; Nguyen, Son


    Commercial sensor technology has the potential to bring cost-effective sensors to a number of U.S. Army applications. By using sensors built for a widespread of commercial application, such as the automotive market, the Army can decrease costs of future systems while increasing overall capabilities. Additional sensors operating in alternate and orthogonal modalities can also be leveraged to gain a broader spectrum measurement of the environment. Leveraging multiple phenomenologies can reduce false alarms and make detection algorithms more robust to varied concealment materials. In this paper, this approach is applied to the detection of roadside hazards partially concealed by light-to-medium vegetation. This paper will present advances in detection algorithms using a ground vehicle-based commercial LADAR system. The benefits of augmenting a LADAR with millimeter-wave automotive radar and results from relevant data sets are also discussed.

  4. Optical imaging process based on two-dimensional Fourier transform for synthetic aperture imaging ladar

    Sun, Zhiwei; Zhi, Ya'nan; Liu, Liren; Sun, Jianfeng; Zhou, Yu; Hou, Peipei


    The synthetic aperture imaging ladar (SAIL) systems typically generate large amounts of data difficult to compress with digital method. This paper presents an optical SAIL processor based on compensation of quadratic phase of echo in azimuth direction and two dimensional Fourier transform. The optical processor mainly consists of one phase-only liquid crystal spatial modulator(LCSLM) to load the phase data of target echo and one cylindrical lens to compensate the quadratic phase and one spherical lens to fulfill the task of two dimensional Fourier transform. We show the imaging processing result of practical target echo obtained by a synthetic aperture imaging ladar demonstrator. The optical processor is compact and lightweight and could provide inherent parallel and the speed-of-light computing capability, it has a promising application future especially in onboard and satellite borne SAIL systems.

  5. Simulation of laser detection and ranging (LADAR) and forward-looking infrared (FLIR) data for autonomous tracking of airborne objects

    Powell, Gavin; Markham, Keith C.; Marshall, David


    This paper presents the results of an investigation leading into an implementation of FLIR and LADAR data simulation for use in a multi sensor data fusion automated target recognition system. At present the main areas of application are in military environments but systems can easily be adapted to other areas such as security applications, robotics and autonomous cars. Recent developments have been away from traditional sensor modeling and toward modeling of features that are external to the system, such as atmosphere and part occlusion, to create a more realistic and rounded system. We have implemented such techniques and introduced a means of inserting these models into a highly detailed scene model to provide a rich data set for later processing. From our study and implementation we are able to embed sensor model components into a commercial graphics and animation package, along with object and terrain models, which can be easily used to create a more realistic sequence of images.

  6. Spectral ladar: towards active 3D multispectral imaging

    Powers, Michael A.; Davis, Christopher C.


    In this paper we present our Spectral LADAR concept, an augmented implementation of traditional LADAR. This sensor uses a polychromatic source to obtain range-resolved 3D spectral images which are used to identify objects based on combined spatial and spectral features, resolving positions in three dimensions and up to hundreds of meters in distance. We report on a proof-of-concept Spectral LADAR demonstrator that generates spectral point clouds from static scenes. The demonstrator transmits nanosecond supercontinuum pulses generated in a photonic crystal fiber. Currently we use a rapidly tuned receiver with a high-speed InGaAs APD for 25 spectral bands with the future expectation of implementing a linear APD array spectrograph. Each spectral band is independently range resolved with multiple return pulse recognition. This is a critical feature, enabling simultaneous spectral and spatial unmixing of partially obscured objects when not achievable using image fusion of monochromatic LADAR and passive spectral imagers. This enables higher identification confidence in highly cluttered environments such as forested or urban areas (e.g. vehicles behind camouflage or foliage). These environments present challenges for situational awareness and robotic perception which can benefit from the unique attributes of Spectral LADAR. Results from this demonstrator unit are presented for scenes typical of military operations and characterize the operation of the device. The results are discussed here in the context of autonomous vehicle navigation and target recognition.

  7. EO Scanned Micro-LADAR Project

    National Aeronautics and Space Administration — In this SBIR program we will develop, design and build new scanning based micro-ladar sensors with unprecedented small size, weight, and power (SWaP), thereby...

  8. EO Scanned Micro-LADAR Project

    National Aeronautics and Space Administration — In this phase II SBIR we will design, build, test, and deliver new scanning based micro-ladar sensors with unprecedented small size, weight, and power (SWaP),...

  9. Precision and accuracy testing of FMCW ladar-based length metrology.

    Mateo, Ana Baselga; Barber, Zeb W


    The calibration and traceability of high-resolution frequency modulated continuous wave (FMCW) ladar sources is a requirement for their use in length and volume metrology. We report the calibration of FMCW ladar length measurement systems by use of spectroscopy of molecular frequency references HCN (C-band) or CO (L-band) to calibrate the chirp rate of the FMCW sources. Propagating the stated uncertainties from the molecular calibrations provided by NIST and measurement errors provide an estimated uncertainty of a few ppm for the FMCW system. As a test of this calibration, a displacement measurement interferometer with a laser wavelength close to that of our FMCW system was built to make comparisons of the relative precision and accuracy. The comparisons performed show <10  ppm agreement, which was within the combined estimated uncertainties of the FMCW system and interferometer.

  10. Ladar System Identifies Obstacles Partly Hidden by Grass

    Castano, Andres


    A ladar-based system now undergoing development is intended to enable an autonomous mobile robot in an outdoor environment to avoid moving toward trees, large rocks, and other obstacles that are partly hidden by tall grass. The design of the system incorporates the assumption that the robot is capable of moving through grass and provides for discrimination between grass and obstacles on the basis of geometric properties extracted from ladar readings as described below. The system (see figure) includes a ladar system that projects a range-measuring pulsed laser beam that has a small angular width of radians and is capable of measuring distances of reflective objects from a minimum of dmin to a maximum of dmax. The system is equipped with a rotating mirror that scans the beam through a relatively wide angular range of in a horizontal plane at a suitable small height above the ground. Successive scans are performed at time intervals of seconds. During each scan, the laser beam is fired at relatively small angular intervals of radians to make range measurements, so that the total number of range measurements acquired in a scan is Ne = / .

  11. Progress on MEMS-scanned ladar

    Stann, Barry L.; Dammann, John F.; Giza, Mark M.


    The Army Research Laboratory (ARL) has continued to research a short-range ladar imager for use on small unmanned ground vehicles (UGV) and recently small unmanned air vehicles (UAV). The current ladar brassboard is based on a micro-electro-mechanical system (MEMS) mirror coupled to a low-cost pulsed erbium fiber laser. It has a 5-6 Hz frame rate, an image size of 256 (h) x 128 (v) pixels, a 42º x 21º field of regard, 35 m range, eyesafe operation, and 40 cm range resolution with provisions for super-resolution. Experience with driving experiments on small ground robots and efforts to extend the use of the ladar to UAV applications has encouraged work to improve the ladar's performance. The data acquisition system can now capture range data from the three return pulses in a pixel (that is first, last, and largest return), and information such as elapsed time, operating parameters, and data from an inertial navigation system. We will mention the addition and performance of subsystems to obtain eye-safety certification. To meet the enhanced range requirement for the UAV application, we describe a new receiver circuit that improves the signal-to-noise (SNR) several-fold over the existing design. Complementing this work, we discuss research to build a low-capacitance large area detector that may enable even further improvement in receiver SNR. Finally, we outline progress to build a breadboard ladar to demonstrate increased range to 160 m. If successful, this ladar will be integrated with a color camera and inertial navigation system to build a data collection package to determine imaging performance for a small UAV.

  12. Integration and demonstration of MEMS-scanned LADAR for robotic navigation

    Stann, Barry L.; Dammann, John F.; Del Giorno, Mark; DiBerardino, Charles; Giza, Mark M.; Powers, Michael A.; Uzunovic, Nenad


    LADAR is among the pre-eminent sensor modalities for autonomous vehicle navigation. Size, weight, power and cost constraints impose significant practical limitations on perception systems intended for small ground robots. In recent years, the Army Research Laboratory (ARL) developed a LADAR architecture based on a MEMS mirror scanner that fundamentally improves the trade-offs between these limitations and sensor capability. We describe how the characteristics of a highly developed prototype correspond to and satisfy the requirements of autonomous navigation and the experimental scenarios of the ARL Robotics Collaborative Technology Alliance (RCTA) program. In particular, the long maximum and short minimum range capability of the ARL MEMS LADAR makes it remarkably suitable for a wide variety of scenarios from building mapping to the manipulation of objects at close range, including dexterous manipulation with robotic arms. A prototype system was applied to a small (approximately 50 kg) unmanned robotic vehicle as the primary mobility perception sensor. We present the results of a field test where the perception information supplied by the LADAR system successfully accomplished the experimental objectives of an Integrated Research Assessment (IRA).

  13. An edge detection algorithm for imaging ladar

    Qi Wang(王骐); Ziqin Li(李自勤); Qi Li(李琦); Jianfeng Sun(孙剑峰); Juncheng Fu(傅俊诚)


    In this paper, the morphological filter based on parametric edge detection is presented and applied toimaging ladar image with speckle noise. This algorithm and Laplacian of Gaussian (LOG) operator arecompared on edge detection. The experimental results indicate the superior performance of this kind ofthe edge detection.

  14. Miniature Ground Mapping LADAR Project

    National Aeronautics and Space Administration — System & Processes Engineering Corporation (SPEC) proposes a miniature solid state surface imaging LADAR, for imaging the landing areas providing precision...

  15. Anomaly Detection in Clutter using Spectrally Enhanced Ladar

    Chhabra, Puneet S; Hopgood, James R


    Discrete return (DR) Laser Detection and Ranging (Ladar) systems provide a series of echoes that reflect from objects in a scene. These can be first, last or multi-echo returns. In contrast, Full-Waveform (FW)-Ladar systems measure the intensity of light reflected from objects continuously over a period of time. In a camouflaged scenario, e.g., objects hidden behind dense foliage, a FW-Ladar penetrates such foliage and returns a sequence of echoes including buried faint echoes. The aim of this paper is to learn local-patterns of co-occurring echoes characterised by their measured spectra. A deviation from such patterns defines an abnormal event in a forest/tree depth profile. As far as the authors know, neither DR or FW-Ladar, along with several spectral measurements, has not been applied to anomaly detection. This work presents an algorithm that allows detection of spectral and temporal anomalies in FW-Multi Spectral Ladar (FW-MSL) data samples. An anomaly is defined as a full waveform temporal and spectral ...

  16. Target recognition of log-polar ladar range images using moment invariants

    Xia, Wenze; Han, Shaokun; Cao, Jie; Yu, Haoyong


    The ladar range image has received considerable attentions in the automatic target recognition field. However, previous research does not cover target recognition using log-polar ladar range images. Therefore, we construct a target recognition system based on log-polar ladar range images in this paper. In this system combined moment invariants and backpropagation neural network are selected as shape descriptor and shape classifier, respectively. In order to fully analyze the effect of log-polar sampling pattern on recognition result, several comparative experiments based on simulated and real range images are carried out. Eventually, several important conclusions are drawn: (i) if combined moments are computed directly by log-polar range images, translation, rotation and scaling invariant properties of combined moments will be invalid (ii) when object is located in the center of field of view, recognition rate of log-polar range images is less sensitive to the changing of field of view (iii) as object position changes from center to edge of field of view, recognition performance of log-polar range images will decline dramatically (iv) log-polar range images has a better noise robustness than Cartesian range images. Finally, we give a suggestion that it is better to divide field of view into recognition area and searching area in the real application.

  17. Adaptive ladar receiver for multispectral imaging

    Johnson, Kenneth; Vaidyanathan, Mohan; Xue, Song; Tennant, William E.; Kozlowski, Lester J.; Hughes, Gary W.; Smith, Duane D.


    We are developing a novel 2D focal plane array (FPA) with read-out integrated circuit (ROIC) on a single chip for 3D laser radar imaging. The ladar will provide high-resolution range and range-resolved intensity images for detection and identification of difficult targets. The initial full imaging-camera-on-a-chip system will be a 64 by 64 element, 100-micrometers pixel-size detector array that is directly bump bonded to a low-noise 64 by 64 array silicon CMOS-based ROIC. The architecture is scalable to 256 by 256 or higher arrays depending on the system application. The system will provide all the required electronic processing at pixel level and the smart FPA enables directly producing the 3D or 4D format data to be captured with a single laser pulse. The detector arrays are made of uncooled InGaAs PIN device for SWIR imaging at 1.5 micrometers wavelength and cooled HgCdTe PIN device for MWIR imaging at 3.8 micrometers wavelength. We are also investigating concepts using multi-color detector arrays for simultaneous imaging at multiple wavelengths that would provide additional spectral dimension capability for enhanced detection and identification of deep-hide targets. The system is suited for flash ladar imaging, for combat identification of ground targets from airborne platforms, flash-ladar imaging seekers, and autonomous robotic/automotive vehicle navigation and collision avoidance applications.

  18. Ultra-compact, High Resolution, LADAR system for 3D Imaging Project

    National Aeronautics and Space Administration — SiWave proposes to develop an innovative, ultra-compact, high resolution, long range LADAR system to produce a 3D map of the exterior of any object in space such as...

  19. Doublet Pulse Coherent Laser Radar for Tracking of Resident Space Objects

    Prasad, Narasimha S.; Rudd, Van; Shald, Scott; Sandford, Stephen; Dimarcantonio, Albert


    In this paper, the development of a long range ladar system known as ExoSPEAR at NASA Langley Research Center for tracking rapidly moving resident space objects is discussed. Based on 100 W, nanosecond class, near-IR laser, this ladar system with coherent detection technique is currently being investigated for short dwell time measurements of resident space objects (RSOs) in LEO and beyond for space surveillance applications. This unique ladar architecture is configured using a continuously agile doublet-pulse waveform scheme coupled to a closed-loop tracking and control loop approach to simultaneously achieve mm class range precision and mm/s velocity precision and hence obtain unprecedented track accuracies. Salient features of the design architecture followed by performance modeling and engagement simulations illustrating the dependence of range and velocity precision in LEO orbits on ladar parameters are presented. Estimated limits on detectable optical cross sections of RSOs in LEO orbits are discussed.

  20. Surface identification from multiband LADAR reflectance with varied incidence angle via database mapping.

    Guiang, Chona; Jin, Xuemin; Levine, Robert Y


    Incident angle dependencies of LADAR reflection depend on bulk material reflectivity and surface texture properties that can be exploited for surface identification. In this paper, surface identification via multiband LADAR reflected radiance is assessed using the nonconventional exploitation factors data system database. A statistics-based dimension reduction algorithm, stochastic neighborhood embedding (t-SNE), is used to separate the data clouds resulting from the monostatic LADAR reflected radiance and corresponding band ratios. The application of t-SNE to multiband reflected radiance effectively separates the data clouds, making surface identification via multiband LADAR reflectance possible in the presence of unknown incident angle dependencies and uncertainties. It is demonstrated that, for both the multiband monostatic reflected radiance and band ratios, the application of t-SNE mapping yields a significant improvement in surface identification from measurements with unknown or varied incident angles.

  1. Pulse laser imaging amplifier for advanced ladar systems

    Khizhnyak, Anatoliy; Markov, Vladimir; Tomov, Ivan; Murrell, David


    Security measures sometimes require persistent surveillance of government, military and public areas Borders, bridges, sport arenas, airports and others are often surveilled with low-cost cameras. Their low-light performance can be enhanced with laser illuminators; however various operational scenarios may require a low-intensity laser illumination with the object-scattered light intensity lower than the sensitivity of the Ladar image detector. This paper discusses a novel type of high-gain optical image amplifier. The approach enables time-synchronization of the incoming and amplifying signals with accuracy <= 1 ns. The technique allows the incoming signal to be amplified without the need to match the input spectrum to the cavity modes. Instead, the incoming signal is accepted within the spectral band of the amplifier. We have gauged experimentally the performance of the amplifier with a 40 dB gain and an angle of view 20 mrad.

  2. Multi-dimensional, non-contact metrology using trilateration and high resolution FMCW ladar.

    Mateo, Ana Baselga; Barber, Zeb W


    Here we propose, describe, and provide experimental proof-of-concept demonstrations of a multidimensional, non-contact-length metrology system design based on high resolution (millimeter to sub-100 micron) frequency modulated continuous wave (FMCW) ladar and trilateration based on length measurements from multiple, optical fiber-connected transmitters. With an accurate FMCW ladar source, the trilateration-based design provides 3D resolution inherently independent of standoff range and allows self-calibration to provide flexible setup of a field system. A proof-of-concept experimental demonstration was performed using a highly stabilized, 2 THz bandwidth chirped laser source, two emitters, and one scanning emitter/receiver providing 1D surface profiles (2D metrology) of diffuse targets. The measured coordinate precision of <200 microns was determined to be limited by laser speckle issues caused by diffuse scattering of the targets.

  3. Estimating Anthropometric Marker Locations from 3-D LADAR Point Clouds


    ESTIMATING ANTHROPOMETRIC MARKER LOCATIONS FROM 3-D LADAR POINT CLOUDS THESIS Matthew J. Maier, Captain, USAF AFIT/GE/ENG/11-27 DEPARTMENT OF THE AIR...United States. AFIT/GE/ENG/11-27 ESTIMATING ANTHROPOMETRIC MARKER LOCATIONS FROM 3-D LADAR POINT CLOUDS THESIS Presented to the Faculty Department of...2-3 2.2.1 Segmentation from Point Clouds . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2

  4. Development of a 3D Flash LADAR Video Camera for Entry, Decent, and Landing Project

    National Aeronautics and Space Administration — Advanced Scientific Concepts, Inc. (ASC) has developed a 128 x 128 frame, 3D Flash LADAR video camera which produces 3-D point clouds at 30 Hz. Flash LADAR captures...

  5. Development of a 3D Flash LADAR Video Camera for Entry, Decent and Landing Project

    National Aeronautics and Space Administration — Advanced Scientific Concepts, Inc. (ASC) has developed a 128 x 128 frame, 3D Flash LADAR video camera capable of a 30 Hz frame rate. Because Flash LADAR captures an...

  6. Object-Based Attention Guided by An Invisible Object

    Xilin Zhang


    Full Text Available Many studies have demonstrated that attention can be object based. One line of evidence supporting object-based attention showed that observers respond to a target faster when the target and cue are in the same object than when they are in different objects, which is called the same-object advantage. By adopting the double-rectangle cuing paradigm (Egly, Driver, & Rafal, 1994, we tested whether this advantage can occur with invisible rectangles. The original paradigm was slightly modified. Rectangles had a low luminance level against a dark background and were presented for only 10 ms, along with a cue or a target. These two characteristics rendered the rectangles invisible to subjects, as confirmed by a forced-choice test. We found a conventional object-based attention effect even when the rectangles were invisible. We also found that the object-based attention was dependent on the orientation of the rectangles presented along with the target, consistent with the finding by Ho and Yeh (2009. These results suggest that object based attention can be guided by an invisible object in an automatic way, with a minimal influence from the high level top-down control.

  7. A Λ-type soft-aperture LADAR SNR improvement with quantum-enhanced receiver

    Yang, Song; Ruan, Ningjuan; Lin, Xuling; Wu, Zhiqiang


    A quantum-enhanced receiver that uses squeezed vacuum injection (SVI) and phase sensitive amplification (PSA) is in principle capable of obtaining effective signal to noise ratio (SNR) improvement in a soft-aperture homodyne-detection LAser Detection And Ranging (LADAR) system over the classical homodyne LADAR to image a far-away target. Here we investigate the performance of quantum-enhanced receiver in Λ-type soft aperture LADAR for target imaging. We also use fast Fourier transform (FFT) Algorithm to simulate LADAR intensity image, and give a comparison of the SNR improvement of soft aperture case and hard aperture case.

  8. Applications of digital IF receivers and under-sampling technique in ladar

    Song, Zhi-yuan; Zhu, Shao-lan; Dong, Li-jun; Feng, Li; He, Hao-dong


    The traditional technique of phase laser range finder is mixing high frequency signals with analog circuits and filtering them to obtain the useful signal with low frequency. But the analog mixing circuits are susceptible to interference and will bring amplitude attenuation, phase jitter and offset and this way has difficulties in achieving high precision ranging and fast speed ranging at the same time. The method of this paper is based on under-sampling technique with digital synchronous detection and referring to Digital down converter technique of digital IF receiver in radar system. This method not only reduces the complexity of data processing, improves the speed and accuracy of phase detection at the same time, but also reduces requirements for ADC devices and DSP chips in the ladar system by a lower sampling rate. At the same time, the structure of electronic system is global simplified compared with traditional analog ladar system and the anti-jamming is greatly enhanced. So this method has important research value.

  9. High-sensitivity 3 to 5 micron PPLN LADAR wavelength converter system

    Kingsley, S. A.; Sriram, S.; Powers, P. E.


    Remote sensing systems, such as LIDAR, have benefited greatly from nonlinear sources capable of generating tunable mid-infrared wavelengths (3-5 microns). Much work has focused on improving the energy output of these sources so as to improve the system's range. We present a different approach to improving the range by focusing on improving the receiver of a LADAR system employing nonlinear optical techniques. In this paper, we will present results of a receiver system based on frequency converting mid-infrared wavelengths to the 1.5 μm region using Periodically-Poled Lithium Niobate (PPLN). By doing so, optical amplifiers and avalanche photodetectors (APDs) developed for the fiber optics communications industry can be used, thus providing very high detection sensitivity and high speed without the need for cryogenically cooled optical detectors. We will present results of laboratory experiments with 3 μm, 2.5 ns FWHM LADAR pulses that have been converted to 1.5 μm. Detection sensitivities as low as 1.5 x 10^-13 Joules have been demonstrated. The performance of the Peltier-cooled 1.5 μm InGaAs APD quasi photon-counting receiver will be described.

  10. Object manipulation facilitates kind-based object individuation of shape-similar objects

    Kingo, Osman Skjold; Krøjgaard, Peter


    Five experiments investigated the importance of shape and object manipulation when 12-month-olds were given the task of individuating objects representing exemplars of kinds in an event-mapping design. In Experiments 1 and 2, results of the study from Xu, Carey, and Quint (2004, Experiment 4) were......, Experiment 4 revealed that allowing infants to manipulate objects shortly before the individuation task enabled them to individuate shape-similar objects from different categories. In Experiment 5, allowing object manipulation did not induce infants to individuate natural-looking objects from the same...... category. These findings suggest that object manipulation facilitates kind-based individuation of shape-similar objects by 12-month-olds. Keywords: Object individuation; Object shape; Object manipulation; Kind representations; Infancy...

  11. Terrain classification of ladar data for bare earth determination

    Neuenschwander, Amy L.; Magruder, Lori A.


    Terrain classification, or bare earth extraction, is an important component to LADAR data analysis. The terrain classification approach presented in this effort utilizes an adaptive lower envelope follower (ALEF) with an adaptive gradient operation for accommodations of local topography and roughness. In order to create a more robust capability, the ALEF was modified to become a strictly data driven process that facilitates a quick production of the data product without the subjective component associated with user inputs. This automated technique was tested on existing LADAR surveys over Wyoming's Powder River Basin and the John Starr Memorial Forest in Mississippi, both locations with dynamic topographic features. The results indicate a useful approach in terms of operational time and accuracy of the final bare earth recovery with the advantage of being fully data driven.

  12. A Nonparametric Approach to Segmentation of Ladar Images


    This can be done through unsupervised classifi- cation (where the class structure and true labels are unknown) and supervised classification (where the...Ladar Images Distribution-free classification methodology in image processing has been widely used in the medical and hyperspectral imaging communities... hyperspectral image analysis and other high-dimensional applications [105]. 7.2 Classification Methodology As has been stated previously in Sec. 7.1, the

  13. Perceptual Load Modulates Object-Based Attention

    Ho, Ming-Chou; Atchley, Paul


    Two experimental series are reported using both reaction time (RT) and a data-limited perceptual report to examine the effects of perceptual load on object-based attention. Perceptual load was manipulated across 3 levels by increasing the complexity of perceptual judgments. Data from the RT-based experiments showed object-based effects when the…

  14. View-based 3-D object retrieval

    Gao, Yue


    Content-based 3-D object retrieval has attracted extensive attention recently and has applications in a variety of fields, such as, computer-aided design, tele-medicine,mobile multimedia, virtual reality, and entertainment. The development of efficient and effective content-based 3-D object retrieval techniques has enabled the use of fast 3-D reconstruction and model design. Recent technical progress, such as the development of camera technologies, has made it possible to capture the views of 3-D objects. As a result, view-based 3-D object retrieval has become an essential but challenging res

  15. Characterization of high resolution range and Doppler LADAR

    Flores, Benjamin C.; Verdin, Berenice


    Compared to microwave radar systems, chaotic ladar has the potential for providing a range resolution well into the mm range. The purpose of this project is to determine the signal processing schemes required to extract range and Doppler information from a chaotic signal scattered by environmental targets. Specifically, a ladar would be driven into the coherence collapse through an external optical resonator, thus generating a chaotic electromagnetic field with a wide rms bandwidth of several GHz. The reflected field would be processed though optical correlation to extract range and Doppler information. Simulations show that the power spectral density properties of the field are dependent on the Lyapunov exponent of the chaotic field, which be exploited to obtain optimum range resolution. A complete statistical analysis of the wideband ambiguity function of the field reveals that the signal has better performance than noise-like signals generated via electro-optic amplitude modulation, thus allowing for high resolution imaging of terrains with pseudo random reflectivity variations.

  16. Coding Transparency in Object-Based Video

    Aghito, Shankar Manuel; Forchhammer, Søren


    A novel algorithm for coding gray level alpha planes in object-based video is presented. The scheme is based on segmentation in multiple layers. Different coders are specifically designed for each layer. In order to reduce the bit rate, cross-layer redundancies as well as temporal correlation...

  17. Spatio-activity based object detection

    Springett, Jarrad


    We present the SAMMI lightweight object detection method which has a high level of accuracy and robustness, and which is able to operate in an environment with a large number of cameras. Background modeling is based on DCT coefficients provided by cameras. Foreground detection uses similarity in temporal characteristics of adjacent blocks of pixels, which is a computationally inexpensive way to make use of object coherence. Scene model updating uses the approximated median method for improved performance. Evaluation at pixel level and application level shows that SAMMI object detection performs better and faster than the conventional Mixture of Gaussians method.

  18. Science objectives in the lunar base advocacy

    Mendell, Wendell W.


    The author considers the potential function of astronomy in planning for a lunar base during the 21st century. He is one of the leading advocates for a permanent settlement on the Moon and has given considerable thought to the possible impact of such a station on science. He considers the rationale for a lunar base, research on the Moon, and the definition of science objectives.

  19. Invariant Object Recognition Based on Extended Fragments

    Evgeniy eBart


    Full Text Available Visual appearance of natural objects is profoundly affected by viewing conditions such as viewpoint and illumination. Human subjects can nevertheless compensate well for variations in these viewing conditions. The strategies that the visual system uses to accomplish this are largely unclear. Previous computational studies have suggested that in principle, certain types of object fragments (rather than whole objects can be used for invariant recognition. However, whether the human visual system is actually capable of using this strategy remains unknown. Here, we show that human observers can achieve illumination invariance by using object fragments that carry the relevant information. To determine this, we have used novel, but naturalistic, 3-D visual objects called ‘digital embryos’. Using novel instances of whole embryos, not fragments, we trained subjects to recognize individual embryos across illuminations. We then tested the illumination-invariant object recognition performance of subjects using fragments. We found that the performance was strongly correlated with the mutual information (MI of the fragments, provided that MI value took variations in illumination into consideration. This correlation was not attributable to any systematic differences in task difficulty between different fragments. These results reveal two important principles of invariant object recognition. First, the subjects can achieve invariance at least in part by compensating for the changes in the appearance of small local features, rather than of whole objects. Second, the subjects do not always rely on generic or pre-existing invariance of features (i.e., features whose appearance remains largely unchanged by variations in illumination, and are capable of using learning to compensate for appearance changes when necessary. These psychophysical results closely fit the predictions of earlier computational studies of fragment-based invariant object recognition.

  20. Circular object recognition based on shape parameters

    Chen Aijun; Li Jinzong; Zhu Bing


    To recognize circular objects rapidly in satellite remote sensing imagery, an approach using their geometry properties is presented.The original image is segmented to be a binary one by one dimension maximum entropy threshold algorithm and the binary image is labeled with an algorithm based on recursion technique.Then, shape parameters of all labeled regions are calculated and those regions with shape parameters satisfying certain conditions are recognized as circular objects.The algorithm is described in detail, and comparison experiments with the randomized Hough transformation (RHT) are also provided.The experimental results on synthetic images and real images show that the proposed method has the merits of fast recognition rate, high recognition efficiency and the ability of anti-noise and anti-jamming.In addition, the method performs well when some circular objects are little deformed and partly misshapen.


    Altamirano Robles Luis Carlos


    Full Text Available The use of clustering algorithms for partition to establish a hierarchical structure in a library of object models based on appearance is deployed. The main contribution corresponds to a novel and intuitive algorithm for clustering of models based on their appearance, closer to “human behavior”. This divides the complete set into subclasses. Immediately, divides each of these in a number of predefined groups to complete the levels of hierarchy that the user wants. Whose main purpose is to obtain a competitive classification compared to what a human would perform.

  2. Object Based Middleware for Grid Computing

    S. Muruganantham


    Full Text Available Problem statement: “Grid” computing has emerged as an important new field, distinguished from conventional distributed computing by its focus on large-scale resource sharing, innovative applications and, in some cases, high-performance orientation. The role of middleware is to ease the task of designing, programming and managing distributed applications by providing a simple, consistent and integrated distributed programming environment. Essentially, middleware is a distributed software layer, which abstracts over the complexity and heterogeneity of the underlying distributed environment with its multitude of network technologies, machine architectures, operating systems and programming languages. Approach: This study brought out the development of supportive middleware to manage resources and distributed workload across multiple administrative boundaries is of central importance to Grid computing. Active middleware services that perform look-up, scheduling and staging are being developed that allow users to identify and utilize appropriate resources that provide sustainable system and user-level qualities of service. Results: Different middleware platforms support different programming models. Perhaps the most popular model is object-based middleware in which applications are structured into objects that interact via location transparent method invocation. Conclusion: The Object Management Group’s CORBA platform offer an Interface Definition Language (IDL which is used to abstract over the fact that objects can be implemented in any suitable programming language, an object request broker which is responsible for transparently directing method invocations to the appropriate target object and a set of services such as naming, time, transactions, replication which further enhance the programming environment.

  3. Robust feature-based object tracking

    Han, Bing; Roberts, William; Wu, Dapeng; Li, Jian


    Object tracking is an important component of many computer vision systems. It is widely used in video surveillance, robotics, 3D image reconstruction, medical imaging, and human computer interface. In this paper, we focus on unsupervised object tracking, i.e., without prior knowledge about the object to be tracked. To address this problem, we take a feature-based approach, i.e., using feature points (or landmark points) to represent objects. Feature-based object tracking consists of feature extraction and feature correspondence. Feature correspondence is particularly challenging since a feature point in one image may have many similar points in another image, resulting in ambiguity in feature correspondence. To resolve the ambiguity, algorithms, which use exhaustive search and correlation over a large neighborhood, have been proposed. However, these algorithms incur high computational complexity, which is not suitable for real-time tracking. In contrast, Tomasi and Kanade's tracking algorithm only searches corresponding points in a small candidate set, which significantly reduces computational complexity; but the algorithm may lose track of feature points in a long image sequence. To mitigate the limitations of the aforementioned algorithms, this paper proposes an efficient and robust feature-based tracking algorithm. The key idea of our algorithm is as below. For a given target feature point in one frame, we first find a corresponding point in the next frame, which minimizes the sum-of-squared-difference (SSD) between the two points; then we test whether the corresponding point gives large value under the so-called Harris criterion. If not, we further identify a candidate set of feature points in a small neighborhood of the target point; then find a corresponding point from the candidate set, which minimizes the SSD between the two points. The algorithm may output no corresponding point due to disappearance of the target point. Our algorithm is capable of tracking

  4. Water Detection Based on Object Reflections

    Rankin, Arturo L.; Matthies, Larry H.


    Water bodies are challenging terrain hazards for terrestrial unmanned ground vehicles (UGVs) for several reasons. Traversing through deep water bodies could cause costly damage to the electronics of UGVs. Additionally, a UGV that is either broken down due to water damage or becomes stuck in a water body during an autonomous operation will require rescue, potentially drawing critical resources away from the primary operation and increasing the operation cost. Thus, robust water detection is a critical perception requirement for UGV autonomous navigation. One of the properties useful for detecting still water bodies is that their surface acts as a horizontal mirror at high incidence angles. Still water bodies in wide-open areas can be detected by geometrically locating the exact pixels in the sky that are reflecting on candidate water pixels on the ground, predicting if ground pixels are water based on color similarity to the sky and local terrain features. But in cluttered areas where reflections of objects in the background dominate the appearance of the surface of still water bodies, detection based on sky reflections is of marginal value. Specifically, this software attempts to solve the problem of detecting still water bodies on cross-country terrain in cluttered areas at low cost.

  5. Object Extraction Based on Evolutionary Morphological Processing

    LI Bin; PAN Li


    This paper introduces a novel technique for object detection using genetic algorithms and morphological processing. The method employs a kind of object oriented structure element, which is derived by genetic algorithms. The population of morphological filters is iteratively evaluated according to a statistical performance index corresponding to object extraction ability, and evolves into an optimal structuring element using the evolution principles of genetic search. Experimental results of road extraction from high resolution satellite images are presented to illustrate the merit and feasibility of the proposed method.

  6. Unambiguous range-Doppler LADAR processing using 2 giga-sample-per-second noise waveforms

    Cole, Z. [S2 Corporation, 2310 University Way 4-1, Bozeman, MT 59715 (United States)]. E-mail:; Roos, P.A. [Spectrum Lab, Montana State University, P.O. Box 173510, Bozeman, MT 59717 (United States); Berg, T. [S2 Corporation, 2310 University Way 4-1, Bozeman, MT 59715 (United States); Kaylor, B. [S2 Corporation, 2310 University Way 4-1, Bozeman, MT 59715 (United States); Merkel, K.D. [S2 Corporation, 2310 University Way 4-1, Bozeman, MT 59715 (United States); Babbitt, W.R. [Spectrum Lab, Montana State University, P.O. Box 173510, Bozeman, MT 59717 (United States); Reibel, R.R. [S2 Corporation, 2310 University Way 4-1, Bozeman, MT 59715 (United States)


    We demonstrate sub-nanosecond range and unambiguous sub-50-Hz Doppler resolved laser radar (LADAR) measurements using spectral holographic processing in rare-earth ion doped crystals. The demonstration utilizes pseudo-random-noise 2 giga-sample-per-second baseband waveforms modulated onto an optical carrier.

  7. The influence of object similarity and orientation on object-based cueing.

    Hein, Elisabeth; Blaschke, Stefan; Rolke, Bettina


    Responses to targets that appear at a noncued position within the same object (invalid-same) compared to a noncued position at an equidistant different object (invalid-different) tend to be faster and more accurate. These cueing effects have been taken as evidence that visual attention can be object based (Egly, Driver, & Rafal, Journal of Experimental Psychology: General, 123, 161-177, 1994). Recent findings, however, have shown that the object-based cueing effect is influenced by object orientation, suggesting that the cueing effect might be due to a more general facilitation of attentional shifts across the horizontal meridian (Al-Janabi & Greenberg, Attention, Perception, & Psychophysics, 1-17, 2016; Pilz, Roggeveen, Creighton, Bennet, & Sekuler, PLOS ONE, 7, e30693, 2012). The aim of this study was to investigate whether the object-based cueing effect is influenced by object similarity and orientation. According to the object-based attention account, objects that are less similar to each other should elicit stronger object-based cueing effects independent of object orientation, whereas the horizontal meridian theory would not predict any effect of object similarity. We manipulated object similarity by using a color (Exp. 1, Exp. 2A) or shape change (Exp. 2B) to distinguish two rectangles in a variation of the classic two-rectangle paradigm (Egly et al., 1994). We found that the object-based cueing effects were influenced by the orientation of the rectangles and strengthened by object dissimilarity. We suggest that object-based cueing effects are strongly affected by the facilitation of attention along the horizontal meridian, but that they also have an object-based attentional component, which is revealed when the dissimilarity between the presented objects is accentuated.

  8. Contour-based object orientation estimation

    Alpatov, Boris; Babayan, Pavel


    Real-time object orientation estimation is an actual problem of computer vision nowadays. In this paper we propose an approach to estimate an orientation of objects lacking axial symmetry. Proposed algorithm is intended to estimate orientation of a specific known 3D object, so 3D model is required for learning. The proposed orientation estimation algorithm consists of 2 stages: learning and estimation. Learning stage is devoted to the exploring of studied object. Using 3D model we can gather set of training images by capturing 3D model from viewpoints evenly distributed on a sphere. Sphere points distribution is made by the geosphere principle. It minimizes the training image set. Gathered training image set is used for calculating descriptors, which will be used in the estimation stage of the algorithm. The estimation stage is focusing on matching process between an observed image descriptor and the training image descriptors. The experimental research was performed using a set of images of Airbus A380. The proposed orientation estimation algorithm showed good accuracy (mean error value less than 6°) in all case studies. The real-time performance of the algorithm was also demonstrated.

  9. Interval-based Specification of Concurrent Objects

    Løvengreen, Hans Henrik; Sørensen, Morten U.


    We propose a logic for specifying the behaviour of concurrent objects, ie. concurrent entities that invoke operation of each other. The logic is an interval logic whith operation invocatins as primitive formulas. The strengths and deficiencies of the logic are illustrated by specifying a variety...

  10. Exploring the relationship between object realism and object-based attention effects.

    Roque, Nelson; Boot, Walter R


    Visual attention prioritizes processing of locations in space, and evidence also suggests that the benefits of attention can be shaped by the presence of objects (object-based attention). However, the prevalence of object-based attention effects has been called into question recently by evidence from a large-sampled study employing classic attention paradigms (Pilz et al., 2012). We conducted two experiments to explore factors that might determine when and if object-based attention effects are observed, focusing on the degree to which the concreteness and realism of objects might contribute to these effects. We adapted the classic attention paradigm first reported by Egly, Driver, and Rafal (1994) by replacing abstract bar stimuli in some conditions with objects that were more concrete and familiar to participants: items of silverware. Furthermore, we varied the realism of these items of silverware, presenting either cartoon versions or photo-realistic versions. Contrary to predictions, increased realism did not increase the size of object-based effects. In fact, no clear object-based effects were observed in either experiment, consistent with previous failures to replicate these effects in similar paradigms. While object-based attention may exist, and may have important influences on how we parse the visual world, these and other findings suggest that the two-object paradigm typically relied upon to study object-based effects may not be the best paradigm to investigate these issues.

  11. Monitoring objects orbiting earth using satellite-based telescopes

    Olivier, Scot S.; Pertica, Alexander J.; Riot, Vincent J.; De Vries, Willem H.; Bauman, Brian J.; Nikolaev, Sergei; Henderson, John R.; Phillion, Donald W.


    An ephemeris refinement system includes satellites with imaging devices in earth orbit to make observations of space-based objects ("target objects") and a ground-based controller that controls the scheduling of the satellites to make the observations of the target objects and refines orbital models of the target objects. The ground-based controller determines when the target objects of interest will be near enough to a satellite for that satellite to collect an image of the target object based on an initial orbital model for the target objects. The ground-based controller directs the schedules to be uploaded to the satellites, and the satellites make observations as scheduled and download the observations to the ground-based controller. The ground-based controller then refines the initial orbital models of the target objects based on the locations of the target objects that are derived from the observations.

  12. Improving Multiple Surface Range Estimation of a 3-Dimensional FLASH LADAR in the Presence of Atmospheric Turbulence


    through clouds among others. Multi-surface ranging with the use of 3-D FLASH LADAR can also be useful in accurately discriminating camouflaged targets of...Unfortunately, as a consequence of nonuniform heating and cooling of the Earth’s atmosphere, the temperature-induced inhomogeneities of the refractive index...optical sensing applications, and especially in the case of 3-D FLASH LADAR. The complexity of the electronics coupled with conventional optics often

  13. Image-Based Multiresolution Implicit Object Modeling

    Sarti Augusto


    Full Text Available We discuss two image-based 3D modeling methods based on a multiresolution evolution of a volumetric function′s level set. In the former method, the role of the level set implosion is to fuse ("sew" and "stitch" together several partial reconstructions (depth maps into a closed model. In the later, the level set′s implosion is steered directly by the texture mismatch between views. Both solutions share the characteristic of operating in an adaptive multiresolution fashion, in order to boost up computational efficiency and robustness.

  14. On a temporal logic for object-based systems

    Distefano, Dino; Katoen, Joost-Pieter; Rensink, Arend


    This paper presents a logic, called BOTL (Object-Based Temporal Logic), that facilitates the specification of dynamic and static properties of object-based systems. The logic is based on the branching temporal logic CTL and the Object Constraint Language (OCL), an optional part of the UML standard f

  15. On a Temporal Logic for Object-Based Systems

    Distefano, Dino; Katoen, Joost-Pieter; Rensink, Arend; Smith, Scott F.; Talcott, Carolyn L.


    This paper presents a logic, called BOTL (Object-Based Temporal Logic), that facilitates the specification of dynamic and static properties of object-based systems. The logic is based on the branching temporal logic CTL and the Object Constraint Language (OCL), an optional part of the UML standard f

  16. Object Conveyance Algorithm for Multiple Mobile Robots based on Object Shape and Size

    Purnomo Sejati


    Full Text Available This paper describes a determination method of a number of a team for multiple mobile robot object conveyance. The number of robot on multiple mobile robot systems is the factor of complexity on robots formation and motion control. In our previous research, we verified the use of the complex-valued neural network for controlling multiple mobile robots in object conveyance problem. Though it is a significant issue to develop effective determination team member for multiple mobile robot object conveyance, few studies have been done on it. Therefore, we propose an algorithm for determining the number of the team member on multiple mobile robot object conveyance with grasping push. The team member is determined based on object weight to obtain appropriate formation. First, the object shape and size measurement is carried out by a surveyor robot that approaches and surrounds the object. During surrounding the object, the surveyor robot measures its distance to the object and records for estimating the object shape and size. Since the object shape and size are estimated, the surveyor robot makes initial push position on the estimated push point and calls additional robots for cooperative push. The algorithm is validated in several computer simulations with varying object shape and size. As a result, the proposed algorithm is promising for minimizing the number of the robot on multiple mobile robot object conveyance.

  17. Bound on range precision for shot-noise limited ladar systems.

    Johnson, Steven; Cain, Stephen


    The precision of ladar range measurements is limited by noise. The fundamental source of noise in a laser signal is the random time between photon arrivals. This phenomenon, called shot noise, is modeled as a Poisson random process. Other noise sources in the system are also modeled as Poisson processes. Under the Poisson-noise assumption, the Cramer-Rao lower bound (CRLB) on range measurements is derived. This bound on the variance of any unbiased range estimate is greater than the CRLB derived by assuming Gaussian noise of equal variance. Finally, it is shown that, for a ladar capable of dividing a fixed amount of energy into multiple laser pulses, the range precision is maximized when all energy is transmitted in a single pulse.

  18. A LADAR bare earth extraction technique for diverse topography and complex scenes

    Neuenschwander, Amy L.; Stevenson, Terry H.; Magruder, Lori A.


    Bare earth extraction is an important component to LADAR data analysis in terms of terrain classification. The challenge in providing accurate digital models is augmented when there is diverse topography within the data set or complex combinations of vegetation and built structures. A successful approach provides a flexible methodology (adaptable for topography and/or environment) that is capable of integrating multiple ladar point cloud data attributes. A newly developed approach (TE-SiP) uses a 2nd and 3rd order spatial derivative for each point in the DEM to determine sets of contiguous regions of similar elevation. Specifically, the derivative of the central point represents the curvature of the terrain at that position. Contiguous sets of high (positive or negative) values define sharp edges such as building edges or cliffs. This method is independent of the slope, such that very steep, but continuous topography still have relatively low curvature values and are preserved in the terrain classification. Next, a recursive segmentation method identifies unique features of homogeneity on the surface separated by areas of high curvature. An iterative selection process is used to eliminate regions containing buildings or vegetation from the terrain surface. This technique was tested on a variety of existing LADAR surveys, each with varying levels of topographic complexity. The results shown here include developed and forested regions in the Dominican Republic.

  19. Temporal segmentation of video objects for hierarchical object-based motion description.

    Fu, Yue; Ekin, Ahmet; Tekalp, A Murat; Mehrotra, Rajiv


    This paper describes a hierarchical approach for object-based motion description of video in terms of object motions and object-to-object interactions. We present a temporal hierarchy for object motion description, which consists of low-level elementary motion units (EMU) and high-level action units (AU). Likewise, object-to-object interactions are decomposed into a hierarchy of low-level elementary reaction units (ERU) and high-level interaction units (IU). We then propose an algorithm for temporal segmentation of video objects into EMUs, whose dominant motion can be described by a single representative parametric model. The algorithm also computes a representative (dominant) affine model for each EMU. We also provide algorithms for identification of ERUs and for classification of the type of ERUs. Experimental results demonstrate that segmenting the life-span of video objects into EMUS and ERUs facilitates the generation of high-level visual summaries for fast browsing and navigation. At present, the formation of high-level action and interaction units is done interactively. We also provide a set of query-by-example results for low-level EMU retrieval from a database based on similarity of the representative dominant affine models.

  20. Tracking target objects orbiting earth using satellite-based telescopes

    De Vries, Willem H; Olivier, Scot S; Pertica, Alexander J


    A system for tracking objects that are in earth orbit via a constellation or network of satellites having imaging devices is provided. An object tracking system includes a ground controller and, for each satellite in the constellation, an onboard controller. The ground controller receives ephemeris information for a target object and directs that ephemeris information be transmitted to the satellites. Each onboard controller receives ephemeris information for a target object, collects images of the target object based on the expected location of the target object at an expected time, identifies actual locations of the target object from the collected images, and identifies a next expected location at a next expected time based on the identified actual locations of the target object. The onboard controller processes the collected image to identify the actual location of the target object and transmits the actual location information to the ground controller.


    XIANG Guishan; WANG Xuanyin; LIANG Dongtai


    In YCbCr colorspace, a method is proposed to reconstruct the background and extract moving objects based on the Gaussian model of chroma components. Background model is updated according to changes of chroma components. In order to eliminate the disturbance of shadow, a shadow detecting principle is proposed in YCbCr colorspace. A Kalman filter is introduced to estimate objects' positions in the image and then the pedestrian is tracked according to its information of shape. Experiments show that the background reconstruction and updating are successful, object extraction and shadow suppression are satisfactory, and real-time and reliable tracking is realized.

  2. a New Object Based Method for Automated Extraction of Urban Objects from Airborne Sensors Data

    Moussa, A.; El-Sheimy, N.


    The classification of urban objects such as buildings, trees and roads from airborne sensors data is an essential step in numerous mapping and modelling applications. The automation of this step is greatly needed as the manual processing is costly and time consuming. The increasing availability of airborne sensors data such as aerial imagery and LIDAR data offers new opportunities to develop more robust approaches for automatic classification. These approaches should integrate these data sources that have different characteristics to exceed the accuracy achieved using any individual data source. The proposed approach presented in this paper fuses the aerial images data with single return LIDAR data to extract buildings and trees for an urban area. Object based analysis is adopted to segment the entire DSM data into objects based on height variation. These objects are preliminarily classified into buildings, trees, and ground. This primary classification is used to compute the height to ground for each object to help improve the accuracy of the second phase of classification. The overlapping perspective aerial images are used to build an ortho-photo to derive a vegetation index value for each object. The second phase of classification is performed based on the height to ground and the vegetation index of each object. The proposed approach has been tested using three areas in the centre of the city of Vaihingen provided by ISPRS test project on urban classification and 3D building reconstruction. These areas have historic buildings having rather complex shapes, few high-rising residential buildings that are surrounded by trees, and a purely residential area with small detached houses. The results of the proposed approach are presented based on a reference solution for evaluation purposes. The classification evaluation exhibits highly successful classification results of buildings class. The proposed approach follows the exact boundary of trees based on LIDAR data

  3. Perceptual Object Extraction Based on Saliency and Clustering

    Qiaorong Zhang


    Full Text Available Object-based visual attention has received an increasing interest in recent years. Perceptual object is the basic attention unit of object-based visual attention. The definition and extraction of perceptual objects is one of the key technologies in object-based visual attention computation model. A novel perceptual object definition and extraction method is proposed in this paper. Based on Gestalt theory and visual feature integration theory, perceptual object is defined using homogeneity region, salient region and edges. An improved saliency map generating algorithm is employed first. Based on the saliency map, salient edges are extracted. Then graph-based clustering algorithm is introduced to get homogeneity regions in the image. Finally an integration strategy is adopted to combine salient edges and homogeneity regions to extract perceptual objects. The proposed perceptual object extraction method has been tested on lots of natural images. Experiment results and analysis are presented in this paper also. Experiment results show that the proposed method is reasonable and valid.

  4. Area Variation Based Color Snake Algorithm for Moving Object Tracking

    Shoum-ik ROYCHOUDHURY; Young-joon HAN


    A snake algorithm has been known that it has a strong point in extracting the exact contour of an object.But it is apt to be influenced by scattered edges around the control points.Since the shape of a moving object in 2D image changes a lot due ta its rotation and translation in the 3D space,the conventional algorithm that takes into account slowly moving objects cannot provide an appropriate solution.To utilize the advantages of the snake algrithm while minimizing the drawbacks,this paper proposes the area variation based color snake algorithm for moving object tracking.The proposed algorithm includes a new energy term which is used for preserving the shape of an object between two consecutive inages.The proposed one can also segment precisely interesting objects on complex image since it is based on color information.Experiment results show that the proposed algorithm is very effective in various environments.

  5. Context based Coding of Quantized Alpha Planes for Video Objects

    Aghito, Shankar Manuel; Forchhammer, Søren


    In object based video, each frame is a composition of objects that are coded separately. The composition is performed through the alpha plane that represents the transparency of the object. We present an alternative to MPEG-4 for coding of alpha planes that considers their specific properties....... Comparisons in terms of rate and distortion are provided, showing that the proposed coding scheme for still alpha planes is better than the algorithms for I-frames used in MPEG-4....

  6. Image-based object recognition in man, monkey and machine.

    Tarr, M J; Bülthoff, H H


    Theories of visual object recognition must solve the problem of recognizing 3D objects given that perceivers only receive 2D patterns of light on their retinae. Recent findings from human psychophysics, neurophysiology and machine vision provide converging evidence for 'image-based' models in which objects are represented as collections of viewpoint-specific local features. This approach is contrasted with 'structural-description' models in which objects are represented as configurations of 3D volumes or parts. We then review recent behavioral results that address the biological plausibility of both approaches, a well as some of their computational advantages and limitations. We conclude that, although the image-based approach holds great promise, it has potential pitfalls that may be best overcome by including structural information. Thus, the most viable model of object recognition may be one that incorporates the most appealing aspects of both image-based and structural description theories.

  7. 2000 fps multi-object tracking based on color histogram

    Gu, Qingyi; Takaki, Takeshi; Ishii, Idaku


    In this study, we develop a real-time, color histogram-based tracking system for multiple color-patterned objects in a 512×512 image at 2000 fps. Our system can simultaneously extract the positions, areas, orientation angles, and color histograms of multiple objects in an image using the hardware implementation of a multi-object, color histogram extraction circuit module on a high-speed vision platform. It can both label multiple objects in an image consisting of connected components and calculate their moment features and 16-bin hue-based color histograms using cell-based labeling. We demonstrate the performance of our system by showing several experimental results: (1) tracking of multiple color-patterned objects on a plate rotating at 16 rps, and (2) tracking of human hand movement with two color-patterned drinking bottles.

  8. Attention shift-based multiple saliency object segmentation

    Wu, Chang-Wei; Zhao, Hou-Qiang; Cao, Song-Xiao; Xiang, Ke; Wang, Xuan-Yin


    Object segmentation is an important but highly challenging problem in computer vision and image processing. An attention shift-based multiple saliency object segmentation model, called ASMSO, is introduced. The proposed ASMSO could produce a pool of potential object regions for each saliency object and be applicable to multiple saliency object segmentation. The potential object regions are produced by combing the methods of gPb-owt-ucm and min-cut graph, whereas the saliency objects are detected by a visual attention model with an attention shift mechanism. In order to deal with various scenes, the model attention shift-based multiple saliency object segmentation (ASMSO) contains different features which include not only traditional features, such as color, uniform, and texture, but also a new position feature originating from proximity of Gestalt theory. Experiments on the training set of PASCAL VOC2012 segmentation dataset not only show that traditional color feature and the proposed position feature work much better than features of texture and uniformity, but also prove that ASMSO is suitable for multiple object segmentation. In addition, experiments on a traditional saliency dataset show that ASMSO could also be applied to traditional saliency object segmentation and performs much better than the state-of-the-art method.

  9. Content-Based Object Movie Retrieval and Relevance Feedbacks

    Lee Greg C


    Full Text Available Object movie refers to a set of images captured from different perspectives around a 3D object. Object movie provides a good representation of a physical object because it can provide 3D interactive viewing effect, but does not require 3D model reconstruction. In this paper, we propose an efficient approach for content-based object movie retrieval. In order to retrieve the desired object movie from the database, we first map an object movie into the sampling of a manifold in the feature space. Two different layers of feature descriptors, dense and condensed, are designed to sample the manifold for representing object movies. Based on these descriptors, we define the dissimilarity measure between the query and the target in the object movie database. The query we considered can be either an entire object movie or simply a subset of views. We further design a relevance feedback approach to improving retrieved results. Finally, some experimental results are presented to show the efficacy of our approach.

  10. Approximate Bayesian methods for kernel-based object tracking

    Zivkovic, Z.; Cemgil, A.T.; Kröse, B.


    A framework for real-time tracking of complex non-rigid objects is presented. The object shape is approximated by an ellipse and its appearance by histogram based features derived from local image properties. An efficient search procedure is used to find the image region with a histogram most simila

  11. Ellipse Fitting Based Approach for Extended Object Tracking

    Borui Li


    Full Text Available With the increase of sensors’ resolution, traditional object tracking technology, which ignores object’s physical extension, gradually becomes inappropriate. Extended object tracking (EOT technology is able to obtain more information about the object through jointly estimating both centroid’s dynamic state and physical extension of the object. Random matrix based approach is a promising method for EOT. It uses ellipse/ellipsoid to describe the physical extension of the object. In order to reduce the physical extension estimation error when object maneuvers, the relationship between ellipse/ellipsoid and symmetrical positive definite matrix is analyzed at first. On this basis, ellipse/ellipsoid fitting based approach (EFA for EOT is proposed based on the measurement model and centroid’s dynamic model of random matrix based EOT approach. Simulation results show that EFA is effective. The physical extension estimation error of EFA is lower than those of random matrix based approaches when object maneuvers. Besides, the estimation error of centroid’s dynamic state of EFA is also lower.

  12. A Method of Object-based De-duplication

    Fang Yan


    Full Text Available Today, the world is increasingly awash in more and more unstructured data, not only because of the Internet, but also because data that used to be collected on paper or media such as film, DVDs and compact discs has moved online [1]. Most of this data is unstructured and in diverse formats such as e-mail, documents, graphics, images, and videos. In managing unstructured data complexity and scalability, object storage has a clear advantage. Object-based data de-duplication is the current most advanced method and is the effective solution for detecting duplicate data. It can detect common embedded data for the first backup across completely unrelated files and even when physical block layout changes. However, almost all of the current researches on data de-duplication do not consider the content of different file types, and they do not have any knowledge of the backup data format. It has been proven that such method cannot achieve optimal performance for compound files.In our proposed system, we will first extract objects from files, Object_IDs are then obtained by applying hash function to the objects. The resulted Object_IDs are used to build as indexing keys in B+ tree like index structure, thus, we avoid the need for a full object index, the searching time for the duplicate objects reduces to O(log n.We introduce a new concept of a duplicate object resolver. The object resolver mediates access to all the objects and is a central point for managing all the metadata and indexes for all the objects. All objects are addressable by their IDs which is unique in the universe. The resolver stores metadata with triple format. This improved metadata management strategy allows us to set, add and resolve object properties with high flexibility, and allows the repeated use of the same metadata among duplicate object.

  13. Monocular model-based 3D tracking of rigid objects

    Lepetit, Vincent


    Many applications require tracking complex 3D objects. These include visual serving of robotic arms on specific target objects, Augmented Reality systems that require real time registration of the object to be augmented, and head tracking systems that sophisticated interfaces can use. Computer vision offers solutions that are cheap, practical and non-invasive. ""Monocular Model-Based 3D Tracking of Rigid Objects"" reviews the different techniques and approaches that have been developed by industry and research. First, important mathematical tools are introduced: camera representation, robust e

  14. A practical approach to object based requirements analysis

    Drew, Daniel W.; Bishop, Michael


    Presented here is an approach developed at the Unisys Houston Operation Division, which supports the early identification of objects. This domain oriented analysis and development concept is based on entity relationship modeling and object data flow diagrams. These modeling techniques, based on the GOOD methodology developed at the Goddard Space Flight Center, support the translation of requirements into objects which represent the real-world problem domain. The goal is to establish a solid foundation of understanding before design begins, thereby giving greater assurance that the system will do what is desired by the customer. The transition from requirements to object oriented design is also promoted by having requirements described in terms of objects. Presented is a five step process by which objects are identified from the requirements to create a problem definition model. This process involves establishing a base line requirements list from which an object data flow diagram can be created. Entity-relationship modeling is used to facilitate the identification of objects from the requirements. An example is given of how semantic modeling may be used to improve the entity-relationship model and a brief discussion on how this approach might be used in a large scale development effort.

  15. A Secure and Robust Object-Based Video Authentication System

    Dajun He


    Full Text Available An object-based video authentication system, which combines watermarking, error correction coding (ECC, and digital signature techniques, is presented for protecting the authenticity between video objects and their associated backgrounds. In this system, a set of angular radial transformation (ART coefficients is selected as the feature to represent the video object and the background, respectively. ECC and cryptographic hashing are applied to those selected coefficients to generate the robust authentication watermark. This content-based, semifragile watermark is then embedded into the objects frame by frame before MPEG4 coding. In watermark embedding and extraction, groups of discrete Fourier transform (DFT coefficients are randomly selected, and their energy relationships are employed to hide and extract the watermark. The experimental results demonstrate that our system is robust to MPEG4 compression, object segmentation errors, and some common object-based video processing such as object translation, rotation, and scaling while securely preventing malicious object modifications. The proposed solution can be further incorporated into public key infrastructure (PKI.

  16. A Secure and Robust Object-Based Video Authentication System

    He, Dajun; Sun, Qibin; Tian, Qi


    An object-based video authentication system, which combines watermarking, error correction coding (ECC), and digital signature techniques, is presented for protecting the authenticity between video objects and their associated backgrounds. In this system, a set of angular radial transformation (ART) coefficients is selected as the feature to represent the video object and the background, respectively. ECC and cryptographic hashing are applied to those selected coefficients to generate the robust authentication watermark. This content-based, semifragile watermark is then embedded into the objects frame by frame before MPEG4 coding. In watermark embedding and extraction, groups of discrete Fourier transform (DFT) coefficients are randomly selected, and their energy relationships are employed to hide and extract the watermark. The experimental results demonstrate that our system is robust to MPEG4 compression, object segmentation errors, and some common object-based video processing such as object translation, rotation, and scaling while securely preventing malicious object modifications. The proposed solution can be further incorporated into public key infrastructure (PKI).

  17. Object-based indexing of MPEG-4 compressed video

    Ferman, Ahmet M.; Gunsel, Bilge; Tekalp, A. Murat


    The MPEG-4 object-based coding standard, designed as a common platform for all multimedia applications, is inherently well-suited for video indexing applications. To fully exploit the advantages offered by MPEG-4, however, a reconsideration of existing indexing strategies is required. This paper proposes a new object-based framework for video indexing and retrieval that treats as the basic indexing unit the object itself, where changes in content are detected through observations made on the objects in the video sequence. We present a temporal segmentation algorithm that is designed to automatically extract key frames for each video object in an MPEG-4 compressed sequence based on the prediction model chosen by the encoder for individual macroblocks. An extension to the existing MPEG-4 syntax is presented for conducting and facilitating vast database searches. The data presented in the proposed 'indexing field' are: the birth and death frames of individual objects, global motion characteristics/camera operations observed in the scene, representative key frames that capture the major transformations each object undergoes, and the dominant motion characteristics of each object throughout its lifetime. We present the validity of the proposed scheme by results obtained on several MPEG-4 test sequences.

  18. 3D Object Recognition Based on Linear Lie Algebra Model

    LI Fang-xing; WU Ping-dong; SUN Hua-fei; PENG Lin-yu


    A surface model called the fibre bundle model and a 3D object model based on linear Lie algebra model are proposed.Then an algorithm of 3D object recognition using the linear Lie algebra models is presented.It is a convenient recognition method for the objects which are symmetric about some axis.By using the presented algorithm,the representation matrices of the fibre or the base curve from only finite points of the linear Lie algebra model can be obtained.At last some recognition results of practicalities are given.

  19. Stereovision-Based Object Segmentation for Automotive Applications

    Fu Shan


    Full Text Available Obstacle detection and classification in a complex urban area are highly demanding, but desirable for pedestrian protection, stop & go, and enhanced parking aids. The most difficult task for the system is to segment objects from varied and complicated background. In this paper, a novel position-based object segmentation method has been proposed to solve this problem. According to the method proposed, object segmentation is performed in two steps: in depth map ( - plane and in layered images ( - planes. The stereovision technique is used to reconstruct image points and generate the depth map. Objects are detected in the depth map. Afterwards, the original edge image is separated into different layers based on the distance of detected objects. Segmentation performed in these layered images can be easier and more reliable. It has been proved that the proposed method offers robust detection of potential obstacles and accurate measurement of their location and size.

  20. An objective prior that unifies objective Bayes and information-based inference

    LaMont, Colin H


    There are three principle paradigms of statistical inference: (i) Bayesian, (ii) information-based and (iii) frequentist inference. We describe an objective prior (the weighting or $w$-prior) which unifies objective Bayes and information-based inference. The $w$-prior is chosen to make the marginal probability an unbiased estimator of the predictive performance of the model. This definition has several other natural interpretations. From the perspective of the information content of the prior, the $w$-prior is both uniformly and maximally uninformative. The $w$-prior can also be understood to result in a uniform density of distinguishable models in parameter space. Finally we demonstrate the the $w$-prior is equivalent to the Akaike Information Criterion (AIC) for regular models in the asymptotic limit. The $w$-prior appears to be generically applicable to statistical inference and is free of {\\it ad hoc} regularization. The mechanism for suppressing complexity is analogous to AIC: model complexity reduces mo...

  1. QWIP focal plane array theoretical model of 3-D imaging LADAR system

    El Mashade, Mohamed Bakry; AbouElez, Ahmed Elsayed


    The aim of this research is to develop a model for the direct detection three-dimensional (3-D) imaging LADAR system using Quantum Well Infrared Photodetector (QWIP) Focal Plane Array (FPA). This model is employed to study how to add 3-D imaging capability to the existing conventional thermal imaging systems of the same basic form which is sensitive to 3–5 mm (mid-wavelength infrared, MWIR) or 8–12 mm (long-wavelength infrared, LWIR) spectral bands. The integrated signal photoelectrons in cas...

  2. Symmetry axis based object recognition under translation, rotation and scaling.

    Hyder, Mashud; Islam, Md Monirul; Akhand, M A H; Murase, Kazuyuki


    This paper presents a new approach, known as symmetry axis based feature extraction and recognition (SAFER), for recognizing objects under translation, rotation and scaling. Unlike most previous invariant object recognition (IOR) systems, SAFER puts emphasis on both simplicity and accuracy of the recognition system. To achieve simplicity, it uses simple formulae for extracting invariant features from an object. The scheme used in feature extraction is based on the axis of symmetry and angles of concentric circles drawn around the object. SAFER divides the extracted features into a number of groups based on their similarity. To improve the recognition performance, SAFER uses a number of neural networks (NNs) instead of single NN are used for training and recognition of extracted features. The new approach, SAFER, has been tested on two of real world problems i.e., English characters with two different fonts and images of different shapes. The experimental results show that SAFER can produce good recognition performance in comparison with other algorithms.

  3. A new approach toward object-based change detection


    Object-based change detection has been the hotspot in remote sensing image processing.A new approach toward object-based change detection is proposed.The two different temporal images are unitedly segmented using the mean shift procedure to obtain corresponding objects.Then change detection is implemented based on the integration of corresponding objects’ intensity and texture differences.Experiments are conducted on both panchromatic images and multispectral images and the results show that the integrated measure is robust with respect to illumination changes and noise.Supplementary color detection is conducted to determine whether the color of the unchanged objects changes or not when dealing with multispectral images.Some verification work is carried out to show the accuracy of the proposed approach.

  4. Recognizing articulated objects using a region-based invariant transform.

    Weiss, Isaac; Ray, Manjit


    In this paper, we present a new method for representing and recognizing objects, based on invariants of the object's regions. We apply the method to articulated objects in low-resolution, noisy range images. Articulated objects such as a back-hoe can have many degrees of freedom, in addition to the unknown variables of viewpoint. Recognizing such an object in an image can involve a search in a high-dimensional space that involves all these unknown variables. Here, we use invariance to reduce this search space to a manageable size. The low resolution of our range images makes it hard to use common features such as edges to find invariants. We have thus developed a new "featureless" method that does not depend on feature detection. Instead of local features, we deal with whole regions of the object. We define a "transform" that converts the image into an invariant representation on a grid, based on invariant descriptors of entire regions centered around the grid points. We use these region-based invariants for indexing and recognition. While the focus here is on articulation, the method can be easily applied to other problems such as the occlusion of fixed objects.

  5. Human-Centered Object-Based Image Retrieval

    Broek, E.L. van den; Rikxoort, E.M. van; Schouten, T.E.


    A new object-based image retrieval (OBIR) scheme is introduced. The images are analyzed using the recently developed, human-based 11 colors quantization scheme and the color correlogram. Their output served as input for the image segmentation algorithm: agglomerative merging, which is extended to co

  6. Summarization-based image resizing by intelligent object carving.

    Dong, Weiming; Zhou, Ning; Lee, Tong-Yee; Wu, Fuzhang; Kong, Yan; Zhang, Xiaopeng


    Image resizing can be more effectively achieved with a better understanding of image semantics. In this paper, similar patterns that exist in many real-world images are analyzed. By interactively detecting similar objects in an image, the image content can be summarized rather than simply distorted or cropped. This method enables the manipulation of image pixels or patches as well as semantic objects in the scene during image resizing process. Given the special nature of similar objects in a general image, the integration of a novel object carving (OC) operator with the multi-operator framework is proposed for summarizing similar objects. The object removal sequence in the summarization strategy directly affects resizing quality. The method by which to evaluate the visual importance of the object as well as to optimally select the candidates for object carving is demonstrated. To achieve practical resizing applications for general images, a template matching-based method is developed. This method can detect similar objects even when they are of various colors, transformed in terms of perspective, or partially occluded. To validate the proposed method, comparisons with state-of-the-art resizing techniques and a user study were conducted. Convincing visual results are shown to demonstrate the effectiveness of the proposed method.

  7. A C++ Class for Rule-Base Objects

    William J. Grenney


    Full Text Available A C++ class, called Tripod, was created as a tool to assist with the development of rule-base decision support systems. The Tripod class contains data structures for the rule-base and member functions for operating on the data. The rule-base is defined by three ASCII files. These files are translated by a preprocessor into a single file that is located when a rule-base object is instantiated. The Tripod class was tested as part of a proto-type decision support system (DSS for winter highway maintenance in the Intermountain West. The DSS is composed of two principal modules: the main program, called the wrapper, and a Tripod rule-base object. The wrapper is a procedural module that interfaces with remote sensors and an external meterological database. The rule-base contains the logic for advising an inexperienced user and for assisting with the decision making process.

  8. Geophysics-based method of locating a stationary earth object

    Daily, Michael R.; Rohde, Steven B.; Novak, James L.


    A geophysics-based method for determining the position of a stationary earth object uses the periodic changes in the gravity vector of the earth caused by the sun- and moon-orbits. Because the local gravity field is highly irregular over a global scale, a model of local tidal accelerations can be compared to actual accelerometer measurements to determine the latitude and longitude of the stationary object.

  9. A New RWA Algorithm Based on Multi-Objective


    In this article, we studied the associated research problems and challenges on routing and wavelength assignment (RWA) in WDM (wavelength division multiplexing) networks. Various RWA approaches are examined and compared. We proposed a new RWA algorithm based on multi-objective. In this new algorithm, we consider multiple network optimizing objectives to setup a lightpath with maximize profit and shortest path under the limited resources. By comparing and analyzing, the proposed algorithm is much better ...

  10. An object-based methodology for knowledge representation in SGML

    Kelsey, R.L. [Los Alamos National Lab., NM (United States)|New Mexico State Univ., Las Cruces, NM (United States); Hartley, R.T. [New Mexico State Univ., Las Cruces, NM (United States); Webster, R.B. [Los Alamos National Lab., NM (United States)


    An object-based methodology for knowledge representation and its Standard Generalized Markup Language (SGML) implementation is presented. The methodology includes class, perspective domain, and event constructs for representing knowledge within an object paradigm. The perspective construct allows for representation of knowledge from multiple and varying viewpoints. The event construct allows actual use of knowledge to be represented. The SGML implementation of the methodology facilitates usability, structured, yet flexible knowledge design, and sharing and reuse of knowledge class libraries.

  11. Dynamic Object Identification with SOM-based neural networks

    Aleksey Averkin


    Full Text Available In this article a number of neural networks based on self-organizing maps, that can be successfully used for dynamic object identification, is described. Unique SOM-based modular neural networks with vector quantized associative memory and recurrent self-organizing maps as modules are presented. The structured algorithms of learning and operation of such SOM-based neural networks are described in details, also some experimental results and comparison with some other neural networks are given.

  12. A framework for Internet service evolution based on active object

    HU Hua; ZHANG Yang


    The wide use of Internet Service in distributed computing and e-business has made the evolution of Internet Service to be one of the most prevalent research fields in software development domain. Traditional methods for software development cannot adapt to the challenge of Internet Service oriented software development. In this paper, we propose a new paradigm for the evolution of Internet Service with active objects from the characteristics of Internet Service and principles of active objects. The paradigm uses an automatic monitoring mechanism of active object to detect and process evolution requirement in system based on Internet Service.

  13. A General Polygon-based Deformable Model for Object Recognition

    Jensen, Rune Fisker; Carstensen, Jens Michael


    We propose a general scheme for object localization and recognition based on a deformable model. The model combines shape and image properties by warping a arbitrary prototype intensity template according to the deformation in shape. The shape deformations are constrained by a probabilistic...... distribution, which combined with a match of the warped intensity template and the image form the final criteria used for localization and recognition of a given object. The chosen representation gives the model an ability to model an almost arbitrary object. Beside the actual model a full general scheme...


    V. S. Gorbatsevich


    Full Text Available In this paper, we propose an original method for objects detection based on a special tree-structured image representation – the trees of morphlets. The method provides robust detection of various types of objects in an image without employing a machine learning procedure. Along with a bounding box creation on a detection step, the method makes pre-segmentation, which can be further used for recognition purposes. Another important feature of the proposed approach is that there are no needs to use a running window as well as a features pyramid in order to detect the objects of different sizes.

  15. A primitive-based 3D object recognition system

    Dhawan, Atam P.


    An intermediate-level knowledge-based system for decomposing segmented data into three-dimensional primitives was developed to create an approximate three-dimensional description of the real world scene from a single two-dimensional perspective view. A knowledge-based approach was also developed for high-level primitive-based matching of three-dimensional objects. Both the intermediate-level decomposition and the high-level interpretation are based on the structural and relational matching; moreover, they are implemented in a frame-based environment.



    Construction of high resolution images from low resolution sequences is often important in surveillance applications. In this letter, an affine based multi-scale block-matching image registration algorithm is first proposed. The images to be registered are divided into overlapped blocks of different size according to its motions. The Least Square (LS) image registration algorithm is extended to match the blocks. Then an object based Super Resolution (SR) scheme is designed, the Maximum A Priori (MAP) super resolution algorithm is extended to enhance the resolution of the interest objects. Experimental results show that the proposed multi-scale registration method provides more accurate registration between frames. Further more, the object based super resolution scheme shows an enhanced performance compared with the traditional MAP method.

  17. Video Based Moving Object Tracking by Particle Filter

    Md. Zahidul Islam


    Full Text Available Usually, the video based object tracking deal with non-stationary image stream that changes over time. Robust and Real time moving object tracking is a problematic issue in computer vision research area. Most of the existing algorithms are able to track only inpredefined and well controlled environment. Some cases, they don’t consider non-linearity problem. In our paper, we develop such a system which considers color information, distance transform (DT based shape information and also nonlinearity. Particle filtering has been proven very successful for non-gaussian and non-linear estimation problems. We examine the difficulties of video based tracking and step by step we analyze these issues. In our firstapproach, we develop the color based particle filter tracker that relies on the deterministic search of window, whose color content matches a reference histogram model. A simple HSV histogram-based color model is used to develop this observation system. Secondly, wedescribe a new approach for moving object tracking with particle filter by shape information. The shape similarity between a template and estimated regions in the video scene is measured by their normalized cross-correlation of distance transformed images. Our observation system of particle filter is based on shape from distance transformed edge features. Template is created instantly by selecting any object from the video scene by a rectangle. Finally, inthis paper we illustrate how our system is improved by using both these two cues with non linearity.

  18. Object-based Storage Integration within the ATLAS DDM system

    Garonne, Vincent; The ATLAS collaboration


    In this paper, we'll talk about our experiences with different data storage technologies within the ATLAS Distributed Data Management system, and in particular about object-based storage. Object-based storage differs in many points from traditional file system storage and offers a highly scalable, simple and most common storage solution for the cloud. First, we describe the needed changes in the Rucio software to integrate this technology, then we present for which use cases we have evaluated them. Finally, we conclude by reporting the results, performances and the potential future by exploiting more of their specificities and features, like metadata support.

  19. Object-based Landslide Mapping: Examples, Challenges and Opportunities

    Hölbling, Daniel; Eisank, Clemens; Friedl, Barbara; Chang, Kang-Tsung; Tsai, Tsai-Tsung; Birkefeldt Møller Pedersen, Gro; Betts, Harley; Cigna, Francesca; Chiang, Shou-Hao; Aubrey Robson, Benjamin; Bianchini, Silvia; Füreder, Petra; Albrecht, Florian; Spiekermann, Raphael; Weinke, Elisabeth; Blaschke, Thomas; Phillips, Chris


    Over the last decade, object-based image analysis (OBIA) has been increasingly used for mapping landslides that occur after triggering events such as heavy rainfall. The increasing availability and quality of Earth Observation (EO) data in terms of temporal, spatial and spectral resolution allows for comprehensive mapping of landslides at multiple scales. Most often very high resolution (VHR) or high resolution (HR) optical satellite images are used in combination with a digital elevation model (DEM) and its products such as slope and curvature. Semi-automated object-based mapping makes use of various characteristics of image objects that are derived through segmentation. OBIA enables numerous spectral, spatial, contextual and textural image object properties to be applied during an analysis. This is especially useful when mapping complex natural features such as landslides and constitutes an advantage over pixel-based image analysis. However, several drawbacks in the process of object-based landslide mapping have not been overcome yet. The developed classification routines are often rather complex and limited regarding their transferability across areas and sensors. There is still more research needed to further improve present approaches and to fully exploit the capabilities of OBIA for landslide mapping. In this study several examples of object-based landslide mapping from various geographical regions with different characteristics are presented. Examples from the Austrian and Italian Alps are shown, whereby one challenge lies in the detection of small-scale landslides on steep slopes while preventing the classification of false positives with similar spectral properties (construction areas, utilized land, etc.). Further examples feature landslides mapped in Iceland, where the differentiation of landslides from other landscape-altering processes in a highly dynamic volcanic landscape poses a very distinct challenge, and in Norway, which is exposed to multiple

  20. The continuous Wagon Wheel Illusion is object-based.

    VanRullen, Rufin


    The occurrence of perceived reversed motion while observers view a periodic, continuously moving stimulus (the "continuous Wagon Wheel Illusion") has been taken as evidence that some aspects of motion perception rely on discrete sampling of visual information. The spatial extent of this sampling is currently under debate. When two separate motion stimuli are viewed simultaneously, the illusion of reversed motion rarely occurs for both objects together: this rules out global sampling of the visual field. The same result holds when the objects are superimposed by transparency: this argues against location-based sampling. Here we show that the sampling is in fact object-based: we use a rotating ring stimulus split in two halves. When the two halves move in opposite directions, appearing to belong to separate objects, perceptual reversals occur in either half at a time, but rarely in both. When the two halves physically move in compatible directions, they generally appear to reverse simultaneously: the illusion keeps the perceptual object united. Rather than the local low-level properties of the motion stimulus (which are comparable in both cases), it is thus the high-level organization of the scene that determines the extent of perceived motion reversals. These results imply that the continuous Wagon Wheel Illusion, and any discrete perceptual sampling that may cause it, is restricted to the object of our attention.

  1. Reactor Network Synthesis Based on Instantaneous Objective Function Characteristic Curves

    张治山; 赵文; 王艳丽; 周传光; 袁希钢


    It is believed that whether the instantaneous objective function curves of plug-flow-reactor (PFR) and continuous-stirred-tank-reactor (CSTR) overlap or not, they have a consistent changing trend for complex reactions(steady state, isothermal and constant volume). As a result of the relation of the objective functions (selectivity or yield) to the instantaneous objective functions (instantaneous selectivity or instantaneous reaction rate), the optimal reactor network configuration can be determined according to the changing trend of the instantaneous objective function curves. Further, a recent partition strategy for the reactor network synthesis based on the instantaneous objective function characteristic curves is proposed by extending the attainable region partition strategy from the concentration space to the instantaneous objective function-unreacted fraction of key reactant space. In this paper,the instantaneous objective function is closed to be the instantaneous selectivity and several samples axe examined to illustrate the proposed method. The comparison with the previous work indicates it is a very convenient and practical systematic tool of the reactor network synthesis and seems also promising for overcoming the dimension limit of the attainable region partition strategy in the concentration space.

  2. Concurrent Object-Oriented Programming Based on MPI

    鲁宏伟; 汪厚祥; 裴晓黎; 肖永玲


    Object-oriented model possesses inherent concurrency. Integration of concurrency and object-orientation is a promising new field. MPI is a message-passing standard and has been adopted by more and more systems. This paper proposes a novel approach to realize concurrent object-oriented programming based on Message-passing interface(MPI) in which future method communication is adopted between concurrent objects. A state behavior set is proposed to solve inheritance anomaly, and a bounded buffer is taken as an example to illustrate this proposal. The definition of ParaMPI class, which is the most important class in the concurrent class library,and implementation issues are briefly described.

  3. Ground-based optical observation system for LEO objects

    Yanagisawa, T.; Kurosaki, H.; Oda, H.; Tagawa, M.


    We propose a ground-based optical observation system for monitoring LEO objects, which uses numerous optical sensors to cover a vast region of the sky. Its potential in terms of detection and orbital determination were examined. About 30 cm LEO objects at 1000 km altitude are detectable using an 18 cm telescope, a CCD camera and the analysis software developed. Simulations and a test observation showed that two longitudinally separate observation sites with arrays of optical sensors can identify the same objects from numerous data sets and determine their orbits precisely. The proposed system may complement or replace the current radar observation system for monitoring LEO objects, like space-situation awareness, in the near future.

  4. Research on Virtual Object Tele-operation Based on Gesture


    A tele-operation method of virtual environment based on gesture is presented.Firstly,the design block diagram and the information flow of the virtual environment tele-operation simulation system are given.Secondly,the coordination transformation between virtual gesture and the tele-operated aircraft is presented.Finally,a tele-operation simulation system based on gesture is developed.And the simulation results demonstrate that there is more consistency between the virtual gesture and the moving object.

  5. Geographic Object-Based Image Analysis: Towards a new paradigm

    Blaschke, T.; Hay, G.J.; Kelly, M.; Lang, S.; Hofmann, P.; Addink, E.A.; Queiroz Feitosa, R.; van der Meer, F.D.; van der Werff, H.M.A.; van Coillie, F.; Tiede, A.


    The amount of scientific literature on (Geographic) Object-based Image Analysis – GEOBIA has been and still is sharply increasing. These approaches to analysing imagery have antecedents in earlier research on image segmentation and use GIS-like spatial analysis within classification and feature extr

  6. Archive Design Based on Planets Inspired Logical Object Model

    Zierau, Eld; Johansen, Anders


    We describe a proposal for a logical data model based on preliminary work the Planets project In OAIS terms the main areas discussed are related to the introduction of a logical data model for representing the past, present and future versions of the digital object associated with the Archival...

  7. Object based data access at the D0 experiment

    Fuess, S.; D0 Collaboration


    The D{O} Experiment at Fermilab is currently participating in the FNAL Computing Division`s ``Computing for Analysis Project`` (CAP) to investigate object based data storage and access. Following a short description of the CAP system architecture, the D{O} data model is explored. A brief discussion of the method of operation of the CAP system leads into a concluding section.

  8. Object-oriented vision for a behavior-based robot

    Bischoff, Rainer; Graefe, Volker; Wershofen, Klaus P.


    As one realization out of the class of behavior-based robot architectures a specific concept of situation-oriented behavior-based navigation has been proposed. Its main characteristic is that the selection of the behaviors to be executed in each moment is based on a continuous recognition and evaluation of the dynamically changing situation in which the robot is finding itself. An important prerequisite for such as approach is a timely and comprehensive perception of the robot's dynamically changing environment. Object-oriented vision as proposed and successfully applied, e.g., in freeway traffic scenes is a particularly well suited sensing modality for robot control. Our work concentrated on modeling the physical objects which are relevant for indoor navigation, i.e. walls, intersections of corridors, and landmarks. In the interest of efficiency these models include only those necessary features for allowing the robot to reliably recognize different situations in real time. According to the concept of object- oriented vision recognizing such objects is largely reduced to a knowledge-based verification of objects or features that may be expected to be visible in the current situation. The following results have been achieved: 1) By using its vision system and a knowledge base in the form of an attributed topological map the robot could orient itself and navigate autonomously in a known environment. 2) In an unknown environment the robot was able to build, by means of supervised learning, an attributed topological map as a basis for subsequent autonomous navigation. 3) The experiments could be performed both under unmodified artificial light and under natural light shining through the glass walls of the building.

  9. Fuzzy Distance-Based Range Queries over Uncertain Moving Objects

    Yi-Fei Chen; Xiao-Lin Qin; Liang Liu; Bo-Han Li


    Data obtained from real world are imprecise or uncertain due to the accuracy of positioning devices,updating protocols or characteristics of applications.On the other hand,users sometimes prefer to qualitatively express their requests with vague conditions and different parts of search region are in-equally important in some applications.We address the problem of efficiently processing the fuzzy range queries for uncertain moving objects whose whereabouts in time are not known exactly,for which the basic syntax is find objects always/sometimes near to the query issuer with the qualifying guarantees no less than a given threshold during a given temporal interval.We model the location uncertainty of moving objects on the utilization of probability density functions and describe the indeterminate boundary of query range with fuzzy set.We present the qualifying guarantee evaluation of objects,and propose pruning techniques based on the α-cut of fuzzy set to shrink the search space efficiently.We also design rules to reject non-qualifying objects and validate qualifying objects in order to avoid unnecessary costly numeric integrations in the refinement step.An extensive empirical study has been conducted to demonstrate the efficiency and effectiveness of algorithms under various experimental settings.

  10. Object-based high contrast travel time tomography

    Lin, Yenting


    We consider travel time tomography problems involving detection of high contrast, discrete high velocity structures. This results in a discrete nonlinear inverse problem, for which traditional grid-based models and iterative linearized least-squares reconstruction algorithms are not suitable. This is because travel paths change significantly near the high contrast velocity structure, making it more difficult to inversely calculate the travel path and infer the velocity along the path. We propose a model-based approach to describe the high velocity structure using pre-defined elementary objects. Compared to a grid-based model, our approach has complexity that increases as a function of the number of objects, rather than increasing with the number of cells (usually very large). A new reconstruction algorithm is developed that provides estimates of the probability that a high velocity structure appears at any point in the region of interest. Simulation results show that our method can efficiently sample the mode...

  11. Object-Based Classification and Change Detection of Hokkaido, Japan

    Park, J. G.; Harada, I.; Kwak, Y.


    Topography and geology are factors to characterize the distribution of natural vegetation. Topographic contour is particularly influential on the living conditions of plants such as soil moisture, sunlight, and windiness. Vegetation associations having similar characteristics are present in locations having similar topographic conditions unless natural disturbances such as landslides and forest fires or artificial disturbances such as deforestation and man-made plantation bring about changes in such conditions. We developed a vegetation map of Japan using an object-based segmentation approach with topographic information (elevation, slope, slope direction) that is closely related to the distribution of vegetation. The results found that the object-based classification is more effective to produce a vegetation map than the pixel-based classification.

  12. Reconstructed key frame and object motion based video retrieval

    Hu, Shuangyan; Li, Junshan; Li, Kun; Wang, Rui; Yang, Weijun


    This paper proposes a video retrieval scheme which can retrieve desired video clips from video databases using color and object motion. The retrieval method includes two steps. In the first step, get the Intra picture frames (I-frames) set from the query MPEG video and reconstruct the key frame of the video based on the set. Then, the video retrieval equals to the retrieval of the reconstructed key frame(R-key frame) and can be easily performed according the methods of content based image retrieval. The second step, the local object motion information that is local motion vector field, is extracted from the video clips set which is the result of the first step, and the final similarity of videos is measured based on the constructed directional histogram. Experimental results show that the proposed two-step retrieval method performed excellently for video retrieval.

  13. Multi-objective based spectral unmixing for hyperspectral images

    Xu, Xia; Shi, Zhenwei


    Sparse hyperspectral unmixing assumes that each observed pixel can be expressed by a linear combination of several pure spectra in a priori library. Sparse unmixing is challenging, since it is usually transformed to a NP-hard l0 norm based optimization problem. Existing methods usually utilize a relaxation to the original l0 norm. However, the relaxation may bring in sensitive weighted parameters and additional calculation error. In this paper, we propose a novel multi-objective based algorithm to solve the sparse unmixing problem without any relaxation. We transform sparse unmixing to a multi-objective optimization problem, which contains two correlative objectives: minimizing the reconstruction error and controlling the endmember sparsity. To improve the efficiency of multi-objective optimization, a population-based randomly flipping strategy is designed. Moreover, we theoretically prove that the proposed method is able to recover a guaranteed approximate solution from the spectral library within limited iterations. The proposed method can directly deal with l0 norm via binary coding for the spectral signatures in the library. Experiments on both synthetic and real hyperspectral datasets demonstrate the effectiveness of the proposed method.

  14. Robust B+ -Tree-Based Indexing of Moving Objects

    Jensen, Christian Søndergaard; Tiesyte, Dalia; Tradisauskas, Nerius


    With the emergence of an infrastructure that enables the geo-positioning of on-line, mobile users, the management of so-called moving objects has emerged as an active area of research. Among the indexing techniques for efficiently answering predictive queries on moving-object positions, the recent...... that is needed to guarantee perfect recall, thus significantly improving robustness. The new technique is empirically evaluated and compared with four other approaches and with the TPR-tree, a competitor that is based on the R*-tree. The results indicate that the new index is indeed more robust than its...

  15. Agent-based Algorithm for Spatial Distribution of Objects

    Collier, Nathan


    In this paper we present an agent-based algorithm for the spatial distribution of objects. The algorithm is a generalization of the bubble mesh algorithm, initially created for the point insertion stage of the meshing process of the finite element method. The bubble mesh algorithm treats objects in space as bubbles, which repel and attract each other. The dynamics of each bubble are approximated by solving a series of ordinary differential equations. We present numerical results for a meshing application as well as a graph visualization application.

  16. Faint spatial object classifier construction based on data mining technology

    Lou, Xin; Zhao, Yang; Liao, Yurong; Nie, Yong-ming


    Data mining can effectively obtain the faint spatial object's patterns and characteristics, the universal relations and other implicated data characteristics, the key of which is classifier construction. Faint spatial object classifier construction with spatial data mining technology for faint spatial target detection is proposed based on theoretical analysis of design procedures and guidelines in detail. For the one-sidedness weakness during dealing with the fuzziness and randomness using this method, cloud modal classifier is proposed. Simulating analyzing results indicate that this method can realize classification quickly through feature combination and effectively resolve the one-sidedness weakness problem.



    A method of fuzzy identification based on a new objective function is proposed. The method could deal with the issue that input variables of a system have an effect on the input space while output variables of the system do not exert an influence on the input space in the proposed objective functions of fuzzy clustering. The method could simultaneously solve the problems about structure identification and parameter estimation; thus it makes the fuzzy model become optimal. Simulation example demonstrates that the method could identify non-linear systems and obviously improve modeling accuracy.

  18. Rules-based object-relational databases ontology construction

    Chen Jia; Wu Yue


    To solve the problems of sharing and reusing information in the information system, a rules-based ontology constructing approach from object-relational databases is proposed. A 3-tuple ontology constructing model is proposed first. Then, four types of ontology constructing rules including class, property, property characteristics, and property restrictions axe formalized affording to the model. Experiment results described in Web ontology language prove that our proposed approach is feasible for applying in the semantic objects project of semantic computing laboratory in UC Irvine. Our approach reduces about twenty percent constructing time compared with the ontology construction from relational databases.

  19. Performance Evaluation of Java Based Object Relational Mapping Tools

    Shoaib Mahmood Bhatti


    Full Text Available Object persistency is the hot issue in the form of ORM (Object Relational Mapping tools in industry as developers use these tools during software development. This paper presents the performance evaluation of Java based ORM tools. For this purpose, Hibernate, Ebean and TopLinkhave been selected as the ORM tools which are popular and open source. Their performance has been measured from execution point of view. The results show that ORM tools are the good option for the developers considering the system throughput in shorter setbacks and they can be used efficiently and effectively for performing mapping of the objects into the relational dominated world of database, thus creating a hope for a better and well dominated future of this technology.

  20. Generative Learning Objects Instantiated with Random Numbers Based Expressions

    Ciprian Bogdan Chirila


    Full Text Available The development of interactive e-learning content requires special skills like programming techniques, web integration, graphic design etc. Generally, online educators do not possess such skills and their e-learning products tend to be static like presentation slides and textbooks. In this paper we propose a new interactive model of generative learning objects as a compromise betweenstatic, dull materials and dynamic, complex software e-learning materials developed by specialized teams. We find that random numbers based automatic initialization learning objects increases content diversity, interactivity thus enabling learners’ engagement. The resulted learning object model is at a limited level of complexity related to special e-learning software, intuitive and capable of increasing learners’ interactivity, engagement and motivation through dynamic content. The approach was applied successfully on several computer programing disciplines.

  1. Features Extraction for Object Detection Based on Interest Point

    Amin Mohamed Ahsan


    Full Text Available In computer vision, object detection is an essential process for further processes such as object tracking, analyzing and so on. In the same context, extraction features play important role to detect the object correctly. In this paper we present a method to extract local features based on interest point which is used to detect key-points within an image, then, compute histogram of gradient (HOG for the region surround that point. Proposed method used speed-up robust feature (SURF method as interest point detector and exclude the descriptor. The new descriptor is computed by using HOG method. The proposed method got advantages of both mentioned methods. To evaluate the proposed method, we used well-known dataset which is Caltech101. The initial result is encouraging in spite of using a small data for training.

  2. Nanoscale synthesis and characterization of graphene-based objects

    Daisuke Fujita


    Full Text Available Graphene-based nano-objects such as nanotrenches, nanowires, nanobelts and nanoscale superstructures have been grown by surface segregation and precipitation on carbon-doped mono- and polycrystalline nickel substrates in ultrahigh vacuum. The dominant morphologies of the nano-objects were nanowire and nanosheet. Nucleation of graphene sheets occurred at surface defects such as step edges and resulted in the directional growth of nanowires. Surface analysis by scanning tunneling microscopy (STM has clarified the structure and functionality of the novel nano-objects at atomic resolution. Nanobelts were detected consisting of bilayer graphene sheets with a nanoscale width and a length of several microns. Moiré patterns and one-dimensional reconstruction were observed on multilayer graphite terraces. As a useful functionality, application to repairable high-resolution STM probes is demonstrated.

  3. Object tracking algorithm based on contextual visual saliency

    Fu, Bao; Peng, XianRong


    As to object tracking, the local context surrounding of the target could provide much effective information for getting a robust tracker. The spatial-temporal context (STC) learning algorithm proposed recently considers the information of the dense context around the target and has achieved a better performance. However STC only used image intensity as the object appearance model. But this appearance model not enough to deal with complicated tracking scenarios. In this paper, we propose a novel object appearance model learning algorithm. Our approach formulates the spatial-temporal relationships between the object of interest and its local context based on a Bayesian framework, which models the statistical correlation between high-level features (Circular-Multi-Block Local Binary Pattern) from the target and its surrounding regions. The tracking problem is posed by computing a visual saliency map, and obtaining the best target location by maximizing an object location likelihood function. Extensive experimental results on public benchmark databases show that our algorithm outperforms the original STC algorithm and other state-of-the-art tracking algorithms.

  4. Likelihood-based CT reconstruction of objects containing known components

    Stayman, J. Webster [Johns Hopkins Univ., Baltimore, MD (United States). Dept. of Biomedical Engineering; Otake, Yoshito; Uneri, Ali; Prince, Jerry L.; Siewerdsen, Jeffrey H.


    There are many situations in medical imaging where there are known components within the imaging volume. Such is the case in diagnostic X-ray CT imaging of patients with implants, in intraoperative CT imaging where there may be surgical tools in the field, or in situations where the patient support (table or frame) or other devices are outside the (truncated) reconstruction FOV. In such scenarios it is often of great interest to image the relation between the known component and the surrounding anatomy, or to provide high-quality images at the boundary of these objects, or simply to minimize artifacts arising from such components. We propose a framework for simultaneously estimating the position and orientation of a known component and the surrounding volume. Toward this end, we adopt a likelihood-based objective function with an image volume jointly parameterized by a known object, or objects, with unknown registration parameters and an unknown background attenuation volume. The objective is solved iteratively using an alternating minimization approach between the two parameter types. Because this model integrates a substantial amount of prior knowledge about the overall volume, we expect a number of advantages including the reduction of metal artifacts, potential for more sparse data acquisition (decreased time and dose), and/or improved image quality. We illustrate this approach using simulated spine CT data that contains pedicle screws placed in a vertebra, and demonstrate improved performance over traditional filtered-backprojection and penalized-likelihood reconstruction techniques. (orig.)

  5. Knowledge-based simulation using object-oriented programming

    Sidoran, Karen M.


    Simulations have become a powerful mechanism for understanding and modeling complex phenomena. Their results have had substantial impact on a broad range of decisions in the military, government, and industry. Because of this, new techniques are continually being explored and developed to make them even more useful, understandable, extendable, and efficient. One such area of research is the application of the knowledge-based methods of artificial intelligence (AI) to the computer simulation field. The goal of knowledge-based simulation is to facilitate building simulations of greatly increased power and comprehensibility by making use of deeper knowledge about the behavior of the simulated world. One technique for representing and manipulating knowledge that has been enhanced by the AI community is object-oriented programming. Using this technique, the entities of a discrete-event simulation can be viewed as objects in an object-oriented formulation. Knowledge can be factual (i.e., attributes of an entity) or behavioral (i.e., how the entity is to behave in certain circumstances). Rome Laboratory's Advanced Simulation Environment (RASE) was developed as a research vehicle to provide an enhanced simulation development environment for building more intelligent, interactive, flexible, and realistic simulations. This capability will support current and future battle management research and provide a test of the object-oriented paradigm for use in large scale military applications.

  6. A Learning Object Approach To Evidence based learning

    Zabin Visram


    Full Text Available This paper describes the philosophy, development and framework of the body of elements formulated to provide an approach to evidence-based learning sustained by Learning Objects and web based technology Due to the demands for continuous improvement in the delivery of healthcare and in the continuous endeavour to improve the quality of life, there is a continuous need for practitioner's to update their knowledge by accomplishing accredited courses. The rapid advances in medical science has meant increasingly, there is a desperate need to adopt wireless schemes, whereby bespoke courses can be developed to help practitioners keep up with expanding knowledge base. Evidently, without current best evidence, practice risks becoming rapidly out of date, to the detriment of the patient. There is a need to provide a tactical, operational and effective environment, which allows professional to update their education, and complete specialised training, just-in-time, in their own time and location. Following this demand in the marketplace the information engineering group, in combination with several medical and dental schools, set out to develop and design a conceptual framework which form the basis of pioneering research, which at last, enables practitioner's to adopt a philosophy of life long learning. The body and structure of this framework is subsumed under the term Object oriented approach to Evidence Based learning, Just-in-time, via Internet sustained by Reusable Learning Objects (The OEBJIRLO Progression. The technical pillars which permit this concept of life long learning are pivoted by the foundations of object oriented technology, Learning objects, Just-in-time education, Data Mining, intelligent Agent technology, Flash interconnectivity and remote wireless technology, which allow practitioners to update their professional skills, complete specialised training which leads to accredited qualifications. This paper sets out to develop and

  7. Data Warehouse Requirements Analysis Framework: Business-Object Based Approach

    Anirban Sarkar


    Full Text Available Detailed requirements analysis plays a key role towards the design of successful Data Warehouse (DW system. The requirements analysis specifications are used as the prime input for the construction of conceptual level multidimensional data model. This paper has proposed a Business Object based requirements analysis framework for DW system which is supported with abstraction mechanism and reuse capability. It also facilitate the stepwise mapping of requirements descriptions into high level design components of graph semantic based conceptual level object oriented multidimensional data model. The proposed framework starts with the identification of the analytical requirements using business process driven approach and finally refine the requirements in further detail to map into the conceptual level DW design model using either Demand-driven of Mixed-driven approach for DW requirements analysi

  8. Moving object detection in aerial video based on spatiotemporal saliency

    Shen Hao; Li Shuxiao; Zhu Chengfei; Chang Hongxing; Zhang Jinglan


    In this paper, the problem of moving object detection in aerial video is addressed. While motion cues have been extensively exploited in the literature, how to use spatial information is still an open problem. To deal with this issue, we propose a novel hierarchical moving target detection method based on spatiotemporal saliency. Temporal saliency is used to get a coarse segmentation, and spatial saliency is extracted to obtain the object’s appearance details in candidate motion regions. Finally, by combining temporal and spatial saliency information, we can get refined detec-tion results. Additionally, in order to give a full description of the object distribution, spatial sal-iency is detected in both pixel and region levels based on local contrast. Experiments conducted on the VIVID dataset show that the proposed method is efficient and accurate.

  9. The OASE project: Object-based Analysis and Seamless prediction

    Troemel, Silke; Wapler, Kathrin; Bick, Theresa; Diederich, Malte; Deneke, Hartwig; Horvath, Akos; Senf, Fabian; Simmer, Clemens; Simon, Juergen


    The research group on Object-based Analysis and SEamless prediction (OASE) is part of the Hans Ertel Centre for Weather Research (HErZ). The group consists of scientists at the Meteorological Institute, University of Bonn, the Leibniz-Institute for Tropospheric Research in Leipzig and the German Weather Service. OASE addresses seamless prediction of convective events from nowcasting to daily predictions by combining radar/satellite compositing and tracking with high-resolution model-based ensemble generation and prediction. While observation-based nowcasting provides good results for lead times between 0-1 hours, numerical weather prediction addresses lead times between 3-21 hours. Especially the discontinuity between 1-3 hours needs to be addressed. Therefore a central goal of the project is a near real-time high-resolved unprecedented data base. A radar and satellite remote sensing-driven 3D observation-microphysics composite covering Germany, currently under development, contains gridded observations and estimated microphysical quantities. Observations and microphysics are intertwined via forward operators and estimated inverse relations, which also provide uncertainties for model ensemble initialisations. The lifetime evolution of dynamics and microphysics in (severe) convective storms is analysed based on 3D scale-space tracking. An object-based analysis condenses the information contained in the dynamic 3D distributions of observables and related microphysics into descriptors, which will allow identifying governing processes leading to the formation and evolution of severe weather events. The object-based approach efficiently characterises and quantifies the process structure and life cycles of severe weather events, and facilitates nowcasting and the generation and initialisation of model prediction ensembles. Observation-based nowcasting will exploit the dual-composite based 3D feature detection and tracking to generate a set of predictions (observation-based

  10. Logical Object as a Basis of Knowledge Based Systems

    徐殿祥; 郑国梁


    This paper presents a framework called logical knowledge object (LKO),which is taken as a basis of the dependable development of knowledge based systems(KBSs).LKO combines logic programming and object-oriented programming paradigms,where objects are viewed as abstractions with states,constraints,behaviors and inheritance.The operational semantics defined in the style of natural semantics is simple and clear.A hybrid knowledge representation amalgamating rule,frame,semantic network and blackboard is available for both most structured and flat knowledge.The management of knowledge bases has been formally specified.Accordingly,LKO is well suited for the formal representation of knowledge and requirements of KBSs.Based on the framework,verification techniques are also explored to enhance the analysis of requirement specifications and the validation of KBSs.In addition,LKO provides a methodology for the development of KBSs,applying the concepts of rapid prototyping and top-down design to deal with changing and incomplete requirements,and to provide multiple abstract models of the domain,where formal methods might be used at each abstract level.

  11. Geographic Object-Based Image Analysis - Towards a new paradigm.

    Blaschke, Thomas; Hay, Geoffrey J; Kelly, Maggi; Lang, Stefan; Hofmann, Peter; Addink, Elisabeth; Queiroz Feitosa, Raul; van der Meer, Freek; van der Werff, Harald; van Coillie, Frieke; Tiede, Dirk


    The amount of scientific literature on (Geographic) Object-based Image Analysis - GEOBIA has been and still is sharply increasing. These approaches to analysing imagery have antecedents in earlier research on image segmentation and use GIS-like spatial analysis within classification and feature extraction approaches. This article investigates these development and its implications and asks whether or not this is a new paradigm in remote sensing and Geographic Information Science (GIScience). We first discuss several limitations of prevailing per-pixel methods when applied to high resolution images. Then we explore the paradigm concept developed by Kuhn (1962) and discuss whether GEOBIA can be regarded as a paradigm according to this definition. We crystallize core concepts of GEOBIA, including the role of objects, of ontologies and the multiplicity of scales and we discuss how these conceptual developments support important methods in remote sensing such as change detection and accuracy assessment. The ramifications of the different theoretical foundations between the 'per-pixel paradigm' and GEOBIA are analysed, as are some of the challenges along this path from pixels, to objects, to geo-intelligence. Based on several paradigm indications as defined by Kuhn and based on an analysis of peer-reviewed scientific literature we conclude that GEOBIA is a new and evolving paradigm.

  12. Multi-objective community detection based on memetic algorithm.

    Peng Wu

    Full Text Available Community detection has drawn a lot of attention as it can provide invaluable help in understanding the function and visualizing the structure of networks. Since single objective optimization methods have intrinsic drawbacks to identifying multiple significant community structures, some methods formulate the community detection as multi-objective problems and adopt population-based evolutionary algorithms to obtain multiple community structures. Evolutionary algorithms have strong global search ability, but have difficulty in locating local optima efficiently. In this study, in order to identify multiple significant community structures more effectively, a multi-objective memetic algorithm for community detection is proposed by combining multi-objective evolutionary algorithm with a local search procedure. The local search procedure is designed by addressing three issues. Firstly, nondominated solutions generated by evolutionary operations and solutions in dominant population are set as initial individuals for local search procedure. Then, a new direction vector named as pseudonormal vector is proposed to integrate two objective functions together to form a fitness function. Finally, a network specific local search strategy based on label propagation rule is expanded to search the local optimal solutions efficiently. The extensive experiments on both artificial and real-world networks evaluate the proposed method from three aspects. Firstly, experiments on influence of local search procedure demonstrate that the local search procedure can speed up the convergence to better partitions and make the algorithm more stable. Secondly, comparisons with a set of classic community detection methods illustrate the proposed method can find single partitions effectively. Finally, the method is applied to identify hierarchical structures of networks which are beneficial for analyzing networks in multi-resolution levels.

  13. Corner Sort for Pareto-Based Many-Objective Optimization.

    Wang, Handing; Yao, Xin


    Nondominated sorting plays an important role in Pareto-based multiobjective evolutionary algorithms (MOEAs). When faced with many-objective optimization problems multiobjective optimization problems (MOPs) with more than three objectives, the number of comparisons needed in nondominated sorting becomes very large. In view of this, a new corner sort is proposed in this paper. Corner sort first adopts a fast and simple method to obtain a nondominated solution from the corner solutions, and then uses the nondominated solution to ignore the solutions dominated by it to save comparisons. Obtaining the nondominated solutions requires much fewer objective comparisons in corner sort. In order to evaluate its performance, several state-of-the-art nondominated sorts are compared with our corner sort on three kinds of artificial solution sets of MOPs and the solution sets generated from MOEAs on benchmark problems. On one hand, the experiments on artificial solution sets show the performance on the solution sets with different distributions. On the other hand, the experiments on the solution sets generated from MOEAs show the influence that different sorts bring to MOEAs. The results show that corner sort performs well, especially on many-objective optimization problems. Corner sort uses fewer comparisons than others.

  14. Filling-Based Techniques Applied to Object Projection Feature Estimation

    Quesada, Luis


    3D motion tracking is a critical task in many computer vision applications. Unsupervised markerless 3D motion tracking systems determine the most relevant object in the screen and then track it by continuously estimating its projection features (center and area) from the edge image and a point inside the relevant object projection (namely, inner point), until the tracking fails. Existing object projection feature estimation techniques are based on ray-casting from the inner point. These techniques present three main drawbacks: when the inner point is surrounded by edges, rays may not reach other relevant areas; as a consequence of that issue, the estimated features may greatly vary depending on the position of the inner point relative to the object projection; and finally, increasing the number of rays being casted and the ray-casting iterations (which would make the results more accurate and stable) increases the processing time to the point the tracking cannot be performed on the fly. In this paper, we anal...

  15. Aerial video and ladar imagery fusion for persistent urban vehicle tracking

    Cho, Peter; Greisokh, Daniel; Anderson, Hyrum; Sandland, Jessica; Knowlton, Robert


    We assess the impact of supplementing two-dimensional video with three-dimensional geometry for persistent vehicle tracking in complex urban environments. Using recent video data collected over a city with minimal terrain content, we first quantify erroneous sources of automated tracking termination and identify those which could be ameliorated by detailed height maps. They include imagery misregistration, roadway occlusion and vehicle deceleration. We next develop mathematical models to analyze the tracking value of spatial geometry knowledge in general and high resolution ladar imagery in particular. Simulation results demonstrate how 3D information could eliminate large numbers of false tracks passing through impenetrable structures. Spurious track rejection would permit Kalman filter coasting times to be significantly increased. Track lifetimes for vehicles occluded by trees and buildings as well as for cars slowing down at corners and intersections could consequently be prolonged. We find high resolution 3D imagery can ideally yield an 83% reduction in the rate of automated tracking failure.


    H. Ding


    Full Text Available Landform classification is a necessary task for various fields of landscape and regional planning, for example for landscape evaluation, erosion studies, hazard prediction, et al. This study proposes an improved object-based classification for Chinese landform types using the factor importance analysis of random forest and the gray-level co-occurrence matrix (GLCM. In this research, based on 1km DEM of China, the combination of the terrain factors extracted from DEM are selected by correlation analysis and Sheffield's entropy method. Random forest classification tree is applied to evaluate the importance of the terrain factors, which are used as multi-scale segmentation thresholds. Then the GLCM is conducted for the knowledge base of classification. The classification result was checked by using the 1:4,000,000 Chinese Geomorphological Map as reference. And the overall classification accuracy of the proposed method is 5.7% higher than ISODATA unsupervised classification, and 15.7% higher than the traditional object-based classification method.

  17. An Object-Based Method for Chinese Landform Types Classification

    Ding, Hu; Tao, Fei; Zhao, Wufan; Na, Jiaming; Tang, Guo'an


    Landform classification is a necessary task for various fields of landscape and regional planning, for example for landscape evaluation, erosion studies, hazard prediction, et al. This study proposes an improved object-based classification for Chinese landform types using the factor importance analysis of random forest and the gray-level co-occurrence matrix (GLCM). In this research, based on 1km DEM of China, the combination of the terrain factors extracted from DEM are selected by correlation analysis and Sheffield's entropy method. Random forest classification tree is applied to evaluate the importance of the terrain factors, which are used as multi-scale segmentation thresholds. Then the GLCM is conducted for the knowledge base of classification. The classification result was checked by using the 1:4,000,000 Chinese Geomorphological Map as reference. And the overall classification accuracy of the proposed method is 5.7% higher than ISODATA unsupervised classification, and 15.7% higher than the traditional object-based classification method.

  18. Code Based Analysis for Object-Oriented Systems

    Swapan Bhattacharya; Ananya Kanjilal


    The basic features of object-oriented software makes it difficult to apply traditional testing methods in objectoriented systems. Control Flow Graph (CFG) is a well-known model used for identification of independent paths in procedural software. This paper highlights the problem of constructing CFG in object-oriented systems and proposes a new model named Extended Control Flow Graph (ECFG) for code based analysis of Object-Oriented (OO) software. ECFG is a layered CFG where nodes refer to methods rather than statements. A new metrics - Extended Cyclomatic Complexity (E-CC) is developed which is analogous to McCabe's Cyclomatic Complexity (CC) and refers to the number of independent execution paths within the OO software. The different ways in which CFG's of individual methods are connected in an ECFG are presented and formulas for E-CC for these different cases are proposed. Finally we have considered an example in Java and based on its ECFG, applied these cases to arrive at the E-CC of the total system as well as proposed a methodology for calculating the basis set, i.e., the set of independent paths for the OO system that will help in creation of test cases for code testing.

  19. Formal Photograph Compression Algorithm Based on Object Segmentation

    Li Zhu; Guo-You Wang; Chen Wang


    Small storage space for photographs in formal documents is increasingly necessary in today's needs for huge amounts of data communication and storage. Traditional compression algorithms do not sufficiently utilize the distinctness of formal photographs. That is, the object is an image of the human head, and the background is in unicolor. Therefore, the compression is of low efficiency and the image after compression is still space-consuming. This paper presents an image compression algorithm based on object segmentation for practical high-efficiency applications. To achieve high coding efficiency, shape-adaptive discrete wavelet transforms are used to transformation arbitrarily shaped objects. The areas of the human head and its background are compressed separately to reduce the coding redundancy of the background. Two methods, lossless image contour coding based on differential chain, and modified set partitioning in hierarchical trees (SPIHT) algorithm of arbitrary shape, are discussed in detail. The results of experiments show that when bit per pixel (bpp)is equal to 0.078, peak signal-to-noise ratio (PSNR) of reconstructed photograph will exceed the standard of SPIHT by nearly 4dB.

  20. Online Discovery of Search Objectives for Test-Based Problems.

    Liskowski, Paweł; Krawiec, Krzysztof


    In test-based problems, commonly approached with competitive coevolutionary algorithms, the fitness of a candidate solution is determined by the outcomes of its interactions with multiple tests. Usually, fitness is a scalar aggregate of interaction outcomes, and as such imposes a complete order on the candidate solutions. However, passing different tests may require unrelated "skills," and candidate solutions may vary with respect to such capabilities. In this study, we provide theoretical evidence that scalar fitness, inherently incapable of capturing such differences, is likely to lead to premature convergence. To mitigate this problem, we propose DISCO, a method that automatically identifies the groups of tests for which the candidate solutions behave similarly and define the above skills. Each such group gives rise to a derived objective, and these objectives together guide the search algorithm in multi-objective fashion. When applied to several well-known test-based problems, the proposed approach significantly outperforms the conventional two-population coevolution. This opens the door to efficient and generic countermeasures to premature convergence for both coevolutionary and evolutionary algorithms applied to problems featuring aggregating fitness functions.

  1. Object Recognition using Feature- and Color-Based Methods

    Duong, Tuan; Duong, Vu; Stubberud, Allen


    An improved adaptive method of processing image data in an artificial neural network has been developed to enable automated, real-time recognition of possibly moving objects under changing (including suddenly changing) conditions of illumination and perspective. The method involves a combination of two prior object-recognition methods one based on adaptive detection of shape features and one based on adaptive color segmentation to enable recognition in situations in which either prior method by itself may be inadequate. The chosen prior feature-based method is known as adaptive principal-component analysis (APCA); the chosen prior color-based method is known as adaptive color segmentation (ACOSE). These methods are made to interact with each other in a closed-loop system to obtain an optimal solution of the object-recognition problem in a dynamic environment. One of the results of the interaction is to increase, beyond what would otherwise be possible, the accuracy of the determination of a region of interest (containing an object that one seeks to recognize) within an image. Another result is to provide a minimized adaptive step that can be used to update the results obtained by the two component methods when changes of color and apparent shape occur. The net effect is to enable the neural network to update its recognition output and improve its recognition capability via an adaptive learning sequence. In principle, the improved method could readily be implemented in integrated circuitry to make a compact, low-power, real-time object-recognition system. It has been proposed to demonstrate the feasibility of such a system by integrating a 256-by-256 active-pixel sensor with APCA, ACOSE, and neural processing circuitry on a single chip. It has been estimated that such a system on a chip would have a volume no larger than a few cubic centimeters, could operate at a rate as high as 1,000 frames per second, and would consume in the order of milliwatts of power.

  2. R2-Based Multi/Many-Objective Particle Swarm Optimization

    Toscano, Gregorio; Barron-Zambrano, Jose Hugo; Tello-Leal, Edgar


    We propose to couple the R2 performance measure and Particle Swarm Optimization in order to handle multi/many-objective problems. Our proposal shows that through a well-designed interaction process we could maintain the metaheuristic almost inalterable and through the R2 performance measure we did not use neither an external archive nor Pareto dominance to guide the search. The proposed approach is validated using several test problems and performance measures commonly adopted in the specialized literature. Results indicate that the proposed algorithm produces results that are competitive with respect to those obtained by four well-known MOEAs. Additionally, we validate our proposal in many-objective optimization problems. In these problems, our approach showed its main strength, since it could outperform another well-known indicator-based MOEA. PMID:27656200

  3. A Quaternionic Wavelet Transform-based Approach for Object Recognition

    R. Ahila Priyadharshini


    Full Text Available Recognizing the objects in complex natural scenes is the challenging task as the object may be occluded, may vary in shape, position and in size. In this paper a method to recognize objects from different categories of images using quaternionic wavelet transform (QWT is presented. This transform separates the information contained in the image better than a traditional Discrete wavelet transform and provides a multiscale image analysis whose coefficients are 2D analytic, with one near-shift invariant magnitude and three phases. The two phases encode local image shifts and the third one contains texture information. In the domain of object recognition, it is often to classify objects from images that make only limited part of the image. Hence to identify local features and certain region of images, patches are extracted over the interest points detected from the original image using Wavelet based interest point detector. Here QWT magnitude and phase features are computed for every patch. Then these features are trained, tested and classified using SVM classifier in order to have supervised learning model. In order to compare the performance of local feature with global feature, the transform is applied to the entire image and the global features are derived. The performance of QWT is compared with discrete wavelet transform (DWT and dual tree discrete wavelet transform (DTDWT. Observations revealed that QWT outperforms the DWT and shift invariant DTDWT with lesser equal error rate. The experimental evaluation is done using the complex Graz databases.Defence Science Journal, Vol. 64, No. 4, July 2014, pp. 350-357, DOI:

  4. Analysis of manufacturing based on object oriented discrete event simulation

    Eirik Borgen


    Full Text Available This paper describes SIMMEK, a computer-based tool for performing analysis of manufacturing systems, developed at the Production Engineering Laboratory, NTH-SINTEF. Its main use will be in analysis of job shop type of manufacturing. But certain facilities make it suitable for FMS as well as a production line manufacturing. This type of simulation is very useful in analysis of any types of changes that occur in a manufacturing system. These changes may be investments in new machines or equipment, a change in layout, a change in product mix, use of late shifts, etc. The effects these changes have on for instance the throughput, the amount of VIP, the costs or the net profit, can be analysed. And this can be done before the changes are made, and without disturbing the real system. Simulation takes into consideration, unlike other tools for analysis of manufacturing systems, uncertainty in arrival rates, process and operation times, and machine availability. It also shows the interaction effects a job which is late in one machine, has on the remaining machines in its route through the layout. It is these effects that cause every production plan not to be fulfilled completely. SIMMEK is based on discrete event simulation, and the modeling environment is object oriented. The object oriented models are transformed by an object linker into data structures executable by the simulation kernel. The processes of the entity objects, i.e. the products, are broken down to events and put into an event list. The user friendly graphical modeling environment makes it possible for end users to build models in a quick and reliable way, using terms from manufacturing. Various tests and a check of model logic are helpful functions when testing validity of the models. Integration with software packages, with business graphics and statistical functions, is convenient in the result presentation phase.

  5. Measurement of spatial object's exterior attitude based on linear CCD


    It is difficult to realize real-time measurement of exterior attitude by the traditional systems based on the area image sensor which have conflict between speed and accuracy.The subsystem for three-dimensional (3D) coordinate rcconstruction of point target (S3DCRPT) which is composed of three one-dimensional (1D) cameras based on linear charge-coupled device (CCD) can determine the distant light spots' spatial position. The attitude angle of the measured object is determined by the spatial solution while the coordinate reconstruction is separately carried on by the S3DCRPT with some point cooperation targets (PCTs) on the measured object. A new optical system is designed to solve the interference problem with one-to-one relationship between the PCTs and the S3DCRPT optical subsystems,which improves the measurement accuracy and saves space. The mathematical model of the attitude measurement is established,and partial and global calibrations are realized for the multi-camera attitude measurement system.The test results show the feasibility of the exterior attitude measurement based on linear CCD.

  6. Cloud Aggregation and Bursting for Object Based Sharable Environment

    Mr. Pradeep Kumar Tripathi


    Full Text Available Cloud computing promises innate scalability and high availability at low cost. So far cloud storage deployments were subject to big companies but an increasing amount of available open-source systems allow also smaller private cloud installations. In this paper we discuss cloud aggregation and cloud bursting with their empirical review. Based on the review we map class and object in the sharable small clouds for making clouds more efficient. We also consider some of the security concern for the cloud computing for authorized data sharing between clouds.

  7. An object-based methodology for knowledge representation

    Kelsey, R.L. [Los Alamos National Lab., NM (United States)|New Mexico State Univ., Las Cruces, NM (United States); Hartley, R.T. [New Mexico State Univ., Las Cruces, NM (United States); Webster, R.B. [Los Alamos National Lab., NM (United States)


    An object based methodology for knowledge representation is presented. The constructs and notation to the methodology are described and illustrated with examples. The ``blocks world,`` a classic artificial intelligence problem, is used to illustrate some of the features of the methodology including perspectives and events. Representing knowledge with perspectives can enrich the detail of the knowledge and facilitate potential lines of reasoning. Events allow example uses of the knowledge to be represented along with the contained knowledge. Other features include the extensibility and maintainability of knowledge represented in the methodology.

  8. Multiple-input multiple-output synthetic aperture ladar system for wide-range swath with high azimuth resolution.

    Tang, Yu; Qin, Bao; Yan, Yun; Xing, Mengdao


    For the trade-off between the high azimuth resolution and the wide-range swath in the single-input single-output synthetic aperture ladar (SAL) system, the range swath of the SAL system is restricted to a narrow range, this paper proposes a multiple-input multiple-output (MIMO) synthetic aperture ladar system. The MIMO system adopts a low pulse repetition frequency (PRF) to avoid a range ambiguity for the wide-range swath and in azimuth adopts the multi-channel method to achieve azimuth high resolution from the unambiguous azimuth wide-spectrum signal, processed through adaptive digital beam-forming technology. Simulations and analytical results are presented.

  9. Low-power portable scanning imaging ladar system

    Pyburn, Dana; Leon, Roberto; Haji-Saeed, B.; Sengupta, Sandip K.; Testorf, Markus; Kierstead, John; Khoury, Jehad; Woods, Charles L.; Lorenzo, Joseph


    We propose and are in the process of progressively implementing an improved architecture for a laser based system to acquire intensity and range images of hard targets in real-time. The system design emphasizes the use of low power laser sources in conjunction with optical preamplification of target return signals to maintain eye safety without incurring the associated performance penalty. The design leverages advanced fiber optic component technology developed for the commercial market to achieve compactness and low power consumption without the high costs and long lead times associated with custom military devices. All important system parameters are designed to be configured in the field, by the user, in software, allowing for adaptive reconfiguration for different missions and targets. Recently we have started our transition from the initial test bed, using a laser in the visible wavelength, into the final system with a 1550nm diode laser. Currently we are able to acquire and display 3-D false-color and gray-scale images, in the laboratory, at moderate frame rates in real-time. Commercial off-the-shelf data acquisition and signal processing software on a desktop computer equipped with commercial acquisition hardware is utilized. Significant improvements in both range and spatial resolution are expected in the near future.

  10. Introducing shape constraints into object-based traveltime tomography

    Gaullier, G.; Charbonnier, P.; Heitz, F.; Côte, P.


    Traveltime tomography is a difficult, ill-posed reconstruction problem due to the nonlinearity of the forward model and the limited number of measurements usually available. In such an adverse situation, pixel-based regularization methods are generally unable to provide satisfactory reconstructions. In this paper we propose a novel object-based reconstruction method that introduces prior information about the shape of the structures to be reconstructed, which yields high quality geoacoustic inversion. The proposed method approaches the forward model by a series of linear problems, leading to a sequence of minimizations during which the shape prior is introduced. The method is demonstrated on synthetic and real data, collected on a specific bench dedicated to non-destructive testing of civil engineering structures.

  11. Object-based landslide detection in different geographic regions

    Friedl, Barbara; Hölbling, Daniel; Eisank, Clemens; Blaschke, Thomas


    Landslides occur in almost all mountainous regions of the world and rank among the most severe natural hazards. In the last decade - according to the world disaster report 2014 published by the International Federation of Red Cross and Red Crescent Societies (IRFC) - more than 9.000 people were killed by mass movements, more than 3.2 million people were affected and the total amount of disaster estimated damage accounts to more than 1.700 million US dollars. The application of remote sensing data for mapping landslides can contribute to post-disaster reconstruction or hazard mitigation, either by providing rapid information about the spatial distribution and location of landslides in the aftermath of triggering events or by creating and updating landslide inventories. This is especially valid for remote and inaccessible areas, where information on landslides is often lacking. However, reliable methods are needed for extracting timely and relevant information about landslides from remote sensing data. In recent years, novel methods such as object-based image analysis (OBIA) have been successfully employed for semi-automated landslide mapping. Several studies revealed that OBIA frequently outperforms pixel-based approaches, as a range of image object properties (spectral, spatial, morphometric, contextual) can be exploited during the analysis. However, object-based methods are often tailored to specific study areas, and thus, the transferability to regions with different geological settings, is often limited. The present case study evaluates the transferability and applicability of an OBIA approach for landslide detection in two distinct regions, i.e. the island of Taiwan and Austria. In Taiwan, sub-areas in the Baichi catchment in the North and in the Huaguoshan catchment in the southern-central part of the island are selected; in Austria, landslide-affected sites in the Upper Salzach catchment in the federal state of Salzburg are investigated. For both regions

  12. Object-based Analysis for Extraction of Dominant Tree Species

    Meiyun; SHAO; Xia; JING; Lu; WANG


    As forest is of great significance for our whole development and the sustainable plan is so focus on it. It is very urgent for us to have the whole distribution,stock volume and other related information about that. So the forest inventory program is on our schedule. Aiming at dealing with the problem in extraction of dominant tree species,we tested the highly hot method-object-based analysis. Based on the ALOS image data,we combined multi-resolution in e Cognition software and fuzzy classification algorithm. Through analyzing the segmentation results,we basically extract the spruce,the pine,the birch and the oak of the study area. Both the spectral and spatial characteristics were derived from those objects,and with the help of GLCM,we got the differences of each species. We use confusion matrix to do the Classification accuracy assessment compared with the actual ground data and this method showed a comparatively good precision as 87% with the kappa coefficient 0. 837.

  13. Reactive underwater object inspection based on artificial electric sense.

    Lebastard, Vincent; Boyer, Frédéric; Lanneau, Sylvain


    Weakly electric fish can perform complex cognitive tasks based on extracting information from blurry electric images projected from their immediate environment onto their electro-sensitive skin. In particular they can be trained to recognize the intrinsic properties of objects such as their shape, size and electric nature. They do this by means of novel perceptual strategies that exploit the relations between the physics of a self-generated electric field, their body morphology and the ability to perform specific movement termed probing motor acts (PMAs). In this article we artificially reproduce and combine these PMAs to build an autonomous control strategy that allows an artificial electric sensor to find electrically contrasted objects, and to orbit around them based on a minimum set of measurements and simple reactive feedback control laws of the probe's motion. The approach does not require any simulation models and could be implemented on an autonomous underwater vehicle (AUV) equipped with artificial electric sense. The AUV has only to satisfy certain simple geometric properties, such as bi-laterally (left/right) symmetrical electrodes and possess a reasonably high aspect (length/width) ratio.

  14. Strength of object representation: its key role in object-based attention for determining the competition result between Gestalt and top-down objects.

    Zhao, Jingjing; Wang, Yonghui; Liu, Donglai; Zhao, Liang; Liu, Peng


    It was found in previous studies that two types of objects (rectangles formed according to the Gestalt principle and Chinese words formed in a top-down fashion) can both induce an object-based effect. The aim of the present study was to investigate how the strength of an object representation affects the result of the competition between these two types of objects based on research carried out by Liu, Wang and Zhou [(2011) Acta Psychologica, 138(3), 397-404]. In Experiment 1, the rectangles were filled with two different colors to increase the strength of Gestalt object representation, and we found that the object effect changed significantly for the different stimulus types. Experiment 2 used Chinese words with various familiarities to manipulate the strength of the top-down object representation. As a result, the object-based effect induced by rectangles was observed only when the Chinese word familiarity was low. These results suggest that the strength of object representation determines the result of competition between different types of objects.

  15. Object Based and Pixel Based Classification Using Rapideye Satellite Imager of ETI-OSA, Lagos, Nigeria

    Esther Oluwafunmilayo Makinde; Ayobami Taofeek Salami; James Bolarinwa Olaleye; Oluwapelumi Comfort Okewusi


    Several studies have been carried out to find an appropriate method to classify the remote sensing data. Traditional classification approaches are all pixel-based, and do not utilize the spatial information within an object which is an important source of information to image classification. Thus, this study compared the pixel based and object based classification algorithms using RapidEye satellite image of Eti-Osa LGA, Lagos. In the object-oriented approach, the image was segmented to homog...

  16. Object Persistence: A Framework Based On Design Patterns

    Kienzle, Jörg; Romanovsky, Alexander


    The poster presents a framework for providing object persistence in object-oriented programming languages without modifying the run-time system or the language itself. The framework does not rely on any kind of special programming language features. It only uses basic object-oriented programming techniques, and is therefore implementable in any object-oriented programming language.

  17. Noise Based Detection and Segmentation of Nebulous Objects

    Akhlaghi, Mohammad


    A noise based non parametric technique to detect nebulous objects, for example irregular or clumpy galaxies, and their structure in noise is introduced. Noise based and non parametric imply that it imposes negligible constraints on the properties of the targets and that it employs no regression analysis or fittings. The sub-sky detection threshold is defined, and initial detections are found, independent of the sky value. False detections are then estimated and removed using the ambient noise as a reference. This results in a purity level of 0.86 for the final detections as compared to 0.27 for SExtractor when a completeness of 1 is desired for a sample extremely faint and diffuse identical mock galaxy profiles. The dispersion in their measured magnitudes is less by one magnitude, allowing much more accurate photometry. Defining the accuracy of detection as the difference of the measured sky with the known background of mock images, an order of magnitude less biased sky measurement is achieved. A non parametr...

  18. Poka Yoke system based on image analysis and object recognition

    Belu, N.; Ionescu, L. M.; Misztal, A.; Mazăre, A.


    Poka Yoke is a method of quality management which is related to prevent faults from arising during production processes. It deals with “fail-sating” or “mistake-proofing”. The Poka-yoke concept was generated and developed by Shigeo Shingo for the Toyota Production System. Poka Yoke is used in many fields, especially in monitoring production processes. In many cases, identifying faults in a production process involves a higher cost than necessary cost of disposal. Usually, poke yoke solutions are based on multiple sensors that identify some nonconformities. This means the presence of different equipment (mechanical, electronic) on production line. As a consequence, coupled with the fact that the method itself is an invasive, affecting the production process, would increase its price diagnostics. The bulky machines are the means by which a Poka Yoke system can be implemented become more sophisticated. In this paper we propose a solution for the Poka Yoke system based on image analysis and identification of faults. The solution consists of a module for image acquisition, mid-level processing and an object recognition module using associative memory (Hopfield network type). All are integrated into an embedded system with AD (Analog to Digital) converter and Zync 7000 (22 nm technology).

  19. Adaptive Multi-Objective Optimization Based on Feedback Design

    窦立谦; 宗群; 吉月辉; 曾凡琳


    The problem of adaptive multi-objective optimization(AMOO) has received extensive attention due to its practical significance.An important issue in optimizing a multi-objective system is adjusting the weighting coefficients of multiple objectives so as to keep track of various conditions.In this paper,a feedback structure for AMOO is designed.Moreover,the reinforcement learning combined with hidden biasing information is applied to online tuning weighting coefficients of objective functions.Finally,the prop...

  20. Vision-based robotic system for object agnostic placing operations

    Rofalis, Nikolaos; Nalpantidis, Lazaros; Andersen, Nils Axel


    to operate within an unknown environment manipulating unknown objects. The developed system detects objects, finds matching compartments in a placing box, and ultimately grasps and places the objects there. The developed system exploits 3D sensing and visual feature extraction. No prior knowledge is provided...

  1. Object-based Dimensionality Reduction in Land Surface Phenology Classification

    Brian E. Bunker


    Full Text Available Unsupervised classification or clustering of multi-decadal land surface phenology provides a spatio-temporal synopsis of natural and agricultural vegetation response to environmental variability and anthropogenic activities. Notwithstanding the detailed temporal information available in calibrated bi-monthly normalized difference vegetation index (NDVI and comparable time series, typical pre-classification workflows average a pixel’s bi-monthly index within the larger multi-decadal time series. While this process is one practical way to reduce the dimensionality of time series with many hundreds of image epochs, it effectively dampens temporal variation from both intra and inter-annual observations related to land surface phenology. Through a novel application of object-based segmentation aimed at spatial (not temporal dimensionality reduction, all 294 image epochs from a Moderate Resolution Imaging Spectroradiometer (MODIS bi-monthly NDVI time series covering the northern Fertile Crescent were retained (in homogenous landscape units as unsupervised classification inputs. Given the inherent challenges of in situ or manual image interpretation of land surface phenology classes, a cluster validation approach based on transformed divergence enabled comparison between traditional and novel techniques. Improved intra-annual contrast was clearly manifest in rain-fed agriculture and inter-annual trajectories showed increased cluster cohesion, reducing the overall number of classes identified in the Fertile Crescent study area from 24 to 10. Given careful segmentation parameters, this spatial dimensionality reduction technique augments the value of unsupervised learning to generate homogeneous land surface phenology units. By combining recent scalable computational approaches to image segmentation, future work can pursue new global land surface phenology products based on the high temporal resolution signatures of vegetation index time series.


    J. Shi


    Full Text Available Unmanned aerial vehicles (UAV have been widely used to capture and down-link real-time videos/images. However, their role as a low-cost airborne platform for capturing high-resolution, geo-referenced still imagery has not been fully utilized. The images obtained from UAV are advantageous over remote sensing images as they can be obtained at a low cost and potentially no risk to human life. However, these images are distorted due to the noise generated by the rotary wings which limits the usefulness of such images. One potential application of such images is to detect changes between the images of the same area which are collected over time. Change detection is of widespread interest due to a large number of applications, including surveillance and civil infrastructure. Although UAVs can provide images with high resolution in a portable and easy way, such images only cover small parts of the entire field of interest and are often with high deformation. Until now, there is not much application of change detection for UAV images. Also the traditional pixel-based change detection method does not give satisfactory results for such images. In this paper, we have proposed a novel object-based method for change detection using UAV images which can overcome the effect of deformation and can fully utilize the high resolution capability of UAV images. The developed method can be divided into five main blocks: pre-processing, image matching, image segmentation and feature extraction, change detection and accuracy evaluation. The pre-processing step is further divided into two sub-steps: the first sub-step is to geometrically correct the bi-temporal image based on the geo-reference information (GPS/INS installed on the UAV system, and the second sub-step is the radiometric normalization using a histogram method. The image matching block uses the well-known scale-invariant feature transform (SIFT algorithm to match the same areas in the images and then

  3. Surface investigation of naturally corroded gilded copper-based objects

    Ingo, G.M., E-mail: [Istituto per lo Studio dei Materiali Nanostrutturati—Consiglio Nazionale delle Ricerche (ISMN—CNR), Area della Ricerca del CNR Roma1-Montelibretti, via Salaria Km 29.3, 00016 Monterotondo Scalo, Rome (Italy); Riccucci, C. [Istituto per lo Studio dei Materiali Nanostrutturati—Consiglio Nazionale delle Ricerche (ISMN—CNR), Area della Ricerca del CNR Roma1-Montelibretti, via Salaria Km 29.3, 00016 Monterotondo Scalo, Rome (Italy); Lavorgna, M.; Salzano de Luna, M. [Istituto per i Polimeri, Compositi e Biomateriali—Consiglio Nazionale delle Ricerche (IPCB—CNR), P.le E. Fermi 1, 80050 Portici, Napoli (Italy); Pascucci, M. [Istituto per lo Studio dei Materiali Nanostrutturati—Consiglio Nazionale delle Ricerche (ISMN—CNR), Area della Ricerca del CNR Roma1-Montelibretti, via Salaria Km 29.3, 00016 Monterotondo Scalo, Rome (Italy); Di Carlo, G., E-mail: [Istituto per lo Studio dei Materiali Nanostrutturati—Consiglio Nazionale delle Ricerche (ISMN—CNR), Area della Ricerca del CNR Roma1-Montelibretti, via Salaria Km 29.3, 00016 Monterotondo Scalo, Rome (Italy)


    Highlights: • Naturally corroded Au and Ag coated Cu-based objects studied by XPS, SEM + EDS and OM. • The main degrading agents are Cl, S and P species from surrounding environment. • Metal galvanic coupling enhances corrosion phenomena. • Corrosion forms a layered patina of noble metal remains, soil components and Cu{sub 2}O. • Useful information to tailor safe cleaning and reliable conservation strategies. - Abstract: Gold and silver coated copper-based artefacts subjected to long-term natural corrosion phenomena were studied by means of the combined use of X-ray photoelectron spectroscopy (XPS), scanning electron microscopy coupled with energy dispersive X-ray spectroscopy (SEM + EDS), and optical microscopy (OM). The results allowed the identification of the chemistry and structure of the Au or Ag layers deposited by fire-gilding or mercury-silvering and the determination of the corrosion products formed due to interaction with the surrounding environment. Different degradation phenomena of the noble metal layer and copper substrate are induced by the presence of chlorine, sulphur and phosphorous and they are boosted by the metal galvanic coupling which makes gilded-metal art works unstable from a chemico-physical point of view. The SEM + EDS and OM results also suggest that particular care must be used during the removal of the encrustations and of the external corrosion products to avoid the loss of the remains of the noble layer often floating or embedded in the corrosion products. Furthermore, in order to avoid the reaction between nantokite (CuCl) and moisture the use no or low toxic inhibitors is suggested to avoid further severe degradation phenomena enhancing the long-lasting chemico-physical stability of these precious artefacts and giving them a greater chance of survival.

  4. Object Based and Pixel Based Classification Using Rapideye Satellite Imager of ETI-OSA, Lagos, Nigeria

    Esther Oluwafunmilayo Makinde


    Full Text Available Several studies have been carried out to find an appropriate method to classify the remote sensing data. Traditional classification approaches are all pixel-based, and do not utilize the spatial information within an object which is an important source of information to image classification. Thus, this study compared the pixel based and object based classification algorithms using RapidEye satellite image of Eti-Osa LGA, Lagos. In the object-oriented approach, the image was segmented to homogenous area by suitable parameters such as scale parameter, compactness, shape etc. Classification based on segments was done by a nearest neighbour classifier. In the pixel-based classification, the spectral angle mapper was used to classify the images. The user accuracy for each class using object based classification were 98.31% for waterbody, 92.31% for vegetation, 86.67% for bare soil and 90.57% for Built up while the user accuracy for the pixel based classification were 98.28% for waterbody, 84.06% for Vegetation 86.36% and 79.41% for Built up. These classification techniques were subjected to accuracy assessment and the overall accuracy of the Object based classification was 94.47%, while that of Pixel based classification yielded 86.64%. The result of classification and accuracy assessment show that the object-based approach gave more accurate and satisfying results

  5. Object Recognition Algorithm Utilizing Graph Cuts Based Image Segmentation

    Zhaofeng Li


    Full Text Available This paper concentrates on designing an object recognition algorithm utilizing image segmentation. The main innovations of this paper lie in that we convert the image segmentation problem into graph cut problem, and then the graph cut results can be obtained by calculating the probability of intensity for a given pixel which is belonged to the object and the background intensity. After the graph cut process, the pixels in a same component are similar, and the pixels in different components are dissimilar. To detect the objects in the test image, the visual similarity between the segments of the testing images and the object types deduced from the training images is estimated. Finally, a series of experiments are conducted to make performance evaluation. Experimental results illustrate that compared with existing methods, the proposed scheme can effectively detect the salient objects. Particularly, we testify that, in our scheme, the precision of object recognition is proportional to image segmentation accuracy

  6. MOPSO-based multi-objective TSO planning considering uncertainties

    Wang, Qi; Zhang, Chunyu; Ding, Yi


    factors, i.e. load growth, generation capacity and line faults, and aims to enhance the transmission system via the multi-objective TSO planning (MOTP) approach. The proposed MOTP approach optimizes three objectives simultaneously, namely the probabilistic available transfer capability (PATC), investment...

  7. Rule set transferability for object-based feature extraction

    Anders, N.S.; Seijmonsbergen, Arie C.; Bouten, Willem


    Cirques are complex landforms resulting from glacial erosion and can be used to estimate Equilibrium Line Altitudes and infer climate history. Automated extraction of cirques may help research on glacial geomorphology and climate change. Our objective was to test the transferability of an object-

  8. Multimedia Learning Systems Based on IEEE Learning Object Metadata (LOM).

    Holzinger, Andreas; Kleinberger, Thomas; Muller, Paul

    One of the "hottest" topics in recent information systems and computer science is metadata. Learning Object Metadata (LOM) appears to be a very powerful mechanism for representing metadata, because of the great variety of LOM Objects. This is on of the reasons why the LOM standard is repeatedly cited in projects in the field of eLearning…

  9. Partial Evaluation for Class-Based Object-Oriented Languages

    Schultz, Ulrik Pagh


    Object-oriented programming facilitates the development of generic software, but at a significant cost in terms of performance. We apply partial evaluation to object-oriented programs, to automatically map generic software into specific implementations. In this paper we give a concise, formal des...... description of a simple partial evaluator for a minimal object-oriented language, and give directions for extending this partial evaluator to handle realistic programs.......Object-oriented programming facilitates the development of generic software, but at a significant cost in terms of performance. We apply partial evaluation to object-oriented programs, to automatically map generic software into specific implementations. In this paper we give a concise, formal...

  10. Orbit determination of space objects based on sparse optical data

    Milani, A; Farnocchia, D; Rossi, A; Schildknecht, T; Jehn, R


    While building up a catalog of Earth orbiting objects, if the available optical observations are sparse, not deliberate follow ups of specific objects, no orbit determination is possible without previous correlation of observations obtained at different times. This correlation step is the most computationally intensive, and becomes more and more difficult as the number of objects to be discovered increases. In this paper we tested two different algorithms (and the related prototype software) recently developed to solve the correlation problem for objects in geostationary orbit (GEO), including the accurate orbit determination by full least squares solutions with all six orbital elements. Because of the presence in the GEO region of a significant subpopulation of high area to mass objects, strongly affected by non-gravitational perturbations, it was actually necessary to solve also for dynamical parameters describing these effects, that is to fit between 6 and 8 free parameters for each orbit. The validation w...

  11. Fragment-based learning of visual object categories.

    Hegdé, Jay; Bart, Evgeniy; Kersten, Daniel


    When we perceive a visual object, we implicitly or explicitly associate it with a category we know. It is known that the visual system can use local, informative image fragments of a given object, rather than the whole object, to classify it into a familiar category. How we acquire informative fragments has remained unclear. Here, we show that human observers acquire informative fragments during the initial learning of categories. We created new, but naturalistic, classes of visual objects by using a novel "virtual phylogenesis" (VP) algorithm that simulates key aspects of how biological categories evolve. Subjects were trained to distinguish two of these classes by using whole exemplar objects, not fragments. We hypothesized that if the visual system learns informative object fragments during category learning, then subjects must be able to perform the newly learned categorization by using only the fragments as opposed to whole objects. We found that subjects were able to successfully perform the classification task by using each of the informative fragments by itself, but not by using any of the comparable, but uninformative, fragments. Our results not only reveal that novel categories can be learned by discovering informative fragments but also introduce and illustrate the use of VP as a versatile tool for category-learning research.

  12. A Biological Hierarchical Model Based Underwater Moving Object Detection

    Jie Shen


    Full Text Available Underwater moving object detection is the key for many underwater computer vision tasks, such as object recognizing, locating, and tracking. Considering the super ability in visual sensing of the underwater habitats, the visual mechanism of aquatic animals is generally regarded as the cue for establishing bionic models which are more adaptive to the underwater environments. However, the low accuracy rate and the absence of the prior knowledge learning limit their adaptation in underwater applications. Aiming to solve the problems originated from the inhomogeneous lumination and the unstable background, the mechanism of the visual information sensing and processing pattern from the eye of frogs are imitated to produce a hierarchical background model for detecting underwater objects. Firstly, the image is segmented into several subblocks. The intensity information is extracted for establishing background model which could roughly identify the object and the background regions. The texture feature of each pixel in the rough object region is further analyzed to generate the object contour precisely. Experimental results demonstrate that the proposed method gives a better performance. Compared to the traditional Gaussian background model, the completeness of the object detection is 97.92% with only 0.94% of the background region that is included in the detection results.

  13. Space Object Tracking Method Based on a Snake Model

    Zhan-wei, Xu; Xin, Wang


    In this paper, aiming at the problem of unstable tracking of low-orbit variable and bright space objects, adopting an active contour model, a kind of improved GVF (Gradient Vector Flow) - Snake algorithm is proposed to realize the real-time search of the real object contour on the CCD image. Combined with the Kalman filter for prediction, a new adaptive tracking method is proposed for space objects. Experiments show that this method can overcome the tracking error caused by the fixed window, and improve the tracking robustness.

  14. A Vision for Spaceflight Reliability: NASA's Objectives Based Strategy

    Groen, Frank; Evans, John; Hall, Tony


    In defining the direction for a new Reliability and Maintainability standard, OSMA has extracted the essential objectives that our programs need, to undertake a reliable mission. These objectives have been structured to lead mission planning through construction of an objective hierarchy, which defines the critical approaches for achieving high reliability and maintainability (R M). Creating a hierarchy, as a basis for assurance implementation, is a proven approach; yet, it holds the opportunity to enable new directions, as NASA moves forward in tackling the challenges of space exploration.

  15. Remote sensing clustering analysis based on object-based interval modeling

    He, Hui; Liang, Tianheng; Hu, Dan; Yu, Xianchuan


    In object-based clustering, image data are segmented into objects (groups of pixels) and then clustered based on the objects' features. This method can be used to automatically classify high-resolution, remote sensing images, but requires accurate descriptions of object features. In this paper, we ascertain that interval-valued data model is appropriate for describing clustering prototype features. With this in mind, we developed an object-based interval modeling method for high-resolution, multiband, remote sensing data. We also designed an adaptive interval-valued fuzzy clustering method. We ran experiments utilizing images from the SPOT-5 satellite sensor, for the Pearl River Delta region and Beijing. The results indicate that the proposed algorithm considers both the anisotropy of the remote sensing data and the ambiguity of objects. Additionally, we present a new dissimilarity measure for interval vectors, which better separates the interval vectors generated by features of the segmentation units (objects). This approach effectively limits classification errors caused by spectral mixing between classes. Compared with the object-based unsupervised classification method proposed earlier, the proposed algorithm improves the classification accuracy without increasing computational complexity.

  16. Tailoring Green Infrastructure Implementation Scenarios based on Stormwater Management Objectives

    Green infrastructure (GI) refers to stormwater management practices that mimic nature by soaking up, storing, and controlling onsite. GI practices can contribute reckonable benefits towards meeting stormwater management objectives, such as runoff peak shaving, volume reduction, f...

  17. Dominant object detection for autonomous vision-based surveillance

    Celik, H.


    The deployment of visual surveillance and monitoring systems has reached massive proportions. Consequently, a need to automate the processes involved in retrieving useful information from surveillance videos, such as detecting and counting objects, and interpreting their individual and joint behavio

  18. Dynamic Cell Formation based on Multi-objective Optimization Model

    Guozhu Jia


    Full Text Available In this paper, a multi-objective model is proposed to address the dynamic cellular manufacturing (DCM formation problem. This model considers four conflicting objectives: relocation cost, machine utilization, material handling cost and maintenance cost. The model also considers the situation that some machines could be shared by more than one cell at the same period. A genetic algorithm is applied to get the solution of this mathematical model. Three numerical examples are simulated to evaluate the validity of this model.  

  19. Image-based modeling of objects and human faces

    Zhang, Zhengyou


    In this paper, provided is an overview of our project on 3D object and face modeling from images taken by a free-moving camera. We strive to advance the state of the art in 3D computer vision, and develop flexible and robust techniques for ordinary users to gain 3D experience from a ste of casually collected 2D images. Applications include product advertisement on the Web, virtual conference, and interactive games. We briefly cover the following topics: camera calibration, stereo rectification, image matching, 3D photo editing, object modeling, and face modeling. Demos on the last three topics will be shown during the conference.

  20. Objective, Way and Method of Faculty Management Based on Ergonomics

    WANG, Hong-bin; Liu, Yu-hua


    The core problem that influences educational quality of talents in colleges and universities is the faculty management. Without advanced faculty, it is difficult to cultivate excellent talents. With regard to some problems in present faculty construction of colleges and universities, this paper puts forward the new objectives, ways and methods of…

  1. Bandwidth trading under misaligned objectives: decentralized measurement-based control

    Mandjes, M.R.H.; Ramakrishnan, M.


    This paper studies the interplay between a profit-maximizing network and a number of users competing for the finite bandwidth on each link. In our setting, the objectives of the network and the users are ‘misaligned’, in that the prices that optimize the network’s profit do not maximize the aggregat

  2. Application of Object-Based Industrial Controls for Cryogenics

    Casas-Cubillos, J; Gomes, P; Pezzetti, M; Sicard, Claude Henri; Varas, F J


    The first application of the CERN Unified Industrial Control system (UNICOS) has been developed for the 1.8 K refrigerator at point 1.8 in mid-2001. This paper presents the engineering methods used for application development, in order to reach the objectives of maintainability and reusability, in the context of a development done by an external consortium of engineering firms. It will also review the lessons learned during this first development and the improvements planned for the next applications.

  3. Development of learning object from IP-based television programme

    Fallahkhair, Sanaz


    The TAMALLE+[1, 2] is a prototype system that supports learners in their television viewing, enhancing informal language learning via interactive television and mobile phones. In this paper we describe a learner-centred study designed to elicit criteria for selection of those language learning object whose annotation or explanation through TAMALLE+ system could best enhance the advanced learner’s understanding of popular broadcast television programmes in English. We identified two main areas...

  4. Scheduler for monitoring objects orbiting earth using satellite-based telescopes

    Olivier, Scot S; Pertica, Alexander J; Riot, Vincent J; De Vries, Willem H; Bauman, Brian J; Nikolaev, Sergei; Henderson, John R; Phillion, Donald W


    An ephemeris refinement system includes satellites with imaging devices in earth orbit to make observations of space-based objects ("target objects") and a ground-based controller that controls the scheduling of the satellites to make the observations of the target objects and refines orbital models of the target objects. The ground-based controller determines when the target objects of interest will be near enough to a satellite for that satellite to collect an image of the target object based on an initial orbital model for the target objects. The ground-based controller directs the schedules to be uploaded to the satellites, and the satellites make observations as scheduled and download the observations to the ground-based controller. The ground-based controller then refines the initial orbital models of the target objects based on the locations of the target objects that are derived from the observations.

  5. Segmentation of object-based video of gaze communication

    Aghito, Shankar Manuel; Stegmann, Mikkel Bille; Forchhammer, Søren;


    Aspects of video communication based on gaze interaction are considered. The overall idea is to use gaze interaction to control video, e.g. for video conferencing. Towards this goal, animation of a facial mask is demonstrated. The animation is based on images using Active Appearance Models (AAM)....

  6. Defining competency-based evaluation objectives in family medicine

    Allen, Tim; Brailovsky, Carlos; Rainsberry, Paul; Lawrence, Katherine; Crichton, Tom; Carpentier, Marie-Pierre; Visser, Shaun


    Abstract Objective To develop a definition of competence in family medicine sufficient to guide a review of Certification examinations by the Board of Examiners of the College of Family Physicians of Canada. Design Delphi analysis of responses to a 4-question postal survey. Setting Canadian family practice. Participants A total of 302 family physicians who have served as examiners for the College of Family Physicians of Canada’s Certification examination. Methods A survey comprising 4 short-answer questions was mailed to the 302 participating family physicians asking them to list elements that define competence in family medicine among newly certified family physicians beginning independent practice. Two expert groups used a modified Delphi consensus process to analyze responses and generate 2 basic components of this definition of competence: first, the problems that a newly practising family physician should be competent to handle; second, the qualities, behaviour, and skills that characterize competence at the start of independent practice. Main findings Response rate was 54%; total number of elements among all responses was 5077, for an average 31 per respondent. Of the elements, 2676 were topics or clinical situations to be dealt with; the other 2401 were skills, behaviour patterns, or qualities, without reference to a specific clinical problem. The expert groups identified 6 essential skills, the phases of the clinical encounter, and 99 priority topics as the descriptors used by the respondents. More than 20% of respondents cited 30 of the topics. Conclusion Family physicians define the domain of competence in family medicine in terms of 6 essential skills, the phases of the clinical encounter, and priority topics. This survey represents the first level of definition of evaluation objectives in family medicine. Definition of the interactions among these elements will permit these objectives to become detailed enough to effectively guide assessment. PMID

  7. A Framework for Geographic Object-Based Image Analysis (GEOBIA) based on geographic ontology

    Gu, H. Y.; Li, H. T.; Yan, L.; Lu, X. J.


    GEOBIA (Geographic Object-Based Image Analysis) is not only a hot topic of current remote sensing and geographical research. It is believed to be a paradigm in remote sensing and GIScience. The lack of a systematic approach designed to conceptualize and formalize the class definitions makes GEOBIA a highly subjective and difficult method to reproduce. This paper aims to put forward a framework for GEOBIA based on geographic ontology theory, which could implement "Geographic entities - Image objects - Geographic objects" true reappearance. It consists of three steps, first, geographical entities are described by geographic ontology, second, semantic network model is built based on OWL(ontology web language), at last, geographical objects are classified with decision rule or other classifiers. A case study of farmland ontology was conducted for describing the framework. The strength of this framework is that it provides interpretation strategies and global framework for GEOBIA with the property of objective, overall, universal, universality, etc., which avoids inconsistencies caused by different experts' experience and provides an objective model for mage analysis.

  8. Evaluation of satellite-based precipitation estimates in winter season using an object-based approach

    Li, J.; Hsu, K.; AghaKouchak, A.; Sorooshian, S.


    Verification has become an integral component of satellite precipitation algorithms and products. A number of object-based verification methods have been proposed to provide diagnostic information regarding the precipitation products' ability to capture the spatial pattern, intensity, and placement of precipitation. However, most object-based methods are not capable of investigating precipitation objects at the storm-scale. In this study, an image processing approach known as watershed segmentation was adopted to detect the storm-scale rainfall objects. Then, a fuzzy logic-based technique was utilized to diagnose and analyze storm-scale object attributes, including centroid distance, area ratio, intersection area ratio and orientation angle difference. Three verification metrics (i.e., false alarm ratio, missing ratio and overall membership score) were generated for validation and verification. Three satellite-based precipitation products, including PERSIANN, CMORPH, 3B42RT, were evaluated against NOAA stage IV MPE multi-sensor composite rain analysis at 0.25° by 0.25° on a daily scale in the winter season of 2010 over the contiguous United States. Winter season is dominated by frontal systems which usually have larger area coverage. All three products and the stage IV observation tend to find large size storm objects. With respect to the evaluation attributes, PERSIANN tends to obtain larger area ratio and consequently has larger centroid distance to the stage IV observations, while 3B42RT are found to be closer to the stage IV for the object size. All evaluation products give small orientation angle differences but vary significantly for the missing ratio and false alarm ratio. This implies that satellite estimates can fail to detect storms in winter. The overall membership scores are close for all three different products which indicate that all three satellite-based precipitation products perform well for capturing the spatial and geometric characteristics of

  9. Model-based beam control for illumination of remote objects

    Chandler, Susan M.; Lukesh, Gordon W.; Voelz, David; Basu, Santasri; Sjogren, Jon A.


    On September 1, 2003, Nukove Scientific Consulting, together with partner New Mexico State University, began work on a Phase 1 Small Business Technology TRansfer (STTR) grant from the United States Air Force Office of Scientific Research (AFOSR). The purpose of the grant was to show the feasibility of taking Nukove's pointing estimation technique from a post-processing tool for estimation of laser system characteristics to a real-time tool usable in the field. Nukove's techniques for pointing, shape, and OCS estimation do not require an imaging sensor nor a target board, thus estimates may be made very quickly. To prove feasibility, Nukove developed an analysis tool RHINO (Real-time Histogram Interpretation of Numerical Observations) and successfully demonstrated the emulation of real-time, frame-by-frame estimation of laser system characteristics, with data streamed into the tool and the estimates displayed as they are made. The eventual objective will be to use the frame-by-frame estimates to allow for feedback to a fielded system. Closely associated with this, NMSU developed a laboratory testbed to illuminate test objects, collect the received photons, and stream the data into RHINO. The two coupled efforts clearly demonstrate the feasibility of real-time pointing control of a laser system.

  10. Object localization based on smoothing preprocessing and cascade classifier

    Zhang, Xingfu; Liu, Lei; Zhao, Feng


    An improved algorithm for image location is proposed in this paper. Firstly, the image is smoothed and the partial noise is removed. Then use the cascade classifier to train a template. Finally, the template is used to detect the related images. The advantage of the algorithm is that it is robust to noise and the proportion of the image is not sensitive to change. At the same time, the algorithm also has the advantages of fast computation speed. In this paper, a real truck bottom picture is chosen as the experimental object. Images of normal components and faulty components are all included in the image sample. Experimental results show that the accuracy rate of the image is more than 90 percent when the grade is more than 40. So we can draw a conclusion that the algorithm proposed in this paper can be applied to the actual image localization project.

  11. Objective Motion Cueing Criteria Investigation Based on Three Flight Tasks

    Zaal, Petrus M. T.; Schroeder, Jeffery A.; Chung, William W.


    This paper intends to help establish fidelity criteria to accompany the simulator motion system diagnostic test specified by the International Civil Aviation Organization. Twelve air- line transport pilots flew three tasks in the NASA Vertical Motion Simulator under four different motion conditions. The experiment used three different hexapod motion configurations, each with a different tradeoff between motion filter gain and break frequency, and one large motion configuration that utilized as much of the simulator's motion space as possible. The motion condition significantly affected: 1) pilot motion fidelity ratings, and sink rate and lateral deviation at touchdown for the approach and landing task, 2) pilot motion fidelity ratings, roll deviations, maximum pitch rate, and number of stick shaker activations in the stall task, and 3) heading deviation after an engine failure in the takeoff task. Significant differences in pilot-vehicle performance were used to define initial objective motion cueing criteria boundaries. These initial fidelity boundaries show promise but need refinement.

  12. Digital Microdroplet Ejection Technology-Based Heterogeneous Objects Prototyping

    Na Li


    Full Text Available An integrate fabrication framework is presented to build heterogeneous objects (HEO using digital microdroplets injecting technology and rapid prototyping. The heterogeneous materials part design and manufacturing method in structure and material was used to change the traditional process. The net node method was used for digital modeling that can configure multimaterials in time. The relationship of material, color, and jetting nozzle was built. The main important contributions are to combine the structure, material, and visualization in one process and give the digital model for manufacture. From the given model, it is concluded that the method is effective for HEO. Using microdroplet rapid prototyping and the model given in the paper HEO could be gotten basically. The model could be used in 3D biomanufacturing.

  13. Video object's behavior analyzing based on motion history image and hidden markov model

    Meng Fanfeng; Qu Zhenshen; Zeng Qingshuang; Li li


    A novel method was proposed, which extracted video object's track and analyzed video object's behavior. Firstly, this method tracked the video object based on motion history image, and obtained the coordinate-based track sequence and orientation-based track sequence of the video object. Then the proposed hidden markov model (HMM) based algorithm was used to analyze the behavior of video object with the track sequence as input. Experimental results on traffic object show that this method can achieve the statistics of a mass of traffic objects' behavior efficiently, can acquire the reasonable velocity behavior curve of traffic object, and can recognize traffic object's various behaviors accurately. It provides a base for further research on video object behavior.

  14. Learning Object Metadata in a Web-Based Learning Environment

    Avgeriou, Paris; Koutoumanos, Anastasios; Retalis, Symeon; Papaspyrou, Nikolaos


    The plethora and variance of learning resources embedded in modern web-based learning environments require a mechanism to enable their structured administration. This goal can be achieved by defining metadata on them and constructing a system that manages the metadata in the context of the learning

  15. Robustifying Correspondence Based 6D Object Pose Estimation

    Hietanen, Antti; Halme, Jussi; Buch, Anders Glent;

    , is more general by making no assumptions about local surface properties. Our region pruning segments a model point cloud into cluster regions and searches good region combinations using a validation set. The robustifying methods are general and can be used with any correspondence based method...

  16. Identifying Objective EEG Based Markers of Linear Vection in Depth

    Palmisano, Stephen; Barry, Robert J.; De Blasio, Frances M.; Fogarty, Jack S.


    This proof-of-concept study investigated whether a time-frequency EEG approach could be used to examine vection (i.e., illusions of self-motion). In the main experiment, we compared the event-related spectral perturbation (ERSP) data of 10 observers during and directly after repeated exposures to two different types of optic flow display (each was 35° wide by 29° high and provided 20 s of motion stimulation). Displays consisted of either a vection display (which simulated constant velocity forward self-motion in depth) or a control display (a spatially scrambled version of the vection display). ERSP data were decomposed using time-frequency Principal Components Analysis (t–f PCA). We found an increase in 10 Hz alpha activity, peaking some 14 s after display motion commenced, which was positively associated with stronger vection ratings. This followed decreases in beta activity, and was also followed by a decrease in delta activity; these decreases in EEG amplitudes were negatively related to the intensity of the vection experience. After display motion ceased, a series of increases in the alpha band also correlated with vection intensity, and appear to reflect vection- and/or motion-aftereffects, as well as later cognitive preparation for reporting the strength of the vection experience. Overall, these findings provide support for the notion that EEG can be used to provide objective markers of changes in both vection status (i.e., “vection/no vection”) and vection strength. PMID:27559328

  17. Relational and Object-Oriented Methodology in Data Bases Systems

    Marian Pompiliu CRISTESCU


    Full Text Available Database programming languages integrate concepts of databases and programming languages to provide both implementation tools for data-intensive applications and high-level user interfaces to databases. Frequently, database programs contain a large amount of application knowledge which is hidden in the procedural code and thus difficult to maintain with changing data and user views. This paper presents a first attempt to improve the situation by supporting the integrated definition and management of data and rules based on a setoriented and predicative approach. The use of database technology for integrated fact and rule base management is shown to have some important advantages in terms of fact and rule integrity, question-answering, and explanation of results.

  18. Design of Objects Tracking System Based on ARM Embedded Platform

    XU Mei; SONG Yong-duan; LV Shao-dong; LIU Zhi-long; HUANG Cong-ying


    In recent years, according to the need of intelligent video surveillance system increasing rapidly in metropolitan cities ,a design based on S3C2440 microprocessor and embedded Linux operating system is adopted for real-time video target tracking. However, it is very challenging as embedded systems usually afford limited processing power and limited resources. Therefore, to address this problem, a real-time tracking algorithm using multi-features based on compressive sensing is proposed and implemented. The algorithm uses multiple matrix as the projection matrix of the compressive sensing and the compressed date as the multiple features to extract useful information needed by tracking process. Functions and libraries in OpenCV which were developed by Intel Corporation are utilized for building the tracking algorithms. It is tested with variant video sequences and the results show that the algorithm achieves stable tracking for the target moved of the light changed.

  19. Modeling Real Objects for Kansei-based Shape Retrieval

    Yukihiro Koda; Ichi Kanaya; Kosuke Sato


    A large number of 3D models are created on computers and available for networks. Some content-based retrieval technologies are indispensable to find out particular data from such anonymous datasets. Though several shape retrieval technologies have been developed, little attention has been given to the points on human's sense and impression (as known as Kansei) in the conventional techniques. In this paper, the authors propose a novel method of shape retrieval based on shape impression of human's Kansei. The key to the method is the Gaussian curvature distribution from 3D models as features for shape retrieval. Then it classifies the 3D models by extracted feature and measures similarity among models in storage.

  20. Object-based wavelet compression using coefficient selection

    Zhao, Lifeng; Kassim, Ashraf A.


    In this paper, we present a novel approach to code image regions of arbitrary shapes. The proposed algorithm combines a coefficient selection scheme with traditional wavelet compression for coding arbitrary regions and uses a shape adaptive embedded zerotree wavelet coding (SA-EZW) to quantize the selected coefficients. Since the shape information is implicitly encoded by the SA-EZW, our decoder can reconstruct the arbitrary region without separate shape coding. This makes the algorithm simple to implement and avoids the problem of contour coding. Our algorithm also provides a sufficient framework to address content-based scalability and improved coding efficiency as described by MPEG-4.

  1. Web-based visualization of spatial objects in 3DGIS

    ZHANG LiQiang; GUO ZhiFeng; KANG ZhiZhong; ZHANG LiXin; ZHANG XingMing; YANG Ling


    Adaptive rendering large and complex spatial data has become an important research issue In a 3DGIS application.In order to transmit the data to the client efficiently,this paper proposes a node-layer data model to manage the 3D scene.Because the large spatial data and limited network bandwidth are the main bottlenecks of web-based 3DGIS,a client/server architecture including progressive transmission methods and multiresolution representations,together with the spatial index,are developed to improve the performance.All this makes the application quite scalable.Experimental results reveal that the application works appropriately.

  2. Design of Object-based Information System Prototype

    Suhyeon Yoo


    Full Text Available Researchers who use science and technology information were found to ask an information service in which they can excerpt the contents they needed, rather than using the information at article level. In this study, we micronized the contents of scholarly articles into text, image, and table and then constructed a micro-content DB to design a new information system prototype based on this micro-content. After designing the prototype, we performed usability test for this prototype so as to confirm the usefulness of the system prototype. We expect that the outcome of this study will fulfill the segmented and diversified information need of researchers.

  3. Underground object characterization based on neural networks for ground penetrating radar data

    Zhang, Yu; Huston, Dryver; Xia, Tian


    In this paper, an object characterization method based on neural networks is developed for GPR subsurface imaging. Currently, most existing studies demonstrate detecting and imaging objects of cylindrical shapes. While in this paper, no restriction is imposed on the object shape. Three neural network algorithms are exploited to characterize different types of object signatures, including object shape, object material, object size, object depth and subsurface medium's dielectric constant. Feature extraction is performed to characterize the instantaneous amplitude and time delay of the reflection signal from the object. The characterization method is evaluated utilizing the data synthesized with the finite-difference timedomain (FDTD) simulator.

  4. From neural-based object recognition toward microelectronic eyes

    Sheu, Bing J.; Bang, Sa Hyun


    Engineering neural network systems are best known for their abilities to adapt to the changing characteristics of the surrounding environment by adjusting system parameter values during the learning process. Rapid advances in analog current-mode design techniques have made possible the implementation of major neural network functions in custom VLSI chips. An electrically programmable analog synapse cell with large dynamic range can be realized in a compact silicon area. New designs of the synapse cells, neurons, and analog processor are presented. A synapse cell based on Gilbert multiplier structure can perform the linear multiplication for back-propagation networks. A double differential-pair synapse cell can perform the Gaussian function for radial-basis network. The synapse cells can be biased in the strong inversion region for high-speed operation or biased in the subthreshold region for low-power operation. The voltage gain of the sigmoid-function neurons is externally adjustable which greatly facilitates the search of optimal solutions in certain networks. Various building blocks can be intelligently connected to form useful industrial applications. Efficient data communication is a key system-level design issue for large-scale networks. We also present analog neural processors based on perceptron architecture and Hopfield network for communication applications. Biologically inspired neural networks have played an important role towards the creation of powerful intelligent machines. Accuracy, limitations, and prospects of analog current-mode design of the biologically inspired vision processing chips and cellular neural network chips are key design issues.

  5. Multi-objective optimization of process based on resource capability


    To improve the practicability, suitability and accuracy of the trade-off among time, cost and quality of a process, a method based on resource capability is introduced. Through analyzing the relationship between an activity and its' supporting resource, the model trades off the time, cost and quality by changing intensity of labor or changing the types of supporting resource or units of labor of resource in a certain time respectively according to the different types of its' supporting resources. Through contrasting this method with the model of unit time cost corresponding to different quality levels and inter-related linear programming model of time, cost and quality for process optimizing, it is shown that this model does not only cover the above two models but also can describe some conditions the above two models can not express. The method supports to select different function to optimize a process according to different types of its supporting resource.

  6. Mass Measurements of Isolated Objects from Space-based Microlensing

    Zhu, Wei; Gould, A; Udalski, A; Han, C; Shvartzvald, Y; Ranc, C; Jorgensen, U G; Poleski, R; Bozza, V; Beichman, C; Bryden, G; Carey, S; Gaudi, B S; Henderson, C B; Pogge, R W; Porritt, I; Wibking, B; Yee, J C; Pawlak, M; Szymanski, M K; Skowron, J; Mroz, P; Kozlowski, S; Wyrzykowski, L; Pietrukowicz, P; Pietrzynski, G; Soszynski, I; Ulaczyk, K; Choi, J Y; Park, H; Jung, Y K; Shin, I -G; Albrow, M D; Park, B -G; Kim, S -L; Lee, C -U; Kim, D -J; Lee, Y; Friedmann, M; Kaspi, S; Maoz, D; Hundertmark, M; Street, R A; Tsapras, Y; Bramich, D M; Cassan, A; Dominik, M; Bachelet, E; Dong, Subo; Jaimes, R Figuera; Horne, K; Mao, S; Menzies, J; Schmidt, R; Snodgrass, C; Steele, I A; Wambsganss, J; Skottfelt, J; Andersen, M I; Burgdorf, M J; Ciceri, S; D'Ago, G; Evans, D F; Gu, S -H; Hinse, T C; Kerins, E; Korhonen, H; Kuffmeier, M; Mancini, L; Peixinho, N; popovas, A; Rabus, M; Rahvar, S; Rasmussen, R T; Scarpetta, G; Southworth, J; Surdej, J; von Essen, C; Wang, Y -B; Wertz, O


    We report on the mass and distance measurements of two single-lens events from the 2015 \\emph{Spitzer} microlensing campaign. With both finite-source effect and microlens parallax measurements, we find that the lens of OGLE-2015-BLG-1268 is a $47\\pm7$ $M_{\\rm J}$ brown dwarf at $5.4\\pm1.0$ kpc, and that the lens of OGLE-2015-BLG-0763 is a $0.50\\pm0.04$ $M_\\odot$ star at $6.9\\pm1.0$ kpc. We show that the probability to definitively measure the mass of isolated microlenses, including isolated stellar mass black holes and free floating planets, is dramatically increased once simultaneous ground- and space-based observations are conducted.

  7. Actin-based propulsion of spatially extended objects

    Enculescu, Mihaela [Institute for Theoretical Physics, Technische Universitaet Berlin, Hardenbergstrasse 36, 10623 Berlin (Germany); Falcke, Martin, E-mail: [Max-Delbrueck-Center for Molecular Medicine, Mathematical Cell Physiology, Robert-Roessle-Street 10, 13125 Berlin (Germany)


    We propose a mathematical model of the actin-based propulsion of spatially extended obstacles. It starts from the properties of individual actin filaments and includes transient attachment to the obstacle, polymerization as well as cross-linking. Two particular geometries are discussed, which apply to the motion of protein-coated beads in a cell-like medium and the leading edge of a cell protrusion, respectively. The model gives rise to both steady and saltatory movement of beads and can explain the experimentally observed transitions of the dynamic regime with changing bead radius and protein surface density. Several spatiotemporal patterns are obtained with a soft obstacle under tension, including the experimentally observed spontaneous emergence of lateral traveling waves in crawling cells. Thus, we suggest a unifying mechanism for systems that are currently described by differential concepts.

  8. Mass Measurements of Isolated Objects from Space-based Microlensing

    Zhu, Wei; Calchi Novati, S.; Gould, A.; Udalski, A.; Han, C.; Shvartzvald, Y.; Ranc, C.; Jørgensen, U. G.; Poleski, R.; Bozza, V.; Beichman, C.; Bryden, G.; Carey, S.; Gaudi, B. S.; Henderson, C. B.; Pogge, R. W.; Porritt, I.; Wibking, B.; Yee, J. C.; SPITZER Team; Pawlak, M.; Szymański, M. K.; Skowron, J.; Mróz, P.; Kozłowski, S.; Wyrzykowski, Ł.; Pietrukowicz, P.; Pietrzyński, G.; Soszyński, I.; Ulaczyk, K.; OGLE Group; Choi, J.-Y.; Park, H.; Jung, Y. K.; Shin, I.-G.; Albrow, M. D.; Park, B.-G.; Kim, S.-L.; Lee, C.-U.; Cha, S.-M.; Kim, D.-J.; Lee, Y.; KMTNET Group; Friedmann, M.; Kaspi, S.; Maoz, D.; WISE Group; Hundertmark, M.; Street, R. A.; Tsapras, Y.; Bramich, D. M.; Cassan, A.; Dominik, M.; Bachelet, E.; Dong, Subo; Figuera Jaimes, R.; Horne, K.; Mao, S.; Menzies, J.; Schmidt, R.; Snodgrass, C.; Steele, I. A.; Wambsganss, J.; RoboNeT Team; Skottfelt, J.; Andersen, M. I.; Burgdorf, M. J.; Ciceri, S.; D'Ago, G.; Evans, D. F.; Gu, S.-H.; Hinse, T. C.; Kerins, E.; Korhonen, H.; Kuffmeier, M.; Mancini, L.; Peixinho, N.; Popovas, A.; Rabus, M.; Rahvar, S.; Tronsgaard, R.; Scarpetta, G.; Southworth, J.; Surdej, J.; von Essen, C.; Wang, Y.-B.; Wertz, O.; MiNDSTEP Group


    We report on the mass and distance measurements of two single-lens events from the 2015 Spitzer microlensing campaign. With both finite-source effect and microlens parallax measurements, we find that the lens of OGLE-2015-BLG-1268 is very likely a brown dwarf (BD). Assuming that the source star lies behind the same amount of dust as the Bulge red clump, we find the lens is a 45 ± 7 {M}{{J}} BD at 5.9 ± 1.0 kpc. The lens of of the second event, OGLE-2015-BLG-0763, is a 0.50 ± 0.04 {M}⊙ star at 6.9 ± 1.0 kpc. We show that the probability to definitively measure the mass of isolated microlenses is dramatically increased once simultaneous ground- and space-based observations are conducted.

  9. Proto-object based rate control for JPEG2000: an approach to content-based scalability.

    Xue, Jianru; Li, Ce; Zheng, Nanning


    The JPEG2000 system provides scalability with respect to quality, resolution and color component in the transfer of images. However, scalability with respect to semantic content is still lacking. We propose a biologically plausible salient region based bit allocation mechanism within the JPEG2000 codec for the purpose of augmenting scalability with respect to semantic content. First, an input image is segmented into several salient proto-objects (a region that possibly contains a semantically meaningful physical object) and background regions (a region that contains no object of interest) by modeling visual focus of attention on salient proto-objects. Then, a novel rate control scheme distributes a target bit rate to each individual region according to its saliency, and constructs quality layers of proto-objects for the purpose of more precise truncation comparable to original quality layers in the standard. Empirical results show that the suggested approach adds to the JPEG2000 system scalability with respect to content as well as the functionality of selectively encoding, decoding, and manipulation of each individual proto-object in the image, with only some slightly trivial modifications to the JPEG2000 standard. Furthermore, the proposed rate control approach efficiently reduces the computational complexity and memory usage, as well as maintains the high quality of the image to a level comparable to the conventional post-compression rate distortion (PCRD) optimum truncation algorithm for JPEG2000.

  10. Spectroscopic Assessment of WISE-based Young Stellar Object Selection

    Koenig, Xavier; Padgett, Deborah; DeFelippis, Daniel


    We have conducted a sensitive search down to the hydrogen burning limit for unextincted stars over $\\sim$200 square degrees around Lambda Orionis and 20 square degrees around Sigma Orionis using the methodology of Koenig & Leisawitz (2014). From WISE and 2MASS data we identify 544 and 418 candidate YSOs in the vicinity of Lambda and Sigma respectively. Based on our followup spectroscopy for some candidates and the existing literature for others, we found that $\\sim$80% of the K14-selected candidates are probable or likely members of the Orion star forming region. The yield from the photometric selection criteria shows that WISE sources with $K_S -w3 > 1.5$ mag and $K_S $ between 10--12 mag are most likely to show spectroscopic signs of youth, while WISE sources with $K_S -w3 > 4$ mag and $K_S > 12$ were often AGNs when followed up spectroscopically. The population of candidate YSOs traces known areas of active star formation, with a few new `hot spots' of activity near Lynds 1588 and 1589 and a more dispe...

  11. Surface investigation of naturally corroded gilded copper-based objects

    Ingo, G. M.; Riccucci, C.; Lavorgna, M.; Salzano de Luna, M.; Pascucci, M.; Di Carlo, G.


    Gold and silver coated copper-based artefacts subjected to long-term natural corrosion phenomena were studied by means of the combined use of X-ray photoelectron spectroscopy (XPS), scanning electron microscopy coupled with energy dispersive X-ray spectroscopy (SEM + EDS), and optical microscopy (OM). The results allowed the identification of the chemistry and structure of the Au or Ag layers deposited by fire-gilding or mercury-silvering and the determination of the corrosion products formed due to interaction with the surrounding environment. Different degradation phenomena of the noble metal layer and copper substrate are induced by the presence of chlorine, sulphur and phosphorous and they are boosted by the metal galvanic coupling which makes gilded-metal art works unstable from a chemico-physical point of view. The SEM + EDS and OM results also suggest that particular care must be used during the removal of the encrustations and of the external corrosion products to avoid the loss of the remains of the noble layer often floating or embedded in the corrosion products. Furthermore, in order to avoid the reaction between nantokite (CuCl) and moisture the use no or low toxic inhibitors is suggested to avoid further severe degradation phenomena enhancing the long-lasting chemico-physical stability of these precious artefacts and giving them a greater chance of survival.

  12. Attentional spreading to task-irrelevant object features: experimental support and a 3-step model of attention for object-based selection and feature-based processing modulation.

    Wegener, Detlef; Galashan, Fingal Orlando; Aurich, Maike Kathrin; Kreiter, Andreas Kurt


    Directing attention to a specific feature of an object has been linked to different forms of attentional modulation. Object-based attention theory founds on the finding that even task-irrelevant features at the selected object are subject to attentional modulation, while feature-based attention theory proposes a global processing benefit for the selected feature even at other objects. Most studies investigated either the one or the other form of attention, leaving open the possibility that both object- and feature-specific attentional effects do occur at the same time and may just represent two sides of a single attention system. We here investigate this issue by testing attentional spreading within and across objects, using reaction time (RT) measurements to changes of attended and unattended features on both attended and unattended objects. We asked subjects to report color and speed changes occurring on one of two overlapping random dot patterns (RDPs), presented at the center of gaze. The key property of the stimulation was that only one of the features (e.g., motion direction) was unique for each object, whereas the other feature (e.g., color) was shared by both. The results of two experiments show that co-selection of unattended features even occurs when those features have no means for selecting the object. At the same time, they demonstrate that this processing benefit is not restricted to the selected object but spreads to the task-irrelevant one. We conceptualize these findings by a 3-step model of attention that assumes a task-dependent top-down gain, object-specific feature selection based on task- and binding characteristics, and a global feature-specific processing enhancement. The model allows for the unification of a vast amount of experimental results into a single model, and makes various experimentally testable predictions for the interaction of object- and feature-specific processes.

  13. Attentional spreading to task-irrelevant object features: Experimental support and a 3-step model of attention for object-based selection and feature-based processing modulation

    Detlef eWegener


    Full Text Available Directing attention to a specific feature of an object has been linked to different forms of attentional modulation. Object-based attention theory founds on the finding that even task-irrelevant features at the selected object are subject to attentional modulation, while feature-based attention theory proposes a global processing benefit for the selected feature even at other objects. Most studies investigated either the one or the other form of attention, leaving open the possibility that both object- and feature-specific attentional effects do occur at the same time and may just represent two sides of a single attention system. We here investigate this issue by testing attentional spreading within and across objects, using reaction time measurements to changes of attended and unattended features on both attended and unattended objects. We asked subjects to report color and speed changes occurring on one of two overlapping random dot patterns, presented at the center of gaze. The key property of the stimulation was that only one of the features (e.g. motion direction was unique for each object, whereas the other feature (e.g. color was shared by both. The results of two experiments show that co-selection of unattended features even occurs when those features have no means for selecting the object. At the same time, they demonstrate that this processing benefit is not restricted to the selected object but spreads to the task-irrelevant one. We conceptualize these findings by a 3-step model of attention that assumes a task-dependent top-down gain, object-specific feature selection based on task- and binding characteristics, and a global feature-specific processing enhancement. The model allows for the unification of a vast amount of experimental results into a single model, and makes various experimentally testable predictions for the interaction of object- and feature-specific processes.


    Decombas, Marc; Dufaux, Frederic; Renan, Erwann; Pesquet-Popescu, Beatrice; Capman, Francois


    ICIP2012; We propose a full reference visual quality metric to evaluate a semantic coding system which may not preserve exactly the position and/or the shape of objects. The metric is based on Scale-Invariant Feature Transform (SIFT) points. More specifically, Structural SIMilarity (SSIM) on windows around the SIFT points measures the compression artifacts (SSIM_SIFT). Conversely, the standard deviation of the matching distance between the SIFT points measures the geometric distortion (GEOMET...

  15. SVM Classifiers: The Objects Identification on the Base of Their Hyperspectral Features

    Demidova Liliya


    Full Text Available The problem of the objects identification on the base of their hyperspectral features has been considered. It is offered to use the SVM classifiers on the base of the modified PSO algorithm, adapted to specifics of the problem of the objects identification on the base of their hyperspectral features. The results of the objects identification on the base of their hyperspectral features with using of the SVM classifiers have been presented.

  16. Image Mining in Remote Sensing for Coastal Wetlands Mapping: from Pixel Based to Object Based Approach

    Farda, N. M.; Danoedoro, P.; Hartono; Harjoko, A.


    The availably of remote sensing image data is numerous now, and with a large amount of data it makes “knowledge gap” in extraction of selected information, especially coastal wetlands. Coastal wetlands provide ecosystem services essential to people and the environment. The aim of this research is to extract coastal wetlands information from satellite data using pixel based and object based image mining approach. Landsat MSS, Landsat 5 TM, Landsat 7 ETM+, and Landsat 8 OLI images located in Segara Anakan lagoon are selected to represent data at various multi temporal images. The input for image mining are visible and near infrared bands, PCA band, invers PCA bands, mean shift segmentation bands, bare soil index, vegetation index, wetness index, elevation from SRTM and ASTER GDEM, and GLCM (Harralick) or variability texture. There is three methods were applied to extract coastal wetlands using image mining: pixel based - Decision Tree C4.5, pixel based - Back Propagation Neural Network, and object based - Mean Shift segmentation and Decision Tree C4.5. The results show that remote sensing image mining can be used to map coastal wetlands ecosystem. Decision Tree C4.5 can be mapped with highest accuracy (0.75 overall kappa). The availability of remote sensing image mining for mapping coastal wetlands is very important to provide better understanding about their spatiotemporal coastal wetlands dynamics distribution.

  17. A Dynamic Object Behavior Model and Implementation Based on Computational Reflection

    HE Cheng-wan; HE Fei; HE Ke-qing


    A dynamic object behavior model based on computational reflection is proposed. This model consists of function level and meta level, the meta objects in meta level manage the base objects and behaviors in function level, including dynamic binding and unbinding of base object and behavior.We implement this model with RoleJava Language, which is our self linguistic extension of the Java Language. Meta Objects are generated automatically at compile-time, this makes the reflecton mechanism transparent to programmers. Finally an example applying this model to a banking system is presented.

  18. A Machine Learning based Efficient Software Reusability Prediction Model for Java Based Object Oriented Software

    Surbhi Maggo


    Full Text Available Software reuse refers to the development of new software systems with the likelihood of completely or partially using existing components or resources with or without modification. Reusability is the measure of the ease with which previously acquired concepts and objects can be used in new contexts. It is a promising strategy for improvements in software quality, productivity and maintainability as it provides for cost effective, reliable (with the consideration that prior testing and use has eliminated bugs and accelerated (reduced time to market development of the software products. In this paper we present an efficient automation model for the identification and evaluation of reusable software components to measure the reusability levels (high, medium or low of procedure oriented Java based (object oriented software systems. The presented model uses a metric framework for the functional analysis of the Object oriented software components that target essential attributes of reusability analysis also taking into consideration Maintainability Index to account for partial reuse. Further machine learning algorithm LMNN is explored to establish relationships between the functional attributes. The model works at functional level rather than at structural level. The system is implemented as a tool in Java and the performance of the automation tool developed is recorded using criteria like precision, recall, accuracy and error rate. The results gathered indicate that the model can be effectively used as an efficient, accurate, fast and economic model for the identification of procedure based reusable components from the existing inventory of software resources.

  19. Novel 3-D Object Recognition Methodology Employing a Curvature-Based Histogram

    Liang-Chia Chen


    Full Text Available In this paper, a new object recognition algorithm employing a curvature-based histogram is presented. Recognition of three-dimensional (3-D objects using range images remains one of the most challenging problems in 3-D computer vision due to its noisy and cluttered scene characteristics. The key breakthroughs for this problem mainly lie in defining unique features that distinguish the similarity among various 3-D objects. In our approach, an object detection scheme is developed to identify targets underlining an automated search in the range images using an initial process of object segmentation to subdivide all possible objects in the scenes and then applying a process of object recognition based on geometric constraints and a curvature-based histogram for object recognition. The developed method has been verified through experimental tests for its feasibility confirmation.

  20. Adaptive surrogate model based multi-objective transfer trajectory optimization between different libration points

    Peng, Haijun; Wang, Wei


    An adaptive surrogate model-based multi-objective optimization strategy that combines the benefits of invariant manifolds and low-thrust control toward developing a low-computational-cost transfer trajectory between libration orbits around the L1 and L2 libration points in the Sun-Earth system has been proposed in this paper. A new structure for a multi-objective transfer trajectory optimization model that divides the transfer trajectory into several segments and gives the dominations for invariant manifolds and low-thrust control in different segments has been established. To reduce the computational cost of multi-objective transfer trajectory optimization, a mixed sampling strategy-based adaptive surrogate model has been proposed. Numerical simulations show that the results obtained from the adaptive surrogate-based multi-objective optimization are in agreement with the results obtained using direct multi-objective optimization methods, and the computational workload of the adaptive surrogate-based multi-objective optimization is only approximately 10% of that of direct multi-objective optimization. Furthermore, the generating efficiency of the Pareto points of the adaptive surrogate-based multi-objective optimization is approximately 8 times that of the direct multi-objective optimization. Therefore, the proposed adaptive surrogate-based multi-objective optimization provides obvious advantages over direct multi-objective optimization methods.

  1. Ontology-Based Retrieval of Spatially Related Objects for Location Based Services

    Haav, Hele-Mai; Kaljuvee, Aivi; Luts, Martin; Vajakas, Toivo

    Advanced Location Based Service (LBS) applications have to integrate information stored in GIS, information about users' preferences (profile) as well as contextual information and information about application itself. Ontology engineering provides methods to semantically integrate several data sources. We propose an ontology-driven LBS development framework: the paper describes the architecture of ontologies and their usage for retrieval of spatially related objects relevant to the user. Our main contribution is to enable personalised ontology driven LBS by providing a novel approach for defining personalised semantic spatial relationships by means of ontologies. The approach is illustrated by an industrial case study.

  2. A Dynamic Feature-Based Method for Hybrid Blurred/Multiple Object Detection in Manufacturing Processes

    Tsun-Kuo Lin


    Full Text Available Vision-based inspection has been applied for quality control and product sorting in manufacturing processes. Blurred or multiple objects are common causes of poor performance in conventional vision-based inspection systems. Detecting hybrid blurred/multiple objects has long been a challenge in manufacturing. For example, single-feature-based algorithms might fail to exactly extract features when concurrently detecting hybrid blurred/multiple objects. Therefore, to resolve this problem, this study proposes a novel vision-based inspection algorithm that entails selecting a dynamic feature-based method on the basis of a multiclassifier of support vector machines (SVMs for inspecting hybrid blurred/multiple object images. The proposed algorithm dynamically selects suitable inspection schemes for classifying the hybrid images. The inspection schemes include discrete wavelet transform, spherical wavelet transform, moment invariants, and edge-feature-descriptor-based classification methods. The classification methods for single and multiple objects are adaptive region growing- (ARG- based and local adaptive region growing- (LARG- based learning approaches, respectively. The experimental results demonstrate that the proposed algorithm can dynamically select suitable inspection schemes by applying a selection algorithm, which uses SVMs for classifying hybrid blurred/multiple object samples. Moreover, the method applies suitable feature-based schemes on the basis of the classification results for employing the ARG/LARG-based method to inspect the hybrid objects. The method improves conventional methods for inspecting hybrid blurred/multiple objects and achieves high recognition rates for that in manufacturing processes.

  3. Sub-OBB based object recognition and localization algorithm using range images

    Hoang, Dinh-Cuong; Chen, Liang-Chia; Nguyen, Thanh-Hung


    This paper presents a novel approach to recognize and estimate pose of the 3D objects in cluttered range images. The key technical breakthrough of the developed approach can enable robust object recognition and localization under undesirable condition such as environmental illumination variation as well as optical occlusion to viewing the object partially. First, the acquired point clouds are segmented into individual object point clouds based on the developed 3D object segmentation for randomly stacked objects. Second, an efficient shape-matching algorithm called Sub-OBB based object recognition by using the proposed oriented bounding box (OBB) regional area-based descriptor is performed to reliably recognize the object. Then, the 3D position and orientation of the object can be roughly estimated by aligning the OBB of segmented object point cloud with OBB of matched point cloud in a database generated from CAD model and 3D virtual camera. To detect accurate pose of the object, the iterative closest point (ICP) algorithm is used to match the object model with the segmented point clouds. From the feasibility test of several scenarios, the developed approach is verified to be feasible for object pose recognition and localization.

  4. 基于Object-Z的UML对象模型的形式化%The Formalization of Object Model in UML Based on Object-Z

    杨卫东; 蔡希尧


    UML is the main visual Object-oriented modeling language currently, which is used widely and supported by most CASE tools. Comparing with traditional Object-oriented methods, LML describes its semantics and syntax more rigouly by using metamodel and Object Constrain Language. But some important concepts in UML are not specified clearly. This paper presents a formal specification for object model of UML, mainly includes the concepts of class, association, association class, aggregation, and inheritance, etc, so that the analyse, verification, refine, and consistent cheking can be applied to object model.

  5. Mean shift-based object tracking in FLIR imagery using multiple features

    Yang, Wei; Li, Junshan; Shi, Deqin; Cheng, Wen


    A novel object tracking algorithm for FLIR imagery based on mean shift using multiple features is proposed to improve the tracking performance. First, the appearance model of infrared object is represented in the combination of gray space, LBP texture space, and orientation space with different feature weight. And then, the mean shift algorithm is employed to find the object location. An on-line feature weight update mechanism is developed based on Fisher criteria, which measure the discrimination of object and background effectively. Experiment results demonstrate the effectiveness and robustness of the proposed method for object tracking in FLIR imagery.

  6. Concealed objects detection based on FWT in active millimeter-wave images

    Du, Kun; Zhang, Lu; Chen, Wei; Wan, Guolong; Fu, Ruoran


    Active millimeter-wave (MMW) near-filed human imaging is a means for concealed objects detection. A method of concealed objects detection based on fast wavelet transforms (FWT) in the usage of active MMW images is presented as a result of image characteristics, which includes high resolution, characteristics varying in different parts of the human, imaging influenced among human, concealed objects and other objects, and different textures of concealed objects. Images segmentation utilizing results of edge detection based on FWT is conducted and preliminary segmentation results can be obtained. Some kinds of concealed objects according to comparing gray value of concealed objects to human average gray value can be detected in this paper. The experiments of concealed objects on images of actual acquisition are conducted with a result of accurate rate 80.92% and false alarm rate 11.78%, illustrating the effectiveness of the method proposed in this paper.

  7. Spatial Object Aggregation Based on Data Structure,Local Triangulation and Hierarchical Analyzing Method


    This paper focuses on the methods and process of spatial aggregation based on semantic and geometric characteristics of spatial objects and relations among the objects with the help of spatial data structure (Formal Data Structure),the Local Constrained Delaunay Triangulations and semantic hierarchy.The adjacent relation among connected objects and unconnected objects has been studied through constrained triangle as elementary processing unit in aggregation operation.The hierarchical semantic analytical matrix is given for analyzing the similarity between objects types and between objects.Several different cases of aggregation have been presented in this paper.

  8. Attribute and topology based change detection in a constellation of previously detected objects

    Paglieroni, David W.; Beer, Reginald N.


    A system that applies attribute and topology based change detection to networks of objects that were detected on previous scans of a structure, roadway, or area of interest. The attributes capture properties or characteristics of the previously detected objects, such as location, time of detection, size, elongation, orientation, etc. The topology of the network of previously detected objects is maintained in a constellation database that stores attributes of previously detected objects and implicitly captures the geometrical structure of the network. A change detection system detects change by comparing the attributes and topology of new objects detected on the latest scan to the constellation database of previously detected objects.

  9. Point pattern match-based change detection in a constellation of previously detected objects

    Paglieroni, David W.


    A method and system is provided that applies attribute- and topology-based change detection to objects that were detected on previous scans of a medium. The attributes capture properties or characteristics of the previously detected objects, such as location, time of detection, detection strength, size, elongation, orientation, etc. The locations define a three-dimensional network topology forming a constellation of previously detected objects. The change detection system stores attributes of the previously detected objects in a constellation database. The change detection system detects changes by comparing the attributes and topological consistency of newly detected objects encountered during a new scan of the medium to previously detected objects in the constellation database. The change detection system may receive the attributes of the newly detected objects as the objects are detected by an object detection system in real time.

  10. Object-Based Image Analysis Beyond Remote Sensing - the Human Perspective

    Blaschke, T.; Lang, S.; Tiede, D.; Papadakis, M.; Györi, A.


    We introduce a prototypical methodological framework for a place-based GIS-RS system for the spatial delineation of place while incorporating spatial analysis and mapping techniques using methods from different fields such as environmental psychology, geography, and computer science. The methodological lynchpin for this to happen - when aiming to delineate place in terms of objects - is object-based image analysis (OBIA).

  11. Change detection of built-up land: A framework of combining pixel-based detection and object-based recognition

    Xiao, Pengfeng; Zhang, Xueliang; Wang, Dongguang; Yuan, Min; Feng, Xuezhi; Kelly, Maggi


    This study proposed a new framework that combines pixel-level change detection and object-level recognition to detect changes of built-up land from high-spatial resolution remote sensing images. First, an adaptive differencing method was designed to detect changes at the pixel level based on both spectral and textural features. Next, the changed pixels were subjected to a set of morphological operations to improve the completeness and to generate changed objects, achieving the transition of change detection from the pixel level to the object level. The changed objects were further recognised through the difference of morphological building index in two phases to indicate changed objects on built-up land. The transformation from changed pixels to changed objects makes the proposed framework distinct with both the pixel-based and the object-based change detection methods. Compared with the pixel-based methods, the proposed framework can improve the change detection capability through the transformation and successive recognition of objects. Compared with the object-based method, the proposed framework avoids the issue of multitemporal segmentation and can generate changed objects directly from changed pixels. The experimental results show the effectiveness of the transformation from changed pixels to changed objects and the successive object-based recognition on improving the detection accuracy, which justify the application potential of the proposed change detection framework.

  12. Ground-Based Deep-Space Ladar for Satellite Detection: A Parametric Study


    306-309 (July/August 1985). 19. Degnan, J. J. and Klein, B. J. - Optical Antenna Gain. 2: Receiving Antennas," Applied Optics, 13: 2397-2401 (October...Degnan, J. J. " Optical Antenna Gain. 1: Transmitting Anten- nas," Applied Optics, 13: 2134-2141 (September 1974). 44. -. " Optical Antenna Gain. 3

  13. Introducing AN Agent-Based Object Recognition Operator for Proximity Analysis

    Behzadi, S.; Ali. Alesheikh, A.


    Object selection is a basic procedure in a Geographic Information System (GIS). Most current methods for doing so, select objects in two phases: create a simple distance-bounded geometric buffer; and intersect it with available features. This paper introduces a novel and intelligent selection operator based on the autonomy of the agent-based approach. The proposed operator recognizes objects around one object only in one step. In the proposed approach, each point object acts as an agent-automata object. It then senses its vicinity and identifies the surrounding objects. To assess the proposed model, the operator is designed, implemented, and evaluated in a case study. Finally, the results are evaluated and presented in details in the paper.

  14. Retrieving top-k prestige-based relevant spatial web objects

    Cao, Xin; Cong, Gao; Jensen, Christian S.


    The location-aware keyword query returns ranked objects that are near a query location and that have textual descriptions that match query keywords. This query occurs inherently in many types of mobile and traditional web services and applications, e.g., Yellow Pages and Maps services. Previous...... of prestige-based relevance to capture both the textual relevance of an object to a query and the effects of nearby objects. Based on this, a new type of query, the Location-aware top-k Prestige-based Text retrieval (LkPT) query, is proposed that retrieves the top-k spatial web objects ranked according...... to both prestige-based relevance and location proximity. We propose two algorithms that compute LkPT queries. Empirical studies with real-world spatial data demonstrate that LkPT queries are more effective in retrieving web objects than a previous approach that does not consider the effects of nearby...

  15. Object Classification based Context Management for Identity Management in Internet of Things

    Mahalle, Parikshit N.; Prasad, Neeli R.; Prasad, Ramjee


    , and there is a need of context-aware access control solution for IdM. Confronting uncertainty of different types of objects in IoT is not easy. This paper presents the logical framework for object classification in context aware IoT, as richer contextual information creates an impact on the access control. This paper...... proposes decision theory based object classification to provide contextual information and context management. Simulation results show that the proposed object classification is useful to improve network lifetime. Results also give motivation of object classification in terms of energy consumption...

  16. Model of Recommendation System for for Indexing and Retrieving the Learning Object based on Multiagent System

    Ronaldo Lima Rocha Campos


    Full Text Available This paper proposes a multiagent system application model for indexing, retrieving and recommendation learning objects stored in different and heterogeneous repositories. The objects within these repositories are described by filled fields using different metadata standards. The searching mechanism covers several different learning object repositories and the same object can be described in these repositories by the use of different types of fields. Aiming to improve accuracy and coverage in terms of recovering a learning object and improve the signification of the results we propose an information retrieval model based on the multiagent system approach and an ontological model to describe the knowledge domain covered.


    A. N. Grigoriev


    Full Text Available Subject of Research. Research findings of the specific application of space-based optical-electronic and radar means for the Earth remote sensing are considered. The subject matter of the study is the current planning of objects survey on the underlying surface in order to increase the effectiveness of sensing system due to the rational use of its resources. Method. New concept of a group object, stochastic swath and stochastic length of the route is introduced. The overview of models for single, group objects and their parameters is given. The criterion for the existence of the group object based on two single objects is formulated. The method for group objects formation while current survey planning has been developed and its description is presented. The method comprises several processing stages for data about objects with the calculation of new parameters, the stochastic characteristics of space means and validates the spatial size of the object value of the stochastic swath and stochastic length of the route. The strict mathematical description of techniques for model creation of a group object based on data about a single object and onboard special complex facilities in difficult conditions of registration of spatial data is given. Main Results. The developed method is implemented on the basis of modern geographic information system in the form of a software tool layout with advanced tools of processing and analysis of spatial data in vector format. Experimental studies of the forming method for the group of objects were carried out on a different real object environment using the parameters of modern national systems of the Earth remote sensing detailed observation Canopus-B and Resurs-P. Practical Relevance. The proposed models and method are focused on practical implementation using vector spatial data models and modern geoinformation technologies. Practical value lies in the reduction in the amount of consumable resources by means of

  18. Object-based classification of earthquake damage from high-resolution optical imagery using machine learning

    Bialas, James; Oommen, Thomas; Rebbapragada, Umaa; Levin, Eugene


    Object-based approaches in the segmentation and classification of remotely sensed images yield more promising results compared to pixel-based approaches. However, the development of an object-based approach presents challenges in terms of algorithm selection and parameter tuning. Subjective methods are often used, but yield less than optimal results. Objective methods are warranted, especially for rapid deployment in time-sensitive applications, such as earthquake damage assessment. Herein, we used a systematic approach in evaluating object-based image segmentation and machine learning algorithms for the classification of earthquake damage in remotely sensed imagery. We tested a variety of algorithms and parameters on post-event aerial imagery for the 2011 earthquake in Christchurch, New Zealand. Results were compared against manually selected test cases representing different classes. In doing so, we can evaluate the effectiveness of the segmentation and classification of different classes and compare different levels of multistep image segmentations. Our classifier is compared against recent pixel-based and object-based classification studies for postevent imagery of earthquake damage. Our results show an improvement against both pixel-based and object-based methods for classifying earthquake damage in high resolution, post-event imagery.

  19. The Study of Object-Oriented Motor Imagery Based on EEG Suppression


    Motor imagery is a conventional method for brain computer interface and motor learning. To avoid the great individual difference of the motor imagery ability, object-oriented motor imagery was applied, and the effects were studied. Kinesthetic motor imagery and visual observation were administered to 15 healthy volunteers. The EEG during cue-based simple imagery (SI), object-oriented motor imagery (OI), non-object-oriented motor imagery (NI) and visual observation (VO) was recorded. Study res...

  20. Object Detection Based on Template Matching through Use of Best-So-Far ABC

    Anan Banharnsakun


    Full Text Available Best-so-far ABC is a modified version of the artificial bee colony (ABC algorithm used for optimization tasks. This algorithm is one of the swarm intelligence (SI algorithms proposed in recent literature, in which the results demonstrated that the best-so-far ABC can produce higher quality solutions with faster convergence than either the ordinary ABC or the current state-of-the-art ABC-based algorithm. In this work, we aim to apply the best-so-far ABC-based approach for object detection based on template matching by using the difference between the RGB level histograms corresponding to the target object and the template object as the objective function. Results confirm that the proposed method was successful in both detecting objects and optimizing the time used to reach the solution.



    The new MPEG-4 video coding standard enables content-based functions. In order to support the new standard, frames should be decomposed into Video Object Planes (VOP),each VOP representing a moving object. This paper proposes an image segmentation method to separate moving objects from image sequences. The proposed method utilizes the spatial-temporal information. Spatial segmentation is applied to divide each image into connected areas and to find precise object boundaries of moving objects. To locate moving objects in image sequences,two consecutive image frames in the temporal direction are examined and a hypothesis testing is performed with Neyman-Pearson criterion. Spatial segmentation produces a spatial segmentation mask, and temporal segmentation yields a change detection mask that indicates moving objects and the background. Then spatial-temporal merging can be used to get the final results. This method has been tested on several images. Experimental results show that this segmentation method is efficient.

  2. A Novel Abandoned Object Detection System Based on Three-Dimensional Image Information

    Yiliang Zeng


    Full Text Available A new idea of an abandoned object detection system for road traffic surveillance systems based on three-dimensional image information is proposed in this paper to prevent traffic accidents. A novel Binocular Information Reconstruction and Recognition (BIRR algorithm is presented to implement the new idea. As initial detection, suspected abandoned objects are detected by the proposed static foreground region segmentation algorithm based on surveillance video from a monocular camera. After detection of suspected abandoned objects, three-dimensional (3D information of the suspected abandoned object is reconstructed by the proposed theory about 3D object information reconstruction with images from a binocular camera. To determine whether the detected object is hazardous to normal road traffic, road plane equation and height of suspected-abandoned object are calculated based on the three-dimensional information. Experimental results show that this system implements fast detection of abandoned objects and this abandoned object system can be used for road traffic monitoring and public area surveillance.

  3. Nonretinotopic perception of orientation: Temporal integration of basic features operates in object-based coordinates.

    Wutz, Andreas; Drewes, Jan; Melcher, David


    Early, feed-forward visual processing is organized in a retinotopic reference frame. In contrast, visual feature integration on longer time scales can involve object-based or spatiotopic coordinates. For example, in the Ternus-Pikler (T-P) apparent motion display, object identity is mapped across the object motion path. Here, we report evidence from three experiments supporting nonretinotopic feature integration even for the most paradigmatic example of retinotopically-defined features: orientation. We presented observers with a repeated series of T-P displays in which the perceived rotation of Gabor gratings indicates processing in either retinotopic or object-based coordinates. In Experiment 1, the frequency of perceived retinotopic rotations decreased exponentially for longer interstimulus intervals (ISIs) between T-P display frames, with object-based percepts dominating after about 150-250 ms. In a second experiment, we show that motion and rotation judgments depend on the perception of a moving object during the T-P display ISIs rather than only on temporal factors. In Experiment 3, we cued the observers' attentional state either toward a retinotopic or object motion-based reference frame and then tracked both the observers' eye position and the time course of the perceptual bias while viewing identical T-P display sequences. Overall, we report novel evidence for spatiotemporal integration of even basic visual features such as orientation in nonretinotopic coordinates, in order to support perceptual constancy across self- and object motion.

  4. A theorem prover-based analysis tool for object-oriented databases

    Spelt, D.; Even, S.J.


    We present a theorem-prover based analysis tool for object-oriented database systems with integrity constraints. Object-oriented database specifications are mapped to higher-order logic (HOL). This allows us to reason about the semantics of database operations using a mechanical theorem prover such

  5. Is Object-Based Attention Mandatory? Strategic Control over Mode of Attention

    Yeari, Menahem; Goldsmith, Morris


    Is object-based attention mandatory or under strategic control? In an adapted spatial cuing paradigm, participants focused initially on a central arrow cue that was part of a perceptual group (Experiment 1) or a uniformly connected object (Experiment 2), encompassing one of the potential target locations. The cue always pointed to an opposite,…

  6. A knowledge-based object recognition system for applications in the space station

    Dhawan, Atam P.


    A knowledge-based three-dimensional (3D) object recognition system is being developed. The system uses primitive-based hierarchical relational and structural matching for the recognition of 3D objects in the two-dimensional (2D) image for interpretation of the 3D scene. At present, the pre-processing, low-level preliminary segmentation, rule-based segmentation, and the feature extraction are completed. The data structure of the primitive viewing knowledge-base (PVKB) is also completed. Algorithms and programs based on attribute-trees matching for decomposing the segmented data into valid primitives were developed. The frame-based structural and relational descriptions of some objects were created and stored in a knowledge-base. This knowledge-base of the frame-based descriptions were developed on the MICROVAX-AI microcomputer in LISP environment. The simulated 3D scene of simple non-overlapping objects as well as real camera data of images of 3D objects of low-complexity have been successfully interpreted.

  7. An Unsupervised Approach to Activity Recognition and Segmentation based on Object-Use Fingerprints

    Gu, Tao; Chen, Shaxun; Tao, Xianping;


    machine learning techniques typically require an appropriate training process in which training data need to be labeled manually. In this paper, we propose an unsupervised approach based on object-use fingerprints to recognize activities without human labeling. We show how to build our activity models...... based on object-use fingerprints, which are sets of contrast patterns describing significant differences of object use between any two activity classes. We then propose a fingerprint-based algorithm to recognize activities. We also propose two heuristic algorithms based on object relevance to segment...... a trace and detect the boundary of any two adjacent activities. We develop a wearable RFID system and conduct a real-world trace collection done by seven volunteers in a smart home over a period of 2 weeks. We conduct comprehensive experimental evaluations and comparison study. The results show that our...



    Video object extraction is a key technology in content-based video coding. A novel video object extracting algorithm by two Dimensional (2-D) mesh-based motion analysis is proposed in this paper. Firstly, a 2-D mesh fitting the original frame image is obtained via feature detection algorithm.Then, higher order statistics motion analysis is applied on the 2-D mesh representation to get an initial motion detection mask. After post-processing, the final segmenting mask is quickly obtained. And hence the video object is effectively extracted. Experimental results show that the proposed algorithm combines the merits of mesh-based segmenting algorithms and pixel-based segmenting algorithms, and hereby achieves satisfactory subjective and objective performance while dramatically increasing the segmenting speed.

  9. A Framework-Based Environment for Object-Oriented Scientific Codes

    Ballance, Robert A.; Giancola, Anthony J.; George F. Luger; Ross, Timothy J.


    Frameworks are reusable object-oriented designs for domain-specific programs. In our estimation, frameworks are the key to productivity and reuse. However, frameworks require increased support from the programming environment. A framework-based environment must include design aides and project browsers that can mediate between the user and the framework. A framework-based approach also places new requirements on conventional tools such as compilers. This article explores the impact of object-...

  10. A correlation-based algorithm for recognition and tracking of partially occluded objects

    Ruchay, Alexey; Kober, Vitaly


    In this work, a correlation-based algorithm consisting of a set of adaptive filters for recognition of occluded objects in still and dynamic scenes in the presence of additive noise is proposed. The designed algorithm is adaptive to the input scene, which may contain different fragments of the target, false objects, and background to be rejected. The algorithm output is high correlation peaks corresponding to pieces of the target in scenes. The proposed algorithm uses a bank of composite optimum filters. The performance of the proposed algorithm for recognition partially occluded objects is compared with that of common algorithms in terms of objective metrics.

  11. A repository based tool for re-engineering towards an object oriented environment

    Signore, Oreste; Loffredo, Mario


    Software re-engineering and object orientation are two areas of growing interest in the last years. However, while many researchers have focused their interest in the object-oriented design methodologies, a little attention has been paid to the re-engineering towards an object-oriented environment. In this paper we examine the motivations towards object-oriented re-engineering (extendibility, robustness and reusability of the code) and the problems found in moving from a process-based to an o...

  12. Home-Explorer: Ontology-Based Physical Artifact Search and Hidden Object Detection System

    Bin Guo


    Full Text Available A new system named Home-Explorer that searches and finds physical artifacts in a smart indoor environment is proposed. The view on which it is based is artifact-centered and uses sensors attached to the everyday artifacts (called smart objects in the real world. This paper makes two main contributions: First, it addresses, the robustness of the embedded sensors, which is seldom discussed in previous smart artifact research. Because sensors may sometimes be broken or fail to work under certain conditions, smart objects become hidden ones. However, current systems provide no mechanism to detect and manage objects when this problem occurs. Second, there is no common context infrastructure for building smart artifact systems, which makes it difficult for separately developed applications to interact with each other and uneasy for them to share and reuse knowledge. Unlike previous systems, Home-Explorer builds on an ontology-based knowledge infrastructure named Sixth-Sense, which makes it easy for the system to interact with other applications or agents also based on this ontology. The hidden object problem is also reflected in our ontology, which enables Home-Explorer to deal with both smart objects and hidden objects. A set of rules for deducing an object's status or location information and for locating hidden objects are described and evaluated.

  13. [Competency-based medical education: National Catalogue of Learning Objectives in surgery].

    Kadmon, M; Bender, M J; Adili, F; Arbab, D; Heinemann, M K; Hofmann, H S; König, S; Küper, M A; Obertacke, U; Rennekampff, H-O; Rolle, U; Rücker, M; Sader, R; Tingart, M; Tolksdorf, M M; Tronnier, V; Will, B; Walcher, F


    Competency-based medical education is a prerequisite to prepare students for the medical profession. A mandatory professional qualification framework is a milestone towards this aim. The National Competency-based Catalogue of Learning Objectives for Undergraduate Medical Education (NKLM) of the German Medical Faculty Association (MFT) and the German Medical Association will constitute a basis for a core curriculum of undergraduate medical training. The Surgical Working Group on Medical Education (CAL) of the German Association of Surgeons (DGCH) aims at formulating a competency-based catalogue of learning objectives for surgical undergraduate training to bridge the gap between the NKLM and the learning objectives of individual medical faculties. This is intended to enhance the prominence and visibility of the surgical discipline in the context of medical education. On the basis of different faculty catalogues of learning objectives, the catalogue of learning objectives of the German Association of Orthopedics and Orthopedic Surgery and the Swiss Catalogue of Learning Objectives representatives of all German Surgical Associations cooperated towards a structured selection process of learning objectives and the definition of levels and areas of competencies. After completion the catalogue of learning objectives will be available online on the webpage of the DGCH.

  14. Query and Update Efficient B+-Tree Based Indexing of Moving Objects

    Jensen, Christian Søndergaard; Lin, Dan; Ooi, Beng Chin


    are streamed to a database. Indexes for moving objects must support queries efficiently, but must also support frequent updates. Indexes based on minimum bounding regions (MBRs) such as the R-tree exhibit high concurrency overheads during node splitting, and each individual update is known to be quite costly....... This motivates the design of a solution that enables the B+-tree to manage moving objects. We represent moving-object locations as vectors that are timestamped based on their update time. By applying a novel linearization technique to these values, it is possible to index the resulting values using a single B...

  15. Affordance estimation for vision-based object replacement on a humanoid robot

    Mustafa, Wail; Wächter, Mirko; Szedmak, Sandor;


    In this paper, we address the problem of finding replacements of missing objects, involved in the execution of manipulation tasks. Our approach is based on estimating functional affordances for the unknown objects in order to propose replacements. We use a vision-based affordance estimation syste...... large-scale datasets. The results indicate that the system is able to successfully predict the affordances of novel objects. We also implement our system on a humanoid robot and demonstrate the affordance estimation in a real scene....

  16. 逆合成孔径成像激光雷达微多普勒效应分析及特征提取%Analysis of Micro-Doppler Effect and Feature Extraction of Target in Inverse Synthetic Aperture Imaging Ladar

    何劲; 张群; 罗迎; 杨小优


    研究了基于逆合成孔径成像激光雷达的目标微多普勒效应,分析了激光信号高载频和大带宽对目标微动点一维距离像的影响,在此基础上建立了相应的微多普勒特征参数方程并讨论了快时间对微多普勒效应的影响.针对逆合成孔径成像激光雷达系统目标微多普勒效应的特点,提出了一种结合二值数学形态学腐蚀膨胀运算和推广Hough变换的目标微多普勒特征提取方法.仿真实验验证了文中微多普勒效应理论分析和微多普勒特征提取算法的正确性,并证明了逆合成孔径成像激光雷达对厘米或毫米量级微动观测的有效性.%The micro-Doppler effect based on the inverse synthetic imaging ladar is studied, and the influence of the high carrier frequency and the bandwidth of laser signal on the range profiles is also analyzed.Furthermore, the corresponding parameters equation of micro-Doppler is constructed and then the influence of the fast-time on the micro-Doppler effect is also discussed. Based on the Extended Hough Transform and the erosion and dilation operations in binary mathematical morphology, a novel extraction method of micro-Doppler effect is proposed for the inverse synthetic imaging ladar accordingly. The simulations are given to verify the theoretical derivation and the validity of the proposed method. In addition, the validity of the measurement of the inverse synthetic imaging ladar for the micro-motion at the centimeter-level or the millimeter-level is also illustrated in the simulations.

  17. The research of moving object detection based on background difference compensation

    Song, Yan-bin; Ying, Jie; Lu, Lin-li


    Moving object detection was implemented in dynamic background based on background difference compensation. Background differential can effectively segment the moving object in static background. But in moving video, the camera motion causes corresponding movement of the target and background, which makes the prospect moving object hard to separate from the background. In order to detect moving object, we can compensate the movement of the background and transfer the dynamic background to static. Moving object detection in static background image was implemented using a new weights updating method that the weights were updated during a certain period. This method based on classical Gaussian mixture model improved the efficiency of image segmentation greatly. Moving object detection in dynamic background was realized using background differential compensation. The global motion of the background was established according to the affined parameters model. The model parameters were estimated by feature points matching based on the search strategy. Invalid matching points were eliminated using the method of distance consistency. Backward mapping was used to get the motion parameters of the background. After compensation of the background with the global motion parameters, frame difference between the current frame and the background can detect moving objects effectively. Experiments were done on computer with the programming tools of VS2010 and MATLAB. Experimental results showed that the algorithm based on differential compensation was effective.

  18. General moving objects recognition method based on graph embedding dimension reduction algorithm

    Yi ZHANG; Jie YANG; Kun LIU


    Effective and robust recognition and tracking of objects are the key problems in visual surveillance systems. Most existing object recognition methods were designed with particular objects in mind. This study presents a general moving objects recognition method using global features of targets. Targets are extracted with an adaptive Gaussian mixture model and their silhouette images are captured and unified. A new objects silhouette database is built to provide abundant samples to train the subspace feature. This database is more convincing than the previous ones. A more effective dimension reduction method based on graph embedding is used to obtain the projection eigenvector. In our experiments, we show the effective performance of our method in addressing the moving objects recognition problem and its superiority compared with the previous methods.

  19. Spatio-temporal Compensation Based Object Detection for Video Surveillance Systems

    LI Ren-jie; YU Song-yu; XIONG Hong-kai


    Moving object detection in video surveillance is an important step.This paper addresses an automatic object detection algorithm based on spatio-temporal compensation for video surveillance.Temporal difference of the pairs of two frames with a k-frame distance is utilized to obtain coarse object masks.Usually,object regions in these coarse masks have discontinuous boundaries and some holes.Region growing with the distance constraint is proposed to compensate these coarse object regions in spatial domain,followed by filling holes.The added distance constraint can prevent object regions from growing infinitely.The proposed fining holes method is simple and effective.To solve the temporarily stopping problem of moving objects,temporal compensation is proposed to compensate the object mask by utilizing temporal coherence of moving objects in temporal domain.The proposed detection algorithm can extract moving objects as completely as possible.Experimental results have successfully demonstrated the validity of the proposed algorithm.

  20. Study on tracking technology of the moving object based on computer vision

    Xu, Ranran; Xu, Zhenying; Li, Boquan


    The tracking technology of the moving object has been an active topic of the visual tracking system. In this paper, the tracking algorithms are classified into four classes: correlation-based methods, boundary-based methods, model-based methods and multifunctional methods. Based on the analysis of the advantages and disadvantages of all these algorithms, a new tracking algorithm, integrating SSDA and advanced Camshift algorithm, is put forward here.

  1. Automatic Multi-Level Thresholding Segmentation Based on Multi-Objective Optimization

    L. DJEROU,


    Full Text Available In this paper, we present a new multi-level image thresholding technique, called Automatic Threshold based on Multi-objective Optimization "ATMO" that combines the flexibility of multi-objective fitness functions with the power of a Binary Particle Swarm Optimization algorithm "BPSO", for searching the "optimum" number of the thresholds and simultaneously the optimal thresholds of three criteria: the between-class variances criterion, the minimum error criterion and the entropy criterion. Some examples of test images are presented to compare our segmentation method, based on the multi-objective optimization approach with Otsu’s, Kapur’s and Kittler’s methods. Our experimental results show that the thresholding method based on multi-objective optimization is more efficient than the classical Otsu’s, Kapur’s and Kittler’s methods.

  2. Multi-view space object recognition and pose estimation based on kernel regression

    Zhang Haopeng; Jiang Zhiguo


    The application of high-performance imaging sensors in space-based space surveillance systems makes it possible to recognize space objects and estimate their poses using vision-based methods. In this paper, we proposed a kernel regression-based method for joint multi-view space object recognition and pose estimation. We built a new simulated satellite image dataset named BUAA-SID 1.5 to test our method using different image representations. We evaluated our method for recognition-only tasks, pose estimation-only tasks, and joint recognition and pose estimation tasks. Experimental results show that our method outperforms the state-of-the-arts in space object recognition, and can recognize space objects and estimate their poses effectively and robustly against noise and lighting conditions.

  3. Multi-view space object recognition and pose estimation based on kernel regression

    Zhang Haopeng


    Full Text Available The application of high-performance imaging sensors in space-based space surveillance systems makes it possible to recognize space objects and estimate their poses using vision-based methods. In this paper, we proposed a kernel regression-based method for joint multi-view space object recognition and pose estimation. We built a new simulated satellite image dataset named BUAA-SID 1.5 to test our method using different image representations. We evaluated our method for recognition-only tasks, pose estimation-only tasks, and joint recognition and pose estimation tasks. Experimental results show that our method outperforms the state-of-the-arts in space object recognition, and can recognize space objects and estimate their poses effectively and robustly against noise and lighting conditions.

  4. Ego, Alter and Object: explaining Personal Involvement with a Social Object Based on Perceived Collective Involvement and Group Identification

    Joao Wachelke


    Full Text Available The present paper aims at testing a model to predict personal involvement with a social object which was inspired by the social psychological triangle proposed by Moscovici. The triangle bridges three essential aspects of social psychology: the individual, the Other and a social object. It was operationalized as an empirical model to explain personal involvement with a social topic from two predictors: perceived collective involvement of group members with the same topic and group identification. The sample was formed by 805 Brazilian undergraduates. The participants completed scales that measured their identification with university students, their perception of students' involvement with two social objects, university course or job, and their own personal involvement with those topics. Regression analyses supported the hypothesis that group identification, perceived collective involvement and their interaction maintained positive relations with personal involvement. Discussion focuses on the relativity of results to specific objects, the complexity of determinant factors of personal involvement and the pertinence of the triangular look to characterize social psychological research.

  5. LABRADOR: a learning autonomous behavior-based robot for adaptive detection and object retrieval

    Yamauchi, Brian; Moseley, Mark; Brookshire, Jonathan


    As part of the TARDEC-funded CANINE (Cooperative Autonomous Navigation in a Networked Environment) Program, iRobot developed LABRADOR (Learning Autonomous Behavior-based Robot for Adaptive Detection and Object Retrieval). LABRADOR was based on the rugged, man-portable, iRobot PackBot unmanned ground vehicle (UGV) equipped with an explosives ordnance disposal (EOD) manipulator arm and a custom gripper. For LABRADOR, we developed a vision-based object learning and recognition system that combined a TLD (track-learn-detect) filter based on object shape features with a color-histogram-based object detector. Our vision system was able to learn in real-time to recognize objects presented to the robot. We also implemented a waypoint navigation system based on fused GPS, IMU (inertial measurement unit), and odometry data. We used this navigation capability to implement autonomous behaviors capable of searching a specified area using a variety of robust coverage strategies - including outward spiral, random bounce, random waypoint, and perimeter following behaviors. While the full system was not integrated in time to compete in the CANINE competition event, we developed useful perception, navigation, and behavior capabilities that may be applied to future autonomous robot systems.

  6. On model checking the dynamics of object-based software : a foundational approach

    Distefano, Dino Salvo


    This dissertation is concerned with software verication, in particular automated techniques to assess the correct functioning of object-based programs. We focus on the dynamic aspects of these programs and consider model-checking based verication techniques. The major obstacle to the design of model

  7. Discrete Topology Based Hierarchical Segmentation for Efficient Object-Based Image Analyis: Application to Object Detection in High Resolution Satellite Images

    Syed, A. H.; Saber, E.; Messinger, D.


    With rapid developments in satellite and sensor technologies, there has been a dramatic increase in the availability of high resolution (HR) remotely sensed images. Hence, the ability to collect images remotely is expected to far exceed our capacity to analyse these images manually. Consequently, techniques that can handle large volumes of data are urgently needed. In many of today's multiscale techniques the underlying representation of objects is still pixel-based, i.e. object entities are still described/accessed via pixelbased descriptors, thereby creating a bottleneck when processing large volumes of data. Also, these techniques do not yet leverage the topological and contextual information present in the image. We propose a framework for Discrete Topology based hierarchical segmentation, addressing both the algorithms and data structures that will be required. The framework consists of three components: 1) Conversion to dart-based representation, 2) Size-Constrained-Region Merging to generate multiple segmentations, and 3) Update of two sparse arrays SIGMA and LAMBDA which together encode the topology of each region in the hierarchy. The results of our representation are demonstrated both on a synthetic and a real high resolution images. Application of this representation to objectdetection is also discussed.

  8. A Framework-Based Environment for Object-Oriented Scientific Codes

    Robert A. Ballance


    Full Text Available Frameworks are reusable object-oriented designs for domain-specific programs. In our estimation, frameworks are the key to productivity and reuse. However, frameworks require increased support from the programming environment. A framework-based environment must include design aides and project browsers that can mediate between the user and the framework. A framework-based approach also places new requirements on conventional tools such as compilers. This article explores the impact of object-oriented frameworks upon a programming environment, in the context of object-oriented finite element and finite difference codes. The role of tools such as design aides and project browsers is discussed, and the impact of a framework-based approach upon compilers is examined. Examples are drawn from our prototype C++ based environment.

  9. Autonomous shooting at middle size space debris objects from space-based APT laser systems

    Gambi, J. M.; García del Pino, M. L.


    This paper is motivated by the need of removing middle size space debris objects. It deals with the problem of increasing the pointing accuracy while shooting at these objects by means of autonomous space-based APT systems endowed with very narrow laser beams. It is shown that shooting at these objects with these systems is the one single ballistic problem that becomes singular in space. This means that the shooting direction that is to be implemented by any of these systems to reach an object at a given instant can only be hopefully implemented after the object has been previously reached. Thus, the problem becomes backwards recurrent with no end for any object-system configuration, except when the LOS direction remains constant for some period of time. It is also shown that the implementation of the point-ahead angles from the data acquired prior to the respective shootings is essential to keep accuracy. In fact, one single omission during the action may cause errors larger than the size of the objects. As a consequence, we find that there is only one way for an autonomous system to minimize the pointing errors: any shooting sequence to any of these objects must be started when the transverse component of the relative velocity of the object with respect to the system is zero (actually, as close to zero as possible).

  10. Recognition of 3-D objects based on Markov random field models

    HUANG Ying; DING Xiao-qing; WANG Sheng-jin


    The recognition of 3-D objects is quite a difficult task for computer vision systems.This paper presents a new object framework,which utilizes densely sampled grids with different resolutions to represent the local information of the input image.A Markov random field model is then created to model the geometric distribution of the object key nodes.Flexible matching,which aims to find the accurate correspondence map between the key points of two images,is performed by combining the local similarities and the geometric relations together using the highest confidence first method.Afterwards,a global similarity is calculated for object recognition. Experimental results on Coil-100 object database,which consists of 7 200 images of 100 objects,are presented.When the numbers of templates vary from 4,8,18 to 36 for each object,and the remaining images compose the test sets,the object recognition rates are 95.75 %,99.30 %,100.0 % and 100.0 %,respectively.The excellent recognition performance is much better than those of the other cited references,which indicates that our approach is well-suited for appearance-based object recognition.

  11. Brazilian Proposal for Agent-Based Learning Objects Metadata Standard - OBAA

    Vicari, Rosa Maria; Ribeiro, Alexandre; da Silva, Júlia Marques Carvalho; Santos, Elder Rizzon; Primo, Tiago; Bez, Marta

    This paper presents the Agent Based Learning Objects - OBAA standard proposal. The main goal of the research was to establish a standardized specification of the technical and functional requirements of interoperable learning objects. In our context, interoperability regards the operation of the content inside Web, Digital TV and mobile environments, supporting accessibility and pedagogical issues. In this proposal it has been explored the convergence among the multi-agent systems, learning object and ubiquitous computing technologies, allowing the authoring, storage and recovery of learning object in varied contexts and through different digital platforms. The result of this research was the definition of the OBAA proposal containing the requirements, specifications and architectures that will compose the Brazilian standard for the management, transmission, storage, search, editing and use of interoperable learning object.

  12. Use of Image Based Modelling for Documentation of Intricately Shaped Objects

    Marčiš, M.; Barták, P.; Valaška, D.; Fraštia, M.; Trhan, O.


    In the documentation of cultural heritage, we can encounter three dimensional shapes and structures which are complicated to measure. Such objects are for example spiral staircases, timber roof trusses, historical furniture or folk costume where it is nearly impossible to effectively use the traditional surveying or the terrestrial laser scanning due to the shape of the object, its dimensions and the crowded environment. The actual methods of digital photogrammetry can be very helpful in such cases with the emphasis on the automated processing of the extensive image data. The created high resolution 3D models and 2D orthophotos are very important for the documentation of architectural elements and they can serve as an ideal base for the vectorization and 2D drawing documentation. This contribution wants to describe the various usage of image based modelling in specific interior spaces and specific objects. The advantages and disadvantages of the photogrammetric measurement of such objects in comparison to other surveying methods are reviewed.

  13. Key Object Discovery and Tracking Based on Context-Aware Saliency

    Geng Zhang


    Full Text Available In this paper, we propose an online key object discovery and tracking system based on visual saliency. We formulate the problem as a temporally consistent binary labelling task on a conditional random field and solve it by using a particle filter. We also propose a context‐aware saliency measurement, which can be used to improve the accuracy of any static or dynamic saliency maps. Our refined saliency maps provide clearer indications as to where the key object lies. Based on good saliency cues, we can further segment the key object inside the resulting bounding box, considering the spatial and temporal context. We tested our system extensively on different video clips. The results show that our method has significantly improved the saliency maps and tracks the key object accurately.

  14. Proposed educational objectives for hospital-based dentists during catastrophic events and disaster response.

    Psoter, Walter J; Herman, Neal G; More, Frederick G; Park, Patricia; Robbins, Miriam; Rekow, E Dianne; Ryan, James M; Triola, Marc M; Glotzer, David


    The purpose of this project was to define education and training requirements for hospital-based dentists to efficiently and meaningfully participate in a hospital disaster response. Eight dental faculty with hospital-based training and/or military command and CBRNE (chemical, biological, radiological, nuclear, and explosive) expertise were recruited as an expert panel. A consensus set of recommended educational objectives for hospital-based dentists was established using the following process: 1) identify assumptions supported by all expert panelists, 2) determine current advanced dental educational training requirements, and 3) conduct additional training and literature review by various panelists and discussions with other content and systems experts. Using this three-step process, educational objectives that the development group believed necessary for hospital-based dentists to be effective in treatment or management roles in times of a catastrophic event were established. These educational objectives are categorized into five thematic areas: 1) disaster systems, 2) triage/medical assessment, 3) blast and burn injuries, 4) chemical agents, and 5) biological agents. Creation of training programs to help dentists acquire these educational objectives would benefit hospital-based dental training programs and strengthen hospital surge manpower needs. The proposed educational objectives are designed to stimulate discussion and debate among dental, medical, and public health professionals about the roles of dentists in meeting hospital surge manpower needs.

  15. Improved maximum average correlation height filter with adaptive log base selection for object recognition

    Tehsin, Sara; Rehman, Saad; Awan, Ahmad B.; Chaudry, Qaiser; Abbas, Muhammad; Young, Rupert; Asif, Afia


    Sensitivity to the variations in the reference image is a major concern when recognizing target objects. A combinational framework of correlation filters and logarithmic transformation has been previously reported to resolve this issue alongside catering for scale and rotation changes of the object in the presence of distortion and noise. In this paper, we have extended the work to include the influence of different logarithmic bases on the resultant correlation plane. The meaningful changes in correlation parameters along with contraction/expansion in the correlation plane peak have been identified under different scenarios. Based on our research, we propose some specific log bases to be used in logarithmically transformed correlation filters for achieving suitable tolerance to different variations. The study is based upon testing a range of logarithmic bases for different situations and finding an optimal logarithmic base for each particular set of distortions. Our results show improved correlation and target detection accuracies.

  16. Single camera-based object detection and tracking for mobile robots

    Anderson, J. Keith; Iftekharuddin, Khan M.; Threlkeld, Elizabeth; Montgomery, Brad


    In order for a mobile robot to successfully navigate its environment, it must have knowledge about the objects in its immediate vicinity. The robot can use this information for localization, navigation and object avoidance. Among many sensors available for object detection we are primarily interested in camera-based vision for indoor robot navigation. In this work, we focus on using a single camera to detect objects in the field of view of the robot for the purpose of obstacle avoidance. In order to obtain an integrated robot obstacle avoidance and navigation technique, we investigate a modular approach. In the first module, we extend an appearance based object detection (ABOD) technique to automatically identify individual objects. We then extract strong corner features, overlaying them over the identified objects. This allows us to select a few representative corners for each object. In the second module, we attempt to group these strong corner features using a planar homography technique to define more natural features such as 'planes' for further processing. As an added feature, we utilize the strong corner features generated from module 1, the corresponding features in the next frame from module 2 and a basic optical flow technique for tracking these identified objects. In the third and final module, we obtain distance and heading information for each of obstacles as the robot avoids and navigates in an indoor environment. We show both simulation and actual results on a mobile robot for each of these three modules. We hope to integrate these three modules to obtain a single camera-based integrated robot obstacle avoidance and navigation technique in future.

  17. Bin-objective shape optimization based on linear programming model of arch dam

    JIN Hai; LIN Gao; YANG Ming-sheng


    Bin-objective shape optimization of arch dam based on linear programming model is discussed to minimize both dam volume and maximal tensile stress. The importance of weight coefficient of the above two objectives is chosen according to the value of importance ratio. The influence of weight coefficient to the optimization result is discussed in detail and the numerical example shows that both the model and method proposed is doable.

  18. Low-Carbon Based Multi-Objective Bi-Level Power Dispatching under Uncertainty


    This research examines a low-carbon power dispatch problem under uncertainty. A hybrid uncertain multi-objective bi-level model with one leader and multiple followers is established to support the decision making of power dispatch and generation. The upper level decision maker is the regional power grid corporation which allocates power quotas to each follower based on the objectives of reasonable returns, a small power surplus and low carbon emissions. The lower level decision makers are the...

  19. Vision based Object Recognition of E-Puck Mobile Robot for Warehouse Application

    Mehmet S. Guzel; John Erwin; Wan Nurshazwani Wan Zakaria


    At present, most warehouses still require human services for unloading of goods. Unloading of goods requires a continuous system to ensure the quality of work productivity. Therefore the need of autonomous robot system in warehouse is needed to improve the quality of work. Thus, a localization and recognition algorithm is developed and implemented on the E-puck robot. The task involves the recognition of desired object based on their colour (red and blue) and locating the desired object to th...

  20. Context based Coding of Binary Shapes by Object Boundary Straightness Analysis

    Aghito, Shankar Manuel; Forchhammer, Søren


    A new lossless compression scheme for bilevel images targeted at binary shapes of image and video objects is presented. The scheme is based on a local analysis of the digital straightness of the causal part of the object boundary, which is used in the context definition for arithmetic encoding. T...... more efficient than the state-of-the-art and more complex free tree coder for most of the binary shape and map test images....

  1. Genetic Algorithm-Based Multi-objective Optimisation for QoS-Aware Web Services Composition

    Li, Li; Yang, Pengyi; Ou, Ling; Zhang, Zili; Cheng, Peng

    Finding an optimal solution for QoS-aware Web service composition with various restrictions on qualities is a multi-objective optimisation problem. A popular multi-objective genetic algorithm, NSGA-II, is studied in order to provide a set of optimal solutions for QoS-based service composition. Experiments with different numbers of abstract and concrete services confirm the expected behaviour of the algorithm.

  2. Video segmentation based on the presence and/or absence of moving objects

    Nitsuwat, Supot; Jin, Jesse S.; Hudson, M. B.


    Video clip is the dominant component of multimedia system. However, video data are voluminous. An effective and efficient visual data management system is highly desired. Recent technology in digital video processing has moved to 'content-based' storage and retrieval. To detect meaningful area/region, using only production and camera operation- based detection is not enough. The contents of a video also have to be considered. The basic idea of this scheme is that if we can distinguish individual objects in the whole video sequence, we would be able to capture the changes in content throughout the sequences. Among many object features, motion content has been widely used as an important key in video storage and retrieval systems. Therefore, through motion- based representation, this paper will investigate an algorithm for sub-shot extraction and key-frame selection. From a given video sequence, first we segment the sequence into shots by using some of the production and camera operation-based detection techniques. Then, from the beginning of each shot, we calculate optical flow vectors by using complex wavelet phase-matching-based method on a pair of successive frames. Next, we segment each moving object based on these vectors using clustering in a competitive agglomeration scheme and represent them into a number of layers. After separating moving object(s) from each other for every frame in this shot, we extract sub-shots and select key-frames by using information about the presence and absence of moving object in each layer. Finally, these key-frames and sub-shots have been used to represent the whole video in panoramic mosaic-based representation form. Experimental results showing the significance of the proposed method are also provided.

  3. A Methodology to Evaluate Object oriented Software Systems Using Change Requirement Traceability Based on Impact Analysis

    Sunil T. D


    Full Text Available It is a well known fact that software maintenance plays a major role and finds importance in software development life cycle. As object - oriented programming has become the standard, it is very important to understand the problems of maintaining object -oriented software systems. This paper aims at evaluating object - oriented software system through change requirement traceability – based impact analysis methodology for non functional requirements using functional requirements . The major issues have been related to change impact algorithms and inheritance of functionality.

  4. Multi-Objective Optimization and Analysis Model of Sintering Process Based on BP Neural Network

    ZHANG Jun-hong; XIE An-guo; SHEN Feng-man


    A multi-objective optimization and analysis model of the sintering process based on BP neural network is presented. Genetic algorithms are combined to simplify the BP neural network, which can reduce the learning time and increase the forecasting accuracy of the network model. This model has been experimented in the sintering process, and the production cost, the energy consumption, the quality (revolving intensity), and the output are considered at the same time. Moreover, the relation between some factors and the multi-objectives has been analyzed, and the results are consistent with the process. Different objectives are emphasized at different practical periods, and this can provide a theoretical basis for the manager.

  5. When action turns into words. Activation of motor-based knowledge during categorization of manipulable objects

    Gerlach, Christian; Law, Ian; Paulson, Olaf B


    Functional imaging studies have demonstrated that processing of man-made objects activate the left ventral premotor cortex, which is known to be concerned with motor function. This has led to the suggestion that the comprehension of man-made objects may rely on motor-based knowledge of object uti...... organized. Instead, the data are compatible with the suggestion that categories differ in the weight they put on different types of knowledge.......Functional imaging studies have demonstrated that processing of man-made objects activate the left ventral premotor cortex, which is known to be concerned with motor function. This has led to the suggestion that the comprehension of man-made objects may rely on motor-based knowledge of object...... utilization (action knowledge). Here we show that the left ventral premotor cortex is activated during categorization of "both" fruit/vegetables and articles of clothing, relative to animals and nonmanipulable man-made objects. This observation suggests that action knowledge may not be important...

  6. Two-year-olds exclude novel objects as potential referents of novel words based on pragmatics.

    Grassmann, Susanne; Stracke, Marén; Tomasello, Michael


    Many studies have established that children tend to exclude objects for which they already have a name as potential referents of novel words. In the current study we asked whether this exclusion can be triggered by social-pragmatic context alone without pre-existing words as blockers. Two-year-old children watched an adult looking at a novel object while saying a novel word with excitement. In one condition the adult had not seen the object beforehand, and so the children interpreted the adult's utterance as referring to the gazed-at object. In another condition the adult and child had previously played jointly with the gazed-at object. In this case, children less often assumed that the adult was referring to the object but rather they searched for an alternative referent--presumably because they inferred that the gazed-at object was old news in their common ground with the adult and so not worthy of excited labeling. Since this inference based on exclusion is highly similar to that underlying the Principle of Contrast/Mutual Exclusivity, we propose that this principle is not purely lexical but rather is based on children's understanding of how and why people direct one another's attention to things either with or without language.

  7. A cluster-based method for marine sensitive object extraction and representation

    Xue, Cunjin; Dong, Qing; Qin, Lijuan


    Within the context of global change, marine sensitive factors or Marine Essential Climate Variables have been defined by many projects, and their sensitive spatial regions and time phases play significant roles in regional sea-air interactions and better understanding of their dynamic process. In this paper, we propose a cluster-based method for marine sensitive region extraction and representation. This method includes a kernel expansion algorithm for extracting marine sensitive regions, and a field-object triple form, integration of object-oriented and field-based model, for representing marine sensitive objects. Firstly, this method recognizes ENSO-related spatial patterns using empirical orthogonal decomposition of long term marine sensitive factors and correlation analysis with multiple ENSO index. The cluster kernel, defined by statistics of spatial patterns, is initialized to carry out spatial expansion and cluster mergence with spatial neighborhoods recursively, then all the related lattices with similar behavior are merged into marine sensitive regions. After this, the Field-object triple form of is used to represent the marine sensitive objects, both with the discrete object with a precise extend and boundary, and the continuous field with variations dependent on spatial locations. Finally, the marine sensitive objects about sea surface temperature are extracted, represented and analyzed as a case of study, which proves the effectiveness and the efficiency of the proposed method.

  8. A drifting trajectory prediction model based on object shape and stochastic mo-tion features

    王胜正; 聂皓冰; 施朝健


    There is a huge demand to develop a method for marine search and rescue (SAR) operators automatically predicting the most probable searching area of the drifting object. This paper presents a novel drifting prediction model to improve the accuracy of the drifting trajectory computation of the sea-surface objects. First, a new drifting kinetic model based on the geometry characteristics of the objects is proposed that involves the effects of the object shape and stochastic motion features in addition to the traditional factors of wind and currents. Then, a computer simulation-based method is employed to analyze the stochastic motion features of the drifting objects, which is applied to estimate the uncertainty parameters of the stochastic factors of the drifting objects. Finally, the accuracy of the model is evaluated by comparison with the flume experimental results. It is shown that the proposed method can be used for various shape objects in the drifting trajectory prediction and the maritime search and rescue decision-making system.



    In this paper a new method is developed to make a dynamic layout adjustment and navigation for enterprise Geographic Information System(GIS) based on object mark recognition. The extraction of object mark images is based on some morphological structural patterns, which are described by morphological structural points, contour property, and other geometrical data in a binary image of enterprise geographic information map. Some pre-processing methods, contour smooth following, linearization and extraction patterns of structural points, are introduced. If any special object is selected to make a decision in a GIS map, the all information around it will be obtained. That is, we need to investigate similar object enterprises around selected region to analyse whether it is necessary for establishing the object enterprise at that place. To further navigate GIS map, we need to move from one region to another. Each time a region is formed and displayed based on the user′ s focus. If a focus point of a map is selected, in terms of extracted object mark image, a dynamic layout and navigation diagram is constructed. When the user changes the focus (i.e. click a node in the navigation mode), a new sub-diagram is formed by dropping old nodes and adding new nodes. The prototype system provides effective interfaces that support GIS image navigation, detailed local image/map viewing, and enterprise information browsing.

  10. Learning-Based Object Identification and Segmentation Using Dual-Energy CT Images for Security.

    Martin, Limor; Tuysuzoglu, Ahmet; Karl, W Clem; Ishwar, Prakash


    In recent years, baggage screening at airports has included the use of dual-energy X-ray computed tomography (DECT), an advanced technology for nondestructive evaluation. The main challenge remains to reliably find and identify threat objects in the bag from DECT data. This task is particularly hard due to the wide variety of objects, the high clutter, and the presence of metal, which causes streaks and shading in the scanner images. Image noise and artifacts are generally much more severe than in medical CT and can lead to splitting of objects and inaccurate object labeling. The conventional approach performs object segmentation and material identification in two decoupled processes. Dual-energy information is typically not used for the segmentation, and object localization is not explicitly used to stabilize the material parameter estimates. We propose a novel learning-based framework for joint segmentation and identification of objects directly from volumetric DECT images, which is robust to streaks, noise and variability due to clutter. We focus on segmenting and identifying a small set of objects of interest with characteristics that are learned from training images, and consider everything else as background. We include data weighting to mitigate metal artifacts and incorporate an object boundary field to reduce object splitting. The overall formulation is posed as a multilabel discrete optimization problem and solved using an efficient graph-cut algorithm. We test the method on real data and show its potential for producing accurate labels of the objects of interest without splits in the presence of metal and clutter.

  11. Research On The Classification Of High Resolution Image Based On Object-oriented And Class Rule

    Li, C. K.; Fang, W.; Dong, X. J.


    With the development of remote sensing technology, the spatial resolution, spectral resolution and time resolution of remote sensing data is greatly improved. How to efficiently process and interpret the massive high resolution remote sensing image data for ground objects, which with spatial geometry and texture information, has become the focus and difficulty in the field of remote sensing research. An object oriented and rule of the classification method of remote sensing data has presents in this paper. Through the discovery and mining the rich knowledge of spectrum and spatial characteristics of high-resolution remote sensing image, establish a multi-level network image object segmentation and classification structure of remote sensing image to achieve accurate and fast ground targets classification and accuracy assessment. Based on worldview-2 image data in the Zangnan area as a study object, using the object-oriented image classification method and rules to verify the experiment which is combination of the mean variance method, the maximum area method and the accuracy comparison to analysis, selected three kinds of optimal segmentation scale and established a multi-level image object network hierarchy for image classification experiments. The results show that the objectoriented rules classification method to classify the high resolution images, enabling the high resolution image classification results similar to the visual interpretation of the results and has higher classification accuracy. The overall accuracy and Kappa coefficient of the object-oriented rules classification method were 97.38%, 0.9673; compared with object-oriented SVM method, respectively higher than 6.23%, 0.078; compared with object-oriented KNN method, respectively more than 7.96%, 0.0996. The extraction precision and user accuracy of the building compared with object-oriented SVM method, respectively higher than 18.39%, 3.98%, respectively better than the object-oriented KNN method 21

  12. Object-based illumination normalization for multi-temporal satellite images in urban area

    Su, Nan; Zhang, Ye; Tian, Shu; Yan, Yiming


    Multi-temporal satellite images acquisition with different illumination conditions cause radiometric difference to have a huge effect on image quality during remote sensing image processing. In particular, image matching of satellite stereo images with great difference between acquisition dates is very difficult for the high-precision DSM generation in the field of satellite photogrammetry. Therefore, illumination normalization is one of the greatest application technology to eliminate radiometric difference for image matching and other image applications. In this paper, we proposed a novel method of object-based illumination normalization to improve image matching of different temporal satellite stereo images in urban area. Our proposed method include two main steps: 1) the object extraction 2) multi-level illumination normalization. Firstly, we proposed a object extraction method for the same objects extraction among the multi-temporal satellite images, which can keep the object structural attribute. Moreover, the multi-level illumination normalization is proposed by combining gradient domain method and singular value decomposition (SVD) according to characteristic information of relevant objects. Our proposed method has great improvement for the illumination of object area to be benefit for image matching in urban area with multiple objects. And the histogram similarity parameter and matching rate are used for illumination consistency quantitative evaluation. The experiments have been conducted on different satellite images with different acquisition dates in the same urban area to verify the effectiveness of our proposed method. The experimental results demonstrate a good performance by comparing other methods.

  13. Implicit kernel sparse shape representation: a sparse-neighbors-based objection segmentation framework.

    Yao, Jincao; Yu, Huimin; Hu, Roland


    This paper introduces a new implicit-kernel-sparse-shape-representation-based object segmentation framework. Given an input object whose shape is similar to some of the elements in the training set, the proposed model can automatically find a cluster of implicit kernel sparse neighbors to approximately represent the input shape and guide the segmentation. A distance-constrained probabilistic definition together with a dualization energy term is developed to connect high-level shape representation and low-level image information. We theoretically prove that our model not only derives from two projected convex sets but is also equivalent to a sparse-reconstruction-error-based representation in the Hilbert space. Finally, a "wake-sleep"-based segmentation framework is applied to drive the evolutionary curve to recover the original shape of the object. We test our model on two public datasets. Numerical experiments on both synthetic images and real applications show the superior capabilities of the proposed framework.

  14. Bindings in working memory: The role of object-based attention.

    Gao, Zaifeng; Wu, Fan; Qiu, Fangfang; He, Kaifeng; Yang, Yue; Shen, Mowei


    Over the past decade, it has been debated whether retaining bindings in working memory (WM) requires more attention than retaining constituent features, focusing on domain-general attention and space-based attention. Recently, we proposed that retaining bindings in WM needs more object-based attention than retaining constituent features (Shen, Huang, & Gao, 2015, Journal of Experimental Psychology: Human Perception and Performance, doi: 10.1037/xhp0000018 ). However, only unitized visual bindings were examined; to establish the role of object-based attention in retaining bindings in WM, more emperical evidence is required. We tested 4 new bindings that had been suggested requiring no more attention than the constituent features in the WM maintenance phase: The two constituent features of binding were stored in different WM modules (cross-module binding, Experiment 1), from auditory and visual modalities (cross-modal binding, Experiment 2), or temporally (cross-time binding, Experiments 3) or spatially (cross-space binding, Experiments 4-6) separated. In the critical condition, we added a secondary object feature-report task during the delay interval of the change-detection task, such that the secondary task competed for object-based attention with the to-be-memorized stimuli. If more object-based attention is required for retaining bindings than for retaining constituent features, the secondary task should impair the binding performance to a larger degree relative to the performance of constituent features. Indeed, Experiments 1-6 consistently revealed a significantly larger impairment for bindings than for the constituent features, suggesting that object-based attention plays a pivotal role in retaining bindings in WM.

  15. Object Detection and Tracking-Based Camera Calibration for Normalized Human Height Estimation

    Jaehoon Jung


    Full Text Available This paper presents a normalized human height estimation algorithm using an uncalibrated camera. To estimate the normalized human height, the proposed algorithm detects a moving object and performs tracking-based automatic camera calibration. The proposed method consists of three steps: (i moving human detection and tracking, (ii automatic camera calibration, and (iii human height estimation and error correction. The proposed method automatically calibrates camera by detecting moving humans and estimates the human height using error correction. The proposed method can be applied to object-based video surveillance systems and digital forensic.

  16. Edge detection based on object tree image representation and wavelet transform

    屈彦呈; 王常虹; 庄显义


    In applications such as image retrieval and recognition, precise edge detection for interested regions plays a decisive role. Existing methods generally take little care about local characteristics, or become time-consuming if every detail is considered. In the paper, a new method is put forward based on the combination of effective image representation and multiscale wavelet analysis. A new object tree image representation is introduced. Then a series of object trees are constructed based on wavelet transform modulus maxima at different scales in descending order. Computation is only needed for interested regions. Implementation steps are also given with an illustrative example.

  17. JTpack90: A parallel, object-based, Fortran 90 linear algebra package

    Turner, J.A.; Kothe, D.B. [Los Alamos National Lab., NM (United States); Ferrell, R.C. [Cambridge Power Computing Associates, Ltd., Brookline, MA (United States)


    The authors have developed an object-based linear algebra package, currently with emphasis on sparse Krylov methods, driven primarily by needs of the Los Alamos National Laboratory parallel unstructured-mesh casting simulation tool Telluride. Support for a number of sparse storage formats, methods, and preconditioners have been implemented, driven primarily by application needs. They describe the object-based Fortran 90 approach, which enhances maintainability, performance, and extensibility, the parallelization approach using a new portable gather/scatter library (PGSLib), current capabilities and future plans, and present preliminary performance results on a variety of platforms.

  18. Goal-directed attention alters the tuning of object-based representations in extrastriate cortex

    Anthony J.-W. Chen


    Full Text Available Humans survive in environments that contain a vast quantity and variety of visual information. All items of perceived visual information must be represented within a limited number of brain networks. The human brain requires mechanisms for selecting only a relevant fraction of perceived information for more in-depth processing, where neural representations of that information may be actively maintained and utilized for goal-directed behavior. Object-based attention is crucial for goal-directed behavior and yet remains poorly understood. Thus, in the study we investigate how neural representations of visual object information are guided by selective attention. The magnitude of activation in human extrastriate cortex has been shown to be modulated by attention; however object-based attention is not likely to be fully explained by a localized gain mechanism. Thus, we measured information coded in spatially distributed patterns of brain activity with fMRI while human participants performed a task requiring selective processing of a relevant visual object category that differed across conditions. Using pattern classification and spatial correlation techniques, we found that the direction of selective attention is implemented as a shift in the tuning of object-based information representations within extrastriate cortex. In contrast, we found that representations within lateral prefrontal cortex coded for the attention condition rather than the concrete representations of object category. In sum, our findings are consistent with a model of object-based selective attention in which representations coded within extrastriate cortex are tuned to favor the representation of goal-relevant information, guided by more abstract representations within lateral prefrontal cortex.

  19. Multi Objective Test Suite Reduction for GUI Based Software Using NSGA-II

    Neha Chaudhary


    Full Text Available Regression Testing is a performed to ensure modified code does not have any unintended side effect on the software. If regression testing is performed with retest-all method it will be very time consuming as testing activity. Therefore test suite reduction methods are used to reduce the size of original test suite. Objective of test suite reduction is to reduce those test cases which are redundant or less important in their fault revealing capability. Test suite reduction can only be used when time is critical to run all test cases and selective testing can only be done. Various methods exist in the literature related to test suite reduction of traditional software. Most of the methods are based of single objective optimization. In case of multi objective optimization of test suite, usually researchers assign different weight values to different objectives and combine them as single objective. However in test suite reduction multiple Pareto-optimal solutions are present, it is difficult to select one test case over other. Since GUI based software is our concern there exist very few reduction techniques and none of them consider multiple objective based reduction. In this work we propose a new test suite reduction technique based on two objectives, event weight and number of faults identified by test case. We evaluated our results for 2 different applications and we achieved 20% reduction in test suite size for both applications. In Terp Paint 3.0 application compromise 15.6% fault revealing capability and for Notepad 11.1% fault revealing capability is reduced.

  20. A hospital information system based on Common Object Request Broker Architecture (CORBA) for exchanging distributed medical objects--an approach to future environment of sharing healthcare information.

    Ohe, K


    Tightly related subsystems in a HIS have to exchange medical data flexibly by the data object rather than by the battery of the data. We developed a CPR subsystem based on Common Object Request Broker Architecture (CORBA) that retrieves and stores clinical information in the object-oriented database via Internet Intra-ORB Protocol (IIOP). The system is hybridized with the legacy HIS applications on the client terminals. We believe that our solution and the experiences will contribute to the future CORBA-based environment in which computerized patient information is shared among hospitals, clinics, and tightly related systems.

  1. A multiple-point spatially weighted k-NN method for object-based classification

    Tang, Yunwei; Jing, Linhai; Li, Hui; Atkinson, Peter M.


    Object-based classification, commonly referred to as object-based image analysis (OBIA), is now commonly regarded as able to produce more appealing classification maps, often of greater accuracy, than pixel-based classification and its application is now widespread. Therefore, improvement of OBIA using spatial techniques is of great interest. In this paper, multiple-point statistics (MPS) is proposed for object-based classification enhancement in the form of a new multiple-point k-nearest neighbour (k-NN) classification method (MPk-NN). The proposed method first utilises a training image derived from a pre-classified map to characterise the spatial correlation between multiple points of land cover classes. The MPS borrows spatial structures from other parts of the training image, and then incorporates this spatial information, in the form of multiple-point probabilities, into the k-NN classifier. Two satellite sensor images with a fine spatial resolution were selected to evaluate the new method. One is an IKONOS image of the Beijing urban area and the other is a WorldView-2 image of the Wolong mountainous area, in China. The images were object-based classified using the MPk-NN method and several alternatives, including the k-NN, the geostatistically weighted k-NN, the Bayesian method, the decision tree classifier (DTC), and the support vector machine classifier (SVM). It was demonstrated that the new spatial weighting based on MPS can achieve greater classification accuracy relative to the alternatives and it is, thus, recommended as appropriate for object-based classification.

  2. Region-Based Image Retrieval Using an Object Ontology and Relevance Feedback

    Kompatsiaris Ioannis


    Full Text Available An image retrieval methodology suited for search in large collections of heterogeneous images is presented. The proposed approach employs a fully unsupervised segmentation algorithm to divide images into regions and endow the indexing and retrieval system with content-based functionalities. Low-level descriptors for the color, position, size, and shape of each region are subsequently extracted. These arithmetic descriptors are automatically associated with appropriate qualitative intermediate-level descriptors, which form a simple vocabulary termed object ontology. The object ontology is used to allow the qualitative definition of the high-level concepts the user queries for (semantic objects, each represented by a keyword and their relations in a human-centered fashion. When querying for a specific semantic object (or objects, the intermediate-level descriptor values associated with both the semantic object and all image regions in the collection are initially compared, resulting in the rejection of most image regions as irrelevant. Following that, a relevance feedback mechanism, based on support vector machines and using the low-level descriptors, is invoked to rank the remaining potentially relevant image regions and produce the final query results. Experimental results and comparisons demonstrate, in practice, the effectiveness of our approach.

  3. Class-Based Garbage Collection in Object-Oriented Programming Environments

    张武生; 黄启峰; 沈美明; 郑纬民


    Many garbage collection algorithms have been proposed, but few address the special needs of long-running server programs. Server applications usually run for years and spawn many threads, so they create and discard thousands of objects. Therefore, efficient garbage collection is especially important for those applications. This paper presents a class-based garbage collector for object-oriented programming environments that classifies objects by their types to achieve better gradualness. Grouping objects of the same type into a group, with a limited type-lock, a mutator cache and the lease protocol will reduce memory fragmentation, which is especially important for the efficiency of long-running server applications. This class-based collector partitions the heap space by type, which provides better concurrency than the traditional mark-sweep collector, and its reusable garbaged object pool also reduces the object allocation overhead. This paper also discusses the implementation details, such as the mutator cache and the lease protocol, and techniques to achieve better accuracy.

  4. A Fast Feature Points-Based Object Tracking Method for Robot Grasp

    Yang Yang


    Full Text Available In this paper, we propose a fast feature points‐based object tracking method for robot grasp. In the detection phase, we detect the object with SIFT feature points extraction and matching. Then we compute the object’s image position with homography constraints and set up an interest window to accommodate the object. In the tracking phase, we only focus on the interest window, detecting feature points from the window and updating the window’s position and size. Our method is of special practical meaning in the case of service robot grasp. Because when the robot grasps the object, the object’s image size is usually small relative to the whole image, it is unnecessary to detect the whole image. On the other hand, the object is partially occluded by the robot gripper. SIFT is good at dealing with occlusion, but it is time consuming. Hence, by combining SIFT and an interest window, our method gains the ability to deal with occlusion and can satisfy the real‐time requirements at the same time. Experiments show that our method exceeds several leading feature points‐based object tracking methods in real‐time performance.

  5. Reweighted mass center based object-oriented sparse subspace clustering for hyperspectral images

    Zhai, Han; Zhang, Hongyan; Zhang, Liangpei; Li, Pingxiang


    Considering the inevitable obstacles faced by the pixel-based clustering methods, such as salt-and-pepper noise, high computational complexity, and the lack of spatial information, a reweighted mass center based object-oriented sparse subspace clustering (RMC-OOSSC) algorithm for hyperspectral images (HSIs) is proposed. First, the mean-shift segmentation method is utilized to oversegment the HSI to obtain meaningful objects. Second, a distance reweighted mass center learning model is presented to extract the representative and discriminative features for each object. Third, assuming that all the objects are sampled from a union of subspaces, it is natural to apply the SSC algorithm to the HSI. Faced with the high correlation among the hyperspectral objects, a weighting scheme is adopted to ensure that the highly correlated objects are preferred in the procedure of sparse representation, to reduce the representation errors. Two widely used hyperspectral datasets were utilized to test the performance of the proposed RMC-OOSSC algorithm, obtaining high clustering accuracies (overall accuracy) of 71.98% and 89.57%, respectively. The experimental results show that the proposed method clearly improves the clustering performance with respect to the other state-of-the-art clustering methods, and it significantly reduces the computational time.

  6. Automatic Segmentation of Nature Object Using Salient Edge Points Based Active Contour

    Shangbing Gao


    Full Text Available Natural image segmentation is often a crucial first step for high-level image understanding, significantly reducing the complexity of content analysis of images. LRAC may have some disadvantages. (1 Segmentation results heavily depend on the initial contour selection which is a very skillful task. (2 In some situations, manual interactions are infeasible. To overcome these shortcomings, we propose a novel model for unsupervised segmentation of viewer’s attention object from natural images based on localizing region-based active model (LRAC. With aid of the color boosting Harris detector and the core saliency map, we get the salient object edge points. Then, these points are employed as the seeds of initial convex hull. Finally, this convex hull is improved by the edge-preserving filter to generate the initial contour for our automatic object segmentation system. In contrast with localizing region-based active contours that require considerable user interaction, the proposed method does not require it; that is, the segmentation task is fulfilled in a fully automatic manner. Extensive experiments results on a large variety of natural images demonstrate that our algorithm consistently outperforms the popular existing salient object segmentation methods, yielding higher precision and better recall rates. Our framework can reliably and automatically extract the object contour from the complex background.

  7. The role of surface-based representations of shape in visual object recognition.

    Reppa, Irene; Greville, W James; Leek, E Charles


    This study contrasted the role of surfaces and volumetric shape primitives in three-dimensional object recognition. Observers (N = 50) matched subsets of closed contour fragments, surfaces, or volumetric parts to whole novel objects during a whole-part matching task. Three factors were further manipulated: part viewpoint (either same or different between component parts and whole objects), surface occlusion (comparison parts contained either visible surfaces only, or a surface that was fully or partially occluded in the whole object), and target-distractor similarity. Similarity was varied in terms of systematic variation in nonaccidental (NAP) or metric (MP) properties of individual parts. Analysis of sensitivity (d') showed a whole-part matching advantage for surface-based parts and volumes over closed contour fragments--but no benefit for volumetric parts over surfaces. We also found a performance cost in matching volumetric parts to wholes when the volumes showed surfaces that were occluded in the whole object. The same pattern was found for both same and different viewpoints, and regardless of target-distractor similarity. These findings challenge models in which recognition is mediated by volumetric part-based shape representations. Instead, we argue that the results are consistent with a surface-based model of high-level shape representation for recognition.

  8. Full-viewpoint 3D Space Object Recognition Based on Kernel Locality Preserving Projections

    Meng Gang; Jiang Zhiguo; Liu Zhengyi; Zhang Haopeng; Zhao Danpei


    Space object recognition plays an important role in spatial exploitation and surveillance,followed by two main problems:lacking of data and drastic changes in viewpoints.In this article,firstly,we build a three-dimensional (3D) satellites dataset named BUAA Satellite Image Dataset (BUAA-SID 1.0) to supply data for 3D space object research.Then,based on the dataset,we propose to recognize full-viewpoint 3D space objects based on kemel locality preserving projections (KLPP).To obtain more accurate and separable description of the objects,firstly,we build feature vectors employing moment invariants,Fourier descriptors,region covariance and histogram of oriented gradients.Then,we map the features into kernel space followed by dimensionality reduction using KLPP to obtain the submanifold of the features.At last,k-nearest neighbor (kNN) is used to accomplish the classification.Experimental results show that the proposed approach is more appropriate for space object recognition mainly considering changes of viewpoints.Encouraging recognition rate could be obtained based on images in BUAA-SID 1.0,and the highest recognition result could achieve 95.87%.

  9. [Location selection for Shenyang urban parks based on GIS and multi-objective location allocation model].

    Zhou, Yuan; Shi, Tie-Mao; Hu, Yuan-Man; Gao, Chang; Liu, Miao; Song, Lin-Qi


    Based on geographic information system (GIS) technology and multi-objective location-allocation (LA) model, and in considering of four relatively independent objective factors (population density level, air pollution level, urban heat island effect level, and urban land use pattern), an optimized location selection for the urban parks within the Third Ring of Shenyang was conducted, and the selection results were compared with the spatial distribution of existing parks, aimed to evaluate the rationality of the spatial distribution of urban green spaces. In the location selection of urban green spaces in the study area, the factor air pollution was most important, and, compared with single objective factor, the weighted analysis results of multi-objective factors could provide optimized spatial location selection of new urban green spaces. The combination of GIS technology with LA model would be a new approach for the spatial optimizing of urban green spaces.

  10. [A method of object detection for remote sensing-imagery based on spectral space transformation].

    Wu, Gui-Ping; Xiao, Peng-Feng; Feng, Xue-Zhi; Wang, Ke


    Object detection is an intermediate link for remote sensing image processing, which is an important guarantee of remote sensing application and services aspects. In view of the characteristics of remotely sensed imagery in frequency domain, a novel object detection algorithm based on spectral space transformation was proposed in the present paper. Firstly, the Fourier transformation method was applied to transform the image in spatial domain into frequency domain. Secondly, the wedge-shaped sample and overlay analysis methods for frequency energy were used to decompose signal into different frequency spectrum zones, and the center frequency values of object's features were acquired as detection marks in frequency domain. Finally, object information was detected with the matched Gabor filters which have direction and frequency selectivity. The results indicate that the proposed algorithm here performs better and it has good detection capability in specific direction as well.

  11. Cascade Boosting-Based Object Detection from High-Level Description to Hardware Implementation

    Khattab K


    Full Text Available Object detection forms the first step of a larger setup for a wide variety of computer vision applications. The focus of this paper is the implementation of a real-time embedded object detection system while relying on high-level description language such as SystemC. Boosting-based object detection algorithms are considered as the fastest accurate object detection algorithms today. However, the implementation of a real time solution for such algorithms is still a challenge. A new parallel implementation, which exploits the parallelism and the pipelining in these algorithms, is proposed. We show that using a SystemC description model paired with a mainstream automatic synthesis tool can lead to an efficient embedded implementation. We also display some of the tradeoffs and considerations, for this implementation to be effective. This implementation proves capable of achieving 42 fps for images as well as bringing regularity in time consuming.

  12. Multi-Objective Optimization Algorithms Design based on Support Vector Regression Metamodeling

    Qi Zhang


    Full Text Available In order to solve the multi-objective optimization problem in the complex engineering, in this paper a NSGA-II multi-objective optimization algorithms based on Support Vector Regression Metamodeling is presented. Appropriate design parameter samples are selected by experimental design theories, and the response samples are obtained from the experiments or numerical simulations, used the SVM method to establish the metamodels of the objective performance functions and constraints, and reconstructed the original optimal problem. The reconstructed metamodels was solved by NSGA-II algorithm and took the structure optimization of the microwave power divider as an example to illustrate the proposed methodology and solve themulti-objective optimization problem. The results show that this methodology is feasible and highly effective, and thus it can be used in the optimum design of engineering fields.

  13. D Modelling and Interactive Web-Based Visualization of Cultural Heritage Objects

    Koeva, M. N.


    Nowadays, there are rapid developments in the fields of photogrammetry, laser scanning, computer vision and robotics, together aiming to provide highly accurate 3D data that is useful for various applications. In recent years, various LiDAR and image-based techniques have been investigated for 3D modelling because of their opportunities for fast and accurate model generation. For cultural heritage preservation and the representation of objects that are important for tourism and their interactive visualization, 3D models are highly effective and intuitive for present-day users who have stringent requirements and high expectations. Depending on the complexity of the objects for the specific case, various technological methods can be applied. The selected objects in this particular research are located in Bulgaria - a country with thousands of years of history and cultural heritage dating back to ancient civilizations. This motivates the preservation, visualisation and recreation of undoubtedly valuable historical and architectural objects and places, which has always been a serious challenge for specialists in the field of cultural heritage. In the present research, comparative analyses regarding principles and technological processes needed for 3D modelling and visualization are presented. The recent problems, efforts and developments in interactive representation of precious objects and places in Bulgaria are presented. Three technologies based on real projects are described: (1) image-based modelling using a non-metric hand-held camera; (2) 3D visualization based on spherical panoramic images; (3) and 3D geometric and photorealistic modelling based on architectural CAD drawings. Their suitability for web-based visualization are demonstrated and compared. Moreover the possibilities for integration with additional information such as interactive maps, satellite imagery, sound, video and specific information for the objects are described. This comparative study


    M. N. Koeva


    Full Text Available Nowadays, there are rapid developments in the fields of photogrammetry, laser scanning, computer vision and robotics, together aiming to provide highly accurate 3D data that is useful for various applications. In recent years, various LiDAR and image-based techniques have been investigated for 3D modelling because of their opportunities for fast and accurate model generation. For cultural heritage preservation and the representation of objects that are important for tourism and their interactive visualization, 3D models are highly effective and intuitive for present-day users who have stringent requirements and high expectations. Depending on the complexity of the objects for the specific case, various technological methods can be applied. The selected objects in this particular research are located in Bulgaria – a country with thousands of years of history and cultural heritage dating back to ancient civilizations. \\this motivates the preservation, visualisation and recreation of undoubtedly valuable historical and architectural objects and places, which has always been a serious challenge for specialists in the field of cultural heritage. In the present research, comparative analyses regarding principles and technological processes needed for 3D modelling and visualization are presented. The recent problems, efforts and developments in interactive representation of precious objects and places in Bulgaria are presented. Three technologies based on real projects are described: (1 image-based modelling using a non-metric hand-held camera; (2 3D visualization based on spherical panoramic images; (3 and 3D geometric and photorealistic modelling based on architectural CAD drawings. Their suitability for web-based visualization are demonstrated and compared. Moreover the possibilities for integration with additional information such as interactive maps, satellite imagery, sound, video and specific information for the objects are described. This

  15. Personalised learning object based on multi-agent model and learners’ learning styles

    Noppamas Pukkhem


    Full Text Available A multi-agent model is proposed in which learning styles and a word analysis technique to create a learning object recommendation system are used. On the basis of a learning style-based design, a concept map combination model is proposed to filter out unsuitable learning concepts from a given course. Our learner model classifies learners into eight styles and implements compatible computational methods consisting of three recommendations: i non-personalised, ii preferred feature-based, and iii neighbour-based collaborative filtering. The analysis of preference error (PE was performed by comparing the actual preferred learning object with the predicted one. In our experiments, the feature-based recommendation algorithm has the fewest PE.

  16. Automated detection of lunar craters based on object-oriented approach

    YUE ZongYu; LIU JianZhong; WU GanGuo


    The object-oriented approach is a powerful method in making classification. With the segmentation of images to objects, many features can be calculated based on the objects so that the targets can be distinguished. However, this method has not been applied to lunar study. In this paper we attempt to apply this method to detecting lunar craters with promising results. Craters are the most obvious features on the moon and they are important for lunar geologic study. One of the important questions in lunar research is to estimate lunar surface ages by examination of crater density per unit area. Hence,proper detection of lunar craters is necessary. Manual crater identification is inefficient, and a more efficient and effective method is needed. This paper describes an object-oriented method to detect lunar craters using lunar reflectance images. In the method, many objects were first segmented from the image based on size, shape, color, and the weights to every layer. Then the feature of "contrast to neighbor objects" was selected to identify craters from the lunar image. In the next step, by merging the adjacent objects belonging to the same class, almost every crater can be taken as an independent object except several very big craters in the study area. To remove the crater rays diagnosed as craters,the feature of "length/width" was further used with suitable parameters to finish recognizing craters.Finally, the result was exported to ArcGIS for manual modification to those big craters and the number of craters was acquired.

  17. Object Hierarchy-based Supervised Characterisation ofSynthetic Aperture Radar Sensor Images

    Ish Rishabh


    Full Text Available A method of supervised characterisation of synthetic aperture radar (SAR satellite imageshas been discussed in which simple object shape features of satellite images have been usedto classify and describe the terrain types. This scheme is based on a multilevel approach inwhich objects of interest are first segmented out from the image and subsequently characterisedbased on their shape features. Once all objects have been characterised, the entire image canbe characterised. Emphasis has been laid on the hierarchical information extraction from theimage which enables greater flexibility in characterising the image and is not restricted to mereclassification. The paper also describes a method for giving relative importance among features,i.e., to give more weights to those features that are better than others in distinguishing betweencompeting classes. A method of comparing two SAR sensor images based on terrain elementspresent in the images has also been described here.

  18. Geographic object-based delineation of neighborhoods of Accra, Ghana using QuickBird satellite imagery.

    Stow, Douglas A; Lippitt, Christopher D; Weeks, John R


    The objective was to test GEographic Object-based Image Analysis (GEOBIA) techniques for delineating neighborhoods of Accra, Ghana using QuickBird multispectral imagery. Two approaches to aggregating census enumeration areas (EAs) based on image-derived measures of vegetation objects were tested: (1) merging adjacent EAs according to vegetation measures and (2) image segmentation. Both approaches exploit readily available functions within commercial GEOBIA software. Image-derived neighborhood maps were compared to a reference map derived by spatial clustering of slum index values (from census data), to provide a relative assessment of potential map utility. A size-constrained iterative segmentation approach to aggregation was more successful than standard image segmentation or feature merge techniques. The segmentation approaches account for size and shape characteristics, enabling more realistic neighborhood boundaries to be delineated. The percentage of vegetation patches within each EA yielded more realistic delineation of potential neighborhoods than mean vegetation patch size per EA.

  19. Object-Based Forest Cover Monitoring Using GAOFEN-2 High Resolution Satellite Images

    Li, S. M.; Li, Z. Y.; Chen, E. X.; Liu, Q. W.


    Forest cover monitoring is an important part of forest management in local or regional area. The structure and tones of forest can be identified in high spatial remote sensing images. When forests cover change, the spectral characteristics of forests is also changed. In this paper a method on object-based forest cover monitoring with data transformation from time series of high resolution images is put forward. First the NDVI difference image and the composite of PC3,PC4, PC5 of the stacked 8 layers of time series of high resolution satellites are segmented into homogeneous objects. With development of the object-based ruleset classification system, the spatial extent of deforestation and afforestation can be identified over time across the landscape. Finally the change accuracy is achieved with reference data.

  20. Comparison of Pixel-Based and Object-Oriented Land Cover Change Detection Methods

    Xie, Zhenlei; Shi, Ruoming; Zhu, Ling; Peng, Shu; Chen, Xu


    Change detection method is an efficient way in the aim of land cover product updating on the basis of the existing products, and at the same time saving lots of cost and time. Considering the object-oriented change detection method for 30m resolution Landsat image, analysis of effect of different segmentation scales on the method of the object-oriented is firstly carried out. On the other hand, for analysing the effectiveness and availability of pixel-based change method, the two indices which complement each other are the differenced Normalized Difference Vegetation Index (dNDVI), the Change Vector (CV) were used. To demonstrate the performance of pixel-based and object-oriented, accuracy assessment of these two change detection results will be conducted by four indicators which include overall accuracy, omission error, commission error and Kappa coefficient.

  1. Vision based Object Recognition of E-Puck Mobile Robot for Warehouse Application

    Mehmet S. Guzel


    Full Text Available At present, most warehouses still require human services for unloading of goods. Unloading of goods requires a continuous system to ensure the quality of work productivity. Therefore the need of autonomous robot system in warehouse is needed to improve the quality of work. Thus, a localization and recognition algorithm is developed and implemented on the E-puck robot. The task involves the recognition of desired object based on their colour (red and blue and locating the desired object to the target location (marked by green marker. In addition, the collision avoidance algorithm is also developed and integrated to allow the robot manoeuvre safely in its working environment. The colour histogram technique is used to recognize the desired object and the target location. Based on the experimental results, the developed algorithm is successfully fulfilling the pick and place requirement with success rate of approximately 70% in simulation study and 50% in real implementation.

  2. Costs and Advantages of Object-Based Image Coding with Shape-Adaptive Wavelet Transform

    Cagnazzo Marco


    Full Text Available Object-based image coding is drawing a great attention for the many opportunities it offers to high-level applications. In terms of rate-distortion performance, however, its value is still uncertain, because the gains provided by an accurate image segmentation are balanced by the inefficiency of coding objects of arbitrary shape, with losses that depend on both the coding scheme and the object geometry. This work aims at measuring rate-distortion costs and gains for a wavelet-based shape-adaptive encoder similar to the shape-adaptive texture coder adopted in MPEG-4. The analysis of the rate-distortion curves obtained in several experiments provides insight about what performance gains and losses can be expected in various operative conditions and shows the potential of such an approach for image coding.

  3. Building and Programming a Smart Robotic System for Distinguishing Objects Based on their Shape and Colour

    Sharari, T. M.


    This paper presents a robotic system designed for holding and placing objects based on their colour and shape. The presented robot is given a complete set of instructions of positions and orientation angles for each manipulation motion. The main feature in this paper is that the developed robot used a combination of vision and motion systems for holding and placing the work-objects, mounted on the flat work-plane, based on their shapes and colors. This combination improves the flexibility of manipulation which may help eliminate the use of some expensive manipulation tasks in a variety of industrial applications. The robotic system presented in this paper is designed as an educational robot that possesses the ability for holding-and-placing operations with limited load. To process the various instructions for holding and placing the work objects, a main control unit - Manipulation Control Unit (MCU) is used as well as a slave unit that performed the actual instructions from the MCU.

  4. A Machine Learning based Efficient Software Reusability Prediction Model for Java Based Object Oriented Software

    Surbhi Maggo; Chetna Gupta


    Software reuse refers to the development of new software systems with the likelihood of completely or partially using existing components or resources with or without modification. Reusability is the measure of the ease with which previously acquired concepts and objects can be used in new contexts. It is a promising strategy for improvements in software quality, productivity and maintainability as it provides for cost effective, reliable (with the consideration that prior testing and use has...

  5. Development of E-Mart Based on Object-Oriented Databases (OODB)

    SUN Li; WANG Li; LIU Hao-shuang


    Focusing on the development of electronic-mart (e-mart)based on object-oriented databases (OODB), the concepts of integrated electronic-commerce (ecommerce) environment and e-mart are introduced, and the basic characteristics of OODB Jasmine are described.In addition, the database mode and hierarchy of e-mart are discussed in derail.

  6. Development of Management System for Regional Pollution Source Based on SuperMap Objects


    Based on the integration of and SuperMap Objects(tool software of component GIS),the management system of regional pollution source is developed.It mainly includes the demand analysis of system,function design,database construction,program design and concrete realization in the management aspect of pollution source.

  7. Learning Mathematics by Designing, Programming, and Investigating with Interactive, Dynamic Computer-Based Objects

    Marshall, Neil; Buteau, Chantal


    As part of their undergraduate mathematics curriculum, students at Brock University learn to create and use computer-based tools with dynamic, visual interfaces, called Exploratory Objects, developed for the purpose of conducting pure or applied mathematical investigations. A student's Development Process Model of creating and using an Exploratory…

  8. Geomorphological change detection using object-based feature extraction from multi-temporal LIDAR data

    Seijmonsbergen, A.C.; Anders, N.S.; Bouten, W.; Feitosa, R.Q.; da Costa, G.A.O.P.; de Almeida, C.M.; Fonseca, L.M.G.; Kux, H.J.H.


    Multi-temporal LiDAR DTMs are used for the development and testing of a method for geomorphological change analysis in western Austria. Our test area is located on a mountain slope in the Gargellen Valley in western Austria. Six geomorphological features were mapped by using stratified Object-Based

  9. Model-based Type B uncertainty evaluations of measurement towards more objective evaluation strategies

    M. Boumans


    This article proposes a more objective Type B evaluation. This can be achieved when Type B uncertainty evaluations are model-based. This implies, however, grey-box modelling and validation instead of white-box modelling and validation which are appropriate for Type A evaluation.

  10. Flexibility on storage-release based distributed hydrologic modeling with object-oriented approach

    Kang, Kwangmin; Merwade, Venkatesh; Chun, Jong Ahn; Timlin, Dennis


    With the availability of advanced hydrologic data in public domain such as remote sensed and climate change scenario data, there is a need for a modeling framework that is capable of using these data to simulate and extend hydrologic processes with multidisciplinary approaches for sustainable water resources management. To address this need, a storage-release based distributed hydrologic model (STORE DHM) is developed based on an object-oriented approach. The model is tested for demonstrating model flexibility and extensibility to know how to well integrate object-oriented approach to further hydrologic research issues, e.g., reconstructing missing precipitation in this study, without changing its main frame. Moreover, the STORE DHM is applied to simulate hydrological processes with multiple classes in the Nanticoke watershed. This study also describes a conceptual and structural framework of object-oriented inheritance and aggregation characteristics under the STORE DHM. In addition, NearestMP (missing value estimation based on nearest neighborhood regression) and KernelMP (missing value estimation based on Kernel Function) are proposed for evaluating STORE DHM flexibility. And then, STORE DHM runoff hydrographs compared with NearestMP and KernelMP runoff hydrographs. Overall results from these comparisons show promising hydrograph outputs generated by the proposed two classes. Consequently, this study suggests that STORE DHM with an object-oriented approach will be a comprehensive water resources modeling tools by adding additional classes for toward developing through its flexibility and extensibility.

  11. An operational framework for object-based land use classification of heterogeneous rural landscapes

    Watmough, Gary Richard; Palm, Cheryl; Sullivan, Clare


    and transferable land use classification definitions and algorithms. We present an operational framework for classifying VHR satellite data in heterogeneous rural landscapes using an object-based and random forest classifier. The framework overcomes the challenges of classifying VHR data in anthropogenic...

  12. Optimizing object-based image analysis for semi-automated geomorphological mapping

    Anders, N.; Smith, M.; Seijmonsbergen, H.; Bouten, W.; Hengl, T.; Evans, I.S.; Wilson, J.P.; Gould, M.


    Object-Based Image Analysis (OBIA) is considered a useful tool for analyzing high-resolution digital terrain data. In the past, both segmentation and classification parameters were optimized manually by trial and error. We propose a method to automatically optimize classification parameters for incr

  13. A general simulation model developing process based on five-object framework

    胡安斌; 伞冶; 陈建明; 陈永强


    Different paradigms that relate verification and validation to the simulation model have different development process. A simulation model developing process based on Five-Object Framework (FOF) is discussed in this paper. An example is given to demonstrate the applications of the proposed method.

  14. Competency-Based Training: Objective Structured Clinical Exercises (OSCE) in Marriage and Family Therapy

    Miller, John K.


    The field of marriage and family therapy (MFT) has recently engaged in the process of defining core competencies for the profession. Many MFT training programs are adapting their curriculum to develop more competency-based training strategies. The Objective Structured Clinical "Examination" (OSCE) is widely used in the medical profession to assess…

  15. Developing a framework for monitoring coastal habitats using aerial imagery and object-based image analysis

    Juel, Anders

    of decreased habitat dynamics exists. A valuable source of land cover changes are historical aerial imagery of which Denmark has unique datasets.This poster presents an object-based image analysis approach for mapping and monitoring af coastal habitat stucture, which integrates the high spectral resolution...

  16. Frequency Affects Object Relative Clause Processing: Some Evidence in Favor of Usage-Based Accounts

    Reali, Florencia


    The processing difficulty of nested grammatical structure has been explained by different psycholinguistic theories. Here I provide corpus and behavioral evidence in favor of usage-based models, focusing on the case of object relative clauses in Spanish as a first language. A corpus analysis of spoken Spanish reveals that, as in English, the…

  17. Extracting Superquadric-based Geon Description for 3D Object Recognition

    XINGWeiwei; LIUWeibin; YUANBaozong


    Geons recognition is one key issue in developing 3D object recognition system based on Recognition by components (RBC) theory. In this paper, we present a novel approach for extracting superquadric-based geon description of 3D volumetric primitives from real shape data, which integrates the advantages of deformable superquadric models reconstruction and SVM-based classification. First, Real-coded genetic algorithm (RCGA) is used for superquadric fitting to 3D data and the quantitative parametric information is obtained; then a new sophisticated feature set is derived from superquadric parameters obtained for the next step; and SVM-based classification is proposed and implemented for geons recognition and the qualitative geometric information is obtained. Furthermore, the knowledge-based feedback of SVM network is introduced for improving the classification performance. Ex-perimental results obtained show that our approach is efficient and precise for extracting superquadric-based geon description from real shape data in 3D object recognition. The results are very encouraging and have significant benefit for developing the general 3D object recognition system.

  18. Hyperspectral Data for Mangrove Species Mapping: A Comparison of Pixel-Based and Object-Based Approach

    Muhammad Kamal


    Full Text Available Visual image interpretation and digital image classification have been used to map and monitor mangrove extent and composition for decades. The presence of a high-spatial resolution hyperspectral sensor can potentially improve our ability to differentiate mangrove species. However, little research has explored the use of pixel-based and object-based approaches on high-spatial hyperspectral datasets for this purpose. This study assessed the ability of CASI-2 data for mangrove species mapping using pixel-based and object-based approaches at the mouth of the Brisbane River area, southeast Queensland, Australia. Three mapping techniques used in this study: spectral angle mapper (SAM and linear spectral unmixing (LSU for the pixel-based approaches, and multi-scale segmentation for the object-based image analysis (OBIA. The endmembers for the pixel-based approach were collected based on existing vegetation community map. Nine targeted classes were mapped in the study area from each approach, including three mangrove species: Avicennia marina, Rhizophora stylosa, and Ceriops australis. The mapping results showed that SAM produced accurate class polygons with only few unclassified pixels (overall accuracy 69%, Kappa 0.57, the LSU resulted in a patchy polygon pattern with many unclassified pixels (overall accuracy 56%, Kappa 0.41, and the object-based mapping produced the most accurate results (overall accuracy 76%, Kappa 0.67. Our results demonstrated that the object-based approach, which combined a rule-based and nearest-neighbor classification method, was the best classifier to map mangrove species and its adjacent environments.

  19. Object-oriented change detection based on weighted polarimetric scattering differences on POLSAR images

    Shi, X.; Lu, L.; Yang, S.; Huang, G.; Zhao, Z.


    For wide application of change detection with SAR imagery, current processing technologies and methods are mostly based on pixels. It is difficult for pixel-based technologies to utilize spatial characteristics of images and topological relations of objects. Object-oriented technology takes objects as processing unit, which takes advantage of the shape and texture information of image. It can greatly improve the efficiency and reliability of change detection. Recently, with the development of polarimetric synthetic aperture radar (PolSAR), more backscattering features on different polarization state can be available for usage of object-oriented change detection study. In this paper, the object-oriented strategy will be employed. Considering the fact that the different target or target's state behaves different backscattering characteristics dependent on polarization state, an object-oriented change detection method that based on weighted polarimetric scattering difference of PolSAR images is proposed. The method operates on the objects generated by generalized statistical region merging (GSRM) segmentation processing. The merit of GSRM method is that image segmentation is executed on polarimetric coherence matrix, which takes full advantages of polarimetric backscattering features. And then, the measurement of polarimetric scattering difference is constructed by combining the correlation of covariance matrix and the difference of scattering power. Through analysing the effects of the covariance matrix correlation and the scattering echo power difference on the polarimetric scattering difference, the weighted method is used to balance the influences caused by the two parts, so that more reasonable weights can be chosen to decrease the false alarm rate. The effectiveness of the algorithm that proposed in this letter is tested by detection of the growth of crops with two different temporal radarsat-2 fully PolSAR data. First, objects are produced by GSRM algorithm

  20. An event-based neurobiological recognition system with orientation detector for objects in multiple orientations

    Hanyu Wang


    Full Text Available A new multiple orientation event-based neurobiological recognition system is proposed by integrating recognition and tracking function in this paper, which is used for asynchronous address-event representation (AER image sensors. The characteristic of this system has been enriched to recognize the objects in multiple orientations with only training samples moving in a single orientation. The system extracts multi-scale and multi-orientation line features inspired by models of the primate visual cortex. An orientation detector based on modified Gaussian blob tracking algorithm is introduced for object tracking and orientation detection. The orientation detector and feature extraction block work in simultaneous mode, without any increase in categorization time. An addresses lookup table (addresses LUT is also presented to adjust the feature maps by addresses mapping and reordering, and they are categorized in the trained spiking neural network. This recognition system is evaluated with the MNIST dataset which have played important roles in the development of computer vision, and the accuracy is increase owing to the use of both ON and OFF events. AER data acquired by a DVS are also tested on the system, such as moving digits, pokers, and vehicles. The experimental results show that the proposed system can realize event-based multi-orientation recognition.The work presented in this paper makes a number of contributions to the event-based vision processing system for multi-orientation object recognition. It develops a new tracking-recognition architecture to feedforward categorization system and an address reorder approach to classify multi-orientation objects using event-based data. It provides a new way to recognize multiple orientation objects with only samples in single orientation.

  1. Review of Remotely Sensed Imagery Classification Patterns Based on Object-oriented Image Analysis

    LIU Yongxue; LI Manchun; MAO Liang; XU Feifei; HUANG Shuo


    With the wide use of high-resolution remotely sensed imagery, the object-oriented remotely sensed information classification pattern has been intensively studied. Starting with the definition of object-oriented remotely sensed information classification pattern and a literature review of related research progress, this paper sums up 4 developing phases of object-oriented classification pattern during the past 20 years. Then, we discuss the three aspects of methodology in detail, namely remotely sensed imagery segmentation, feature analysis and feature selection, and classification rule generation, through comparing them with remotely sensed information classification method based on per-pixel. At last, this paper presents several points that need to be paid attention to in the future studies on object-oriented RS information classification pattern: 1) developing robust and highly effective image segmentation algorithm for multi-spectral RS imagery; 2) improving the feature-set including edge, spatial-adjacent and temporal characteristics; 3) discussing the classification rule generation classifier based on the decision tree; 4) presenting evaluation methods for classification result by object-oriented classification pattern.

  2. Objective video quality assessment method for freeze distortion based on freeze aggregation

    Watanabe, Keishiro; Okamoto, Jun; Kurita, Takaaki


    With the development of the broadband network, video communications such as videophone, video distribution, and IPTV services are beginning to become common. In order to provide these services appropriately, we must manage them based on subjective video quality, in addition to designing a network system based on it. Currently, subjective quality assessment is the main method used to quantify video quality. However, it is time-consuming and expensive. Therefore, we need an objective quality assessment technology that can estimate video quality from video characteristics effectively. Video degradation can be categorized into two types: spatial and temporal. Objective quality assessment methods for spatial degradation have been studied extensively, but methods for temporal degradation have hardly been examined even though it occurs frequently due to network degradation and has a large impact on subjective quality. In this paper, we propose an objective quality assessment method for temporal degradation. Our approach is to aggregate multiple freeze distortions into an equivalent freeze distortion and then derive the objective video quality from the equivalent freeze distortion. Specifically, our method considers the total length of all freeze distortions in a video sequence as the length of the equivalent single freeze distortion. In addition, we propose a method using the perceptual characteristics of short freeze distortions. We verified that our method can estimate the objective video quality well within the deviation of subjective video quality.

  3. Intelligent Video Object Classification Scheme using Offline Feature Extraction and Machine Learning based Approach

    Chandra Mani Sharma


    Full Text Available Classification of objects in video stream is important because of its application in many emerging areas such as visual surveillance, content based video retrieval and indexing etc. The task is far more challenging because the video data is of heavy and highly variable nature. The processing of video data is required to be in real-time. This paper presents a multiclass object classification technique using machine learning approach. Haar-like features are used for training the classifier. The feature calculation is performed using Integral Image representation and we train the classifier offline using a Stage-wise Additive Modeling using a Multiclass Exponential loss function (SAMME. The validity of the method has been verified from the implementation of a real-time human-car detector. Experimental results show that the proposed method can accurately classify objects, in video, into their respective classes. The proposed object classifier works well in outdoor environment in presence of moderate lighting conditions and variable scene background. The proposed technique is compared, with other object classification techniques, based on various performance parameters.

  4. Autonomic Management of Object Replication for FT-CORBA Based Intelligent Transportation Systems

    Suh, Woonsuk; Lee, Eunseok

    Intelligent Transportation Systems (ITS) comprises the electronics, communications or information processing used singly or integrated to improve the efficiency or safety of surface transportation. Accordingly, the ITS has to perform collection, management, and provision of real time transport information reliably. It can be deployed based on the Common Object Request Broker Architecture (CORBA) of the Object Management Group (OMG) because it consists of many interconnected heterogeneous systems deployed by independent organizations. Fault Tolerant CORBA (FT-CORBA) supports real time requirement of transport information stably through redundancy by replication of server objects. However, object replication, management, and related protocols of FT-CORBA require extra system resources of CPU and memory, and can degrade the system performance both locally and as a whole. This paper proposes an architecture to enhance performance of FT-CORBA based ITS in terms of CPU and memory by managing object replication adaptively during system operation with an agent. The application of the agent is expected to support fault tolerance of real ITS efficiently.

  5. Insomnia Phenotypes Based on Objective Sleep Duration in Adolescents: Depression Risk and Differential Behavioral Profiles

    Julio Fernandez-Mendoza


    Full Text Available Based on previous studies on the role of objective sleep duration in predicting morbidity in individuals with insomnia, we examined the role of objective sleep duration in differentiating behavioral profiles in adolescents with insomnia symptoms. Adolescents from the Penn State Child Cohort (n = 397, ages 12–23, 54.7% male underwent a nine-hour polysomnography (PSG, clinical history, physical examination and psychometric testing, including the Child or Adult Behavior Checklist and Pediatric Behavior Scale. Insomnia symptoms were defined as a self-report of difficulty falling and/or staying asleep and objective “short” sleep duration as a PSG total sleep time ≤7 h. A significant interaction showed that objective short sleep duration modified the association of insomnia symptoms with internalizing problems. Consistently, adolescents with insomnia symptoms and short sleep duration were characterized by depression, rumination, mood dysregulation and social isolation, while adolescents with insomnia symptoms and normal sleep duration were characterized by rule-breaking and aggressive behaviors and, to a lesser extent, rumination. These findings indicate that objective sleep duration is useful in differentiating behavioral profiles among adolescents with insomnia symptoms. The insomnia with objective short sleep duration phenotype is associated with an increased risk of depression earlier in the lifespan than previously believed.

  6. A foreground object features-based stereoscopic image visual comfort assessment model

    Jin, Xin; Jiang, G.; Ying, H.; Yu, M.; Ding, S.; Peng, Z.; Shao, F.


    Since stereoscopic images provide observers with both realistic and discomfort viewing experience, it is necessary to investigate the determinants of visual discomfort. By considering that foreground object draws most attention when human observing stereoscopic images. This paper proposes a new foreground object based visual comfort assessment (VCA) metric. In the first place, a suitable segmentation method is applied to disparity map and then the foreground object is ascertained as the one having the biggest average disparity. In the second place, three visual features being average disparity, average width and spatial complexity of foreground object are computed from the perspective of visual attention. Nevertheless, object's width and complexity do not consistently influence the perception of visual comfort in comparison with disparity. In accordance with this psychological phenomenon, we divide the whole images into four categories on the basis of different disparity and width, and exert four different models to more precisely predict its visual comfort in the third place. Experimental results show that the proposed VCA metric outperformance other existing metrics and can achieve a high consistency between objective and subjective visual comfort scores. The Pearson Linear Correlation Coefficient (PLCC) and Spearman Rank Order Correlation Coefficient (SROCC) are over 0.84 and 0.82, respectively.

  7. Object Tracking Using Adaptive Covariance Descriptor and Clustering-Based Model Updating for Visual Surveillance

    Lei Qin


    Full Text Available We propose a novel approach for tracking an arbitrary object in video sequences for visual surveillance. The first contribution of this work is an automatic feature extraction method that is able to extract compact discriminative features from a feature pool before computing the region covariance descriptor. As the feature extraction method is adaptive to a specific object of interest, we refer to the region covariance descriptor computed using the extracted features as the adaptive covariance descriptor. The second contribution is to propose a weakly supervised method for updating the object appearance model during tracking. The method performs a mean-shift clustering procedure among the tracking result samples accumulated during a period of time and selects a group of reliable samples for updating the object appearance model. As such, the object appearance model is kept up-to-date and is prevented from contamination even in case of tracking mistakes. We conducted comparing experiments on real-world video sequences, which confirmed the effectiveness of the proposed approaches. The tracking system that integrates the adaptive covariance descriptor and the clustering-based model updating method accomplished stable object tracking on challenging video sequences.

  8. Properties of resonant trans-Neptunian objects based on Herschel Space Observatory data

    Farkas Anikó, Takácsné; Kiss, Csaba; Mueller, Thomas G.; Mommert, Michael; Vilenius, Esa


    The goal of our work is to characterise the physical characteristics of resonant, detached and scattered disk objects in the trans-Neptunian region, observed in the framework of the "TNOs are Cool!" Herschel Open Time Key Program. Based on thermal emission measurements with the Herschel/PACS and Spitzer/MIPS instruments we were able to determine size, albedo, and surface thermal properties for 23 objects using radiometric modelling techniques. This is the first analysis in which the physical properties of objects in the outer resonances are determined for a larger sample. In addition to the results for individual objects, we have compared these characteristic with the bulk properties of other populations of the trans-Neptunian region. The newly analysed objects show e.g. a large variety of beaming factors, indicating diverse surfaces, and in general they follow the albedo-colour clustering identified earlier for Kuiper belt objects and Centaurs, further strengthening the evidence for a compositional discontinuity in the young solar system.

  9. A bio-inspired method and system for visual object-based attention and segmentation

    Huber, David J.; Khosla, Deepak


    This paper describes a method and system of human-like attention and object segmentation in visual scenes that (1) attends to regions in a scene in their rank of saliency in the image, (2) extracts the boundary of an attended proto-object based on feature contours, and (3) can be biased to boost the attention paid to specific features in a scene, such as those of a desired target object in static and video imagery. The purpose of the system is to identify regions of a scene of potential importance and extract the region data for processing by an object recognition and classification algorithm. The attention process can be performed in a default, bottom-up manner or a directed, top-down manner which will assign a preference to certain features over others. One can apply this system to any static scene, whether that is a still photograph or imagery captured from video. We employ algorithms that are motivated by findings in neuroscience, psychology, and cognitive science to construct a system that is novel in its modular and stepwise approach to the problems of attention and region extraction, its application of a flooding algorithm to break apart an image into smaller proto-objects based on feature density, and its ability to join smaller regions of similar features into larger proto-objects. This approach allows many complicated operations to be carried out by the system in a very short time, approaching real-time. A researcher can use this system as a robust front-end to a larger system that includes object recognition and scene understanding modules; it is engineered to function over a broad range of situations and can be applied to any scene with minimal tuning from the user.

  10. Information extraction with object based support vector machines and vegetation indices

    Ustuner, Mustafa; Abdikan, Saygin; Balik Sanli, Fusun


    Information extraction through remote sensing data is important for policy and decision makers as extracted information provide base layers for many application of real world. Classification of remotely sensed data is the one of the most common methods of extracting information however it is still a challenging issue because several factors are affecting the accuracy of the classification. Resolution of the imagery, number and homogeneity of land cover classes, purity of training data and characteristic of adopted classifiers are just some of these challenging factors. Object based image classification has some superiority than pixel based classification for high resolution images since it uses geometry and structure information besides spectral information. Vegetation indices are also commonly used for the classification process since it provides additional spectral information for vegetation, forestry and agricultural areas. In this study, the impacts of the Normalized Difference Vegetation Index (NDVI) and Normalized Difference Red Edge Index (NDRE) on the classification accuracy of RapidEye imagery were investigated. Object based Support Vector Machines were implemented for the classification of crop types for the study area located in Aegean region of Turkey. Results demonstrated that the incorporation of NDRE increase the classification accuracy from 79,96% to 86,80% as overall accuracy, however NDVI decrease the classification accuracy from 79,96% to 78,90%. Moreover it is proven than object based classification with RapidEye data give promising results for crop type mapping and analysis.

  11. Multi-objective intelligent coordinating optimization blending system based on qualitative and quantitative synthetic model

    WANG Ya-lin; MA Jie; GUI Wei-hua; YANG Chun-hua; ZHANG Chuan-fu


    A multi-objective intelligent coordinating optimization strategy based on qualitative and quantitative synthetic model for Pb-Zn sintering blending process was proposed to obtain optimal mixture ratio. The mechanism and neural network quantitative models for predicting compositions and rule models for expert reasoning were constructed based on statistical data and empirical knowledge. An expert reasoning method based on these models were proposed to solve blending optimization problem, including multi-objective optimization for the first blending process and area optimization for the second blending process, and to determine optimal mixture ratio which will meet the requirement of intelligent coordination. The results show that the qualified rates of agglomerate Pb, Zn and S compositions are increased by 7.1%, 6.5% and 6.9%, respectively, and the fluctuation of sintering permeability is reduced by 7.0 %, which effectively stabilizes the agglomerate compositions and the permeability.

  12. Infrared object detection using global and local cues based on LARK

    Qi, Wei; Han, Jing; Zhang, Yi; Bai, Lian-fa


    Object detection has become a challenging problem in computer vision. Locally Adaptive Regression Kernel (LARK) based detection methods are able to produce visually pleasing results without any training. We in this paper present an effective object detection method by exploring global and local cues based on LARK features. First, we encode the local context similarity by exploiting region Structural LARK (SLARK) features, which measure the likeness of a pixel to its surroundings in the query image and the test image. Second, a global constraint based on SLARK features via Heat equation is learned to detect similar features in the test image. Results from matrix cosine similarity are computed to estimate similar regions between these computed features. A compactness score is provided to refine these regions. Next, we detect the location of objects in the test image using non-maxima suppression. We show in experiments that the proposed method significantly outperforms other methods on the infrared image datasets, localizing the objects in the test images effectively.

  13. Multi-objective optimization of stamping forming process of head using Pareto-based genetic algorithm

    周杰; 卓芳; 黄磊; 罗艳


    To obtain the optimal process parameters of stamping forming, finite element analysis and optimization technique were integrated via transforming multi-objective issue into a single-objective issue. A Pareto-based genetic algorithm was applied to optimizing the head stamping forming process. In the proposed optimal model, fracture, wrinkle and thickness varying are a function of several factors, such as fillet radius, draw-bead position, blank size and blank-holding force. Hence, it is necessary to investigate the relationship between the objective functions and the variables in order to make objective functions varying minimized simultaneously. Firstly, the central composite experimental (CCD) with four factors and five levels was applied, and the experimental data based on the central composite experimental were acquired. Then, the response surface model (RSM) was set up and the results of the analysis of variance (ANOVA) show that it is reliable to predict the fracture, wrinkle and thickness varying functions by the response surface model. Finally, a Pareto-based genetic algorithm was used to find out a set of Pareto front, which makes fracture, wrinkle and thickness varying minimized integrally. A head stamping case indicates that the present method has higher precision and practicability compared with the“trial and error”procedure.

  14. Lossy to lossless object-based coding of 3-D MRI data.

    Menegaz, Gloria; Thiran, Jean-Philippe


    We propose a fully three-dimensional (3-D) object-based coding system exploiting the diagnostic relevance of the different regions of the volumetric data for rate allocation. The data are first decorrelated via a 3-D discrete wavelet transform. The implementation via the lifting steps scheme allows to map integer-to-integer values, enabling lossless coding, and facilitates the definition of the object-based inverse transform. The coding process assigns disjoint segments of the bitstream to the different objects, which can be independently accessed and reconstructed at any up-to-lossless quality. Two fully 3-D coding strategies are considered: embedded zerotree coding (EZW-3D) and multidimensional layered zero coding (MLZC), both generalized for region of interest (ROI)-based processing. In order to avoid artifacts along region boundaries, some extra coefficients must be encoded for each object. This gives rise to an overheading of the bitstream with respect to the case where the volume is encoded as a whole. The amount of such extra information depends on both the filter length and the decomposition depth. The system is characterized on a set of head magnetic resonance images. Results show that MLZC and EZW-3D have competitive performances. In particular, the best MLZC mode outperforms the others state-of-the-art techniques on one of the datasets for which results are available in the literature.

  15. A vague-set-based fuzzy multi-objective decision making model for bidding purchase

    WANG Zhou-jing; QIAN Edward Y.


    A vague-set-based fuzzy multi-objective decision making model is developed for evaluating bidding plans in a bidding purchase process. A group of decision-makers (DMs) first independently assess bidding plans according to their experience and preferences, and these assessments may be expressed as linguistic terms, which are then converted to fuzzy numbers. The resulting decision matrices are then transformed to objective membership grade matrices. The lower bound of satisfaction and upper bound of dissatisfaction are used to determine each bidding plan's supporting, opposing, and neutral objective sets, which together determine the vague value of a bidding plan. Finally, a score function is employed to rank all bidding plans. A new score function based on vague sets is introduced in the model and a novel method is presented for calculating the lower bound of satisfaction and upper bound of dissatisfaction. In a vague-set-based fuzzy multi-objective decision making model, different valuations for upper and lower bounds of satisfaction usually lead to distinct ranking results. Therefore, it is crucial to effectively contain DMs' arbitrariness and subjectivity when these values are determined.

  16. Multi-objective optimization of an insulating product based on wood fibre material

    Hobballah, Mohamad; Vignon, Pierre; Tran, Huyen


    This article addresses the optimization of the quality of an insulating material that is based on wood fibres. In a context where several conflicting objectives must be satisfied simultaneously in the design process, meta-heuristic approaches provide efficient methods for optimization. Multi-objective particle swarm optimization (MOPSO) has been chosen here to solve this complex problem in which physical properties such as thermal conductivity and thickness recovery, that are conflicting, are modelled through heterogeneous variables and nonlinear mathematical models. This is an ongoing work; Influence graph and the first mathematical model are presented in this paper while the preliminary optimization results will be presented during the ESAFROM conference.

  17. Affine Invariant, Model-Based Object Recognition Using Robust Metrics and Bayesian Statistics

    Zografos, Vasileios; 10.1007/11559573_51


    We revisit the problem of model-based object recognition for intensity images and attempt to address some of the shortcomings of existing Bayesian methods, such as unsuitable priors and the treatment of residuals with a non-robust error norm. We do so by using a refor- mulation of the Huber metric and carefully chosen prior distributions. Our proposed method is invariant to 2-dimensional affine transforma- tions and, because it is relatively easy to train and use, it is suited for general object matching problems.

  18. Smart learning objects for smart education in computer science theory, methodology and robot-based implementation

    Stuikys, Vytautas


    This monograph presents the challenges, vision and context to design smart learning objects (SLOs) through Computer Science (CS) education modelling and feature model transformations. It presents the latest research on the meta-programming-based generative learning objects (the latter with advanced features are treated as SLOs) and the use of educational robots in teaching CS topics. The introduced methodology includes the overall processes to develop SLO and smart educational environment (SEE) and integrates both into the real education setting to provide teaching in CS using constructivist a

  19. Development of an image based system to objectively score the severity of phoriasis

    Gomez, David Delgado


    that characterize the lesion and help to track the evolution of the disease. The thesis starts by analyzing an accurate type of equipment with which collect dermatological images. Later, a method to segment the different areas embedded in dermatological lesions is developed. Results of the segmentation task......The objective of this thesis is to provide a possible solution to one of the current problems in dermatology: the lack of suitable methods to objectively evaluate the severity of dermatological lesions. An image based system is developed with the goal of automatically obtaining summarization values...

  20. Stability Analysis of Ranking Alternatives Based on Subjective and Objective Weight

    JIANG Yan; ZUO Bao-he; YUE Chao-yuan


    Weights of criteria are used to assess the relative importance of the different criteria in multicriteria analysis, which can influence ranking result more or less depending on the multicriteria decisionmaking method used. In this paper, the influences of alternatives' ranking result associated with the change of weight are discussed by making use of the concept of weight stability intervals based on subjective and objective integrated weighting approach. Meamwhile, A model of weight proportion stability intervals is proposed. a numeral example is used to illuminate how many increment of objective weight can change the ranking results determined by subjective weight.

  1. An Extensible Component-Based Multi-Objective Evolutionary Algorithm Framework

    Sørensen, Jan Corfixen; Jørgensen, Bo Nørregaard


    The ability to easily modify the problem definition is currently missing in Multi-Objective Evolutionary Algorithms (MOEA). Existing MOEA frameworks do not support dynamic addition and extension of the problem formulation. The existing frameworks require a re-specification of the problem definition...... with different compositions of objectives from the horticulture domain are formulated based on a state of the art micro-climate simulator, electricity prices and weather forecasts. The experimental results demonstrate that the Controleum framework support dynamic reconfiguration of the problem formulation...

  2. Recognition of Gene Acceptor Site Based on Multi-objective Optimization

    Jing ZHAO; Yue-Min ZHU; Pei-Ming SONG; Qing FANG; Jian-Hua LUO


    A new method for predicting the gene acceptor site based on multi-objective optimization is introduced in this paper. The models for the acceptor, branch and distance between acceptor site and branch site were constructed according to the characteristics of the sequences from the exon-intron database and using common biological knowledge. The acceptor function, branch function and distance function were defined respectively, and the multi-objective optimization model was constructed to recognize the splice site. The test results show that the algorithm used in this study performs better than the SplicePredictor,which is one of the leading acceptor site detectors.

  3. Network Analysis Modeling Towards GIS Based on Object-Relation Database

    YUE Peng; WANG Yandong; GONG Jianya; HUANG Xianfeng


    This paper compares the differences between the mathematical model in graph theory and GIS network analysis model. Thus it claims that the GIS network analysis model needs to solve. Then this paper introduces the spatial data management methods in object-relation database for GIS and discusses its effects on the network analysis model. Finally it puts forward the GIS network analysis model based on the object-relation database. The structure of the model is introduced in detail and research is done to the internal and external memory data structure of the model. The results show that it performs well in practice.

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

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


    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.

  5. Design for sustainability of industrial symbiosis based on emergy and multi-objective particle swarm optimization.

    Ren, Jingzheng; Liang, Hanwei; Dong, Liang; Sun, Lu; Gao, Zhiqiu


    Industrial symbiosis provides novel and practical pathway to the design for the sustainability. Decision support tool for its verification is necessary for practitioners and policy makers, while to date, quantitative research is limited. The objective of this work is to present an innovative approach for supporting decision-making in the design for the sustainability with the implementation of industrial symbiosis in chemical complex. Through incorporating the emergy theory, the model is formulated as a multi-objective approach that can optimize both the economic benefit and sustainable performance of the integrated industrial system. A set of emergy based evaluation index are designed. Multi-objective Particle Swarm Algorithm is proposed to solve the model, and the decision-makers are allowed to choose the suitable solutions form the Pareto solutions. An illustrative case has been studied by the proposed method, a few of compromises between high profitability and high sustainability can be obtained for the decision-makers/stakeholders to make decision.

  6. Semantic Predicate Types and Approximation for Class-based Object Oriented Programming

    van Bakel, Steffen


    We apply the principles of the intersection type discipline to the study of class-based object oriented programs and; our work follows from a similar approach (in the context of Abadi and Cardelli's Varsigma-object calculus) taken by van Bakel and de'Liguoro. We define an extension of Featherweight Java, FJc and present a predicate system which we show to be sound and expressive. We also show that our system provides a semantic underpinning for the object oriented paradigm by generalising the concept of approximant from the Lambda Calculus and demonstrating an approximation result: all expressions to which we can assign a predicate have an approximant that satisfies the same predicate. Crucial to this result is the notion of predicate language, which associates a family of predicates with a class.

  7. A Robust Algorithm Based on Object Contours and Order Matching for Disparity Map Post-Processing


    Based on the feature of stereo images' content and the property of natural objects, we redefine the general order matching constraint with object contour restriction. According to the modified order matching constraint, we propose a robust algorithm for disparity-map post-processing. Verified by computer simulations using synthetic stereo images with given disparities, our new algorithm proves to be not only efficient in disparity error detection and correction, but also very robust, which can resolve the severe problem in the algorithm proposed in Ref.[3] that if there are large differences among the depths of objects in a scene, the algorithm will make mistakes during the process of disparity error detection and correction.

  8. Object-oriented analysis and design: a methodology for modeling the computer-based patient record.

    Egyhazy, C J; Eyestone, S M; Martino, J; Hodgson, C L


    The article highlights the importance of an object-oriented analysis and design (OOAD) methodology for the computer-based patient record (CPR) in the military environment. Many OOAD methodologies do not adequately scale up, allow for efficient reuse of their products, or accommodate legacy systems. A methodology that addresses these issues is formulated and used to demonstrate its applicability in a large-scale health care service system. During a period of 6 months, a team of object modelers and domain experts formulated an OOAD methodology tailored to the Department of Defense Military Health System and used it to produce components of an object model for simple order processing. This methodology and the lessons learned during its implementation are described. This approach is necessary to achieve broad interoperability among heterogeneous automated information systems.

  9. New multi-camera calibration algorithm based on 1D objects

    Zi-jian ZHAO; Yun-cai LIU


    A new calibration algorithm for multi-camera systems using 1D calibration objects is proposed. The algorithm integrates the rank-4 factorization with Zhang (2004)'s method. The intrinsic parameters as well as the extrinsic parameters are recovered by capturing with cameras the 1D object's rotations around a fixed point. The algorithm is based on factorization of the scaled measurement matrix, the projective depth of which is estimated in an analytical equation instead of a recursive form. For more than three points on a 1D object, the approach of our algorithm is to extend the scaled measurement matrix. The obtained parameters are finally refined through the maximum likelihood inference. Simulations and experiments with real images verify that the proposed technique achieves a good trade-off between the intrinsic and extrinsic camera parameters.

  10. [CORBA-based design and packaging of picture archiving and communication system object].

    Zhang, Zu-jin; Sun, An-yu


    Regionalization has become one of the most important trends in the development of picture archiving and communication systems (PACS) due to the existence of large amounts of information islands. The conventional PACS designed according to the DICOM standard on the basis of the C/S structure fails to meet the demand of regional information system maintenance. A regional PACS system based on common object request broker architecture (CORBA) distributed technology is therefore proposed and implemented. This article describes the design and packaging of the PACS objects. When mounting a series of separate logical and related functional PACS objects on the ORB bus, the multi-scale PACS systems can be established. This method eliminates the limitation of the C/S structure and offers good compatibility and scalability.

  11. Design of object surveillance system based on enhanced fish-eye lens

    Jianhui Wu; Kuntao Yang; Qiaolian Xiang; Nanyang Zhang


    A new method is proposed for the object surveillance system based on the enhanced fish-eye lens and the high speed digital signal processor (DSP). The improved fish-eye lens images an ellipse picture on the charge-coupled device (CCD) surface, which increases both the utilization rate of the 4:3 rectangular CCD and the imaging resolution, and remains the view angle of 183°. The algorithm of auto-adapted renewal background subtraction (ARBS) is also explored to extract the object from the monitoring image. The experimental result shows that the ARBS algorithm has high anti-jamming ability and high resolution, leading to excellent object detecting ability from the enhanced elliptical fish-eye image under varies en-vironments. This system has potential applications in different security monitoring fields due to its wide monitoring space, simple structure, working stability, and reliability.

  12. A Unified Spatiotemporal Prior based on Geodesic Distance for Video Object Segmentation.

    Wang, Wenguan; Shen, Jianbing; Yang, Ruigang; Porikli, Fatih


    Video saliency, aiming for estimation of a single dominant object in a sequence, offers strong object-level cues for unsupervised video object segmentation. In this paper, we present a geodesic distance based technique that provides reliable and temporally consistent saliency measurement of superpixels as a prior for pixel-wise labeling. Using undirected intra-frame and inter-frame graphs constructed from spatiotemporal edges or appearance and motion, and a skeleton abstraction step to further enhance saliency estimates, our method formulates the pixel-wise segmentation task as an energy minimization problem on a function that consists of unary terms of global foreground and background models, dynamic location models, and pairwise terms of label smoothness potentials. We perform extensive quantitative and qualitative experiments on benchmark datasets. Our method achieves superior performance in comparison to the current state-of-the-art in terms of accuracy and speed.

  13. A Line Graph-Based Continuous Range Query Method for Moving Objects in Networks

    Hengcai Zhang


    Full Text Available The rapid growth of location-based services has motivated the development of continuous range queries in networks. Existing query algorithms usually adopt an expansion tree to reuse the previous query results to get better efficiency. However, the high maintenance costs of the traditional expansion tree lead to a sharp efficiency decrease. In this paper, we propose a line graph-based continuous range (LGCR query algorithm for moving objects in networks, which is characterized by a novel graph-based expansion tree (GET structure used to monitor queries in an incremental manner. In particular, GET is developed based on the line graph model of networks and simultaneously supports offline pre-computation to better adapt our proposed algorithm to different sizes of networks. To improve performance, we create a series of related data structures, such as bridgeable edges and distance edges. Correspondingly, we develop several algorithms, including initialization, insertion of objects, filter and refinement and location update, to incrementally re-evaluate continuous range queries. Finally, we implement the GET and related algorithms in the native graph database Neo4J. We conduct experiments using real-world networks and simulated moving objects and compare the proposed LGCR with the existing classical algorithm to verify its effectiveness and demonstrate its greater efficiency.

  14. Real-Time Projection-Based Augmented Reality System for Dynamic Objects in the Performing Arts

    Jaewoon Lee


    Full Text Available This paper describes the case study of applying projection-based augmented reality, especially for dynamic objects in live performing shows, such as plays, dancing, or musicals. Our study aims to project imagery correctly inside the silhouettes of flexible objects, in other words, live actors or the surface of actor’s costumes; the silhouette transforms its own shape frequently. To realize this work, we implemented a special projection system based on the real-time masking technique, that is to say real-time projection-based augmented reality system for dynamic objects in performing arts. We installed the sets on a stage for live performance, and rehearsed particular scenes of a musical. In live performance, using projection-based augmented reality technology enhances technical and theatrical aspects which were not possible with existing video projection techniques. The projected images on the surfaces of actor’s costume could not only express the particular scene of a performance more effectively, but also lead the audience to an extraordinary visual experience.

  15. An object-oriented based daytime over land fog detection approach using EOS/MODIS data

    Wen, Xiongfei; Liu, Liangming; Li, Wei; Dong, Pei


    A new algorithm is presented for land fog detection from daytime image of Earth Observation System Moderate Resolution Imaging Spectroradiometer (EOS/MODIS) data. Due to its outstanding spatial and spectral resolutions, this image is an ideal data source for fog detection. The algorithm utilizes an object-oriented technique to separate fog from other cloud types. In this paper, MOD35 product is first introduced to exclude cloud-free areas, and high clouds are removed with MODIS 26 band, and then a parameter named Normalized Difference Fog Index (NDFI) is proposed based on Streamer radiative model and MODIS data for fog detection. Through segmenting NDFI image into regions of pixels, and computing attributes (e.g. mean value of brightness temperature) for each region to create objects, each object could be identified based on the attributes selected to determine whether belongs to fog or cloud. Algorithm's performance is evaluated against ground-based measurements over China in winter. The algorithm is proved to be effective in detecting fog accurately based on two different test cases.

  16. Web-based evaluation of Parkinson's disease subjects: objective performance capacity measurements and subjective characterization profiles.

    Kondraske, George V; Stewart, R Malcolm


    Parkinson's Disease (PD) is classified as a progressively degenerative movement disorder, affecting approximately 0.2% of the population and resulting in decreased performance in a wide variety of activities of daily living. Motivated by needs associated with the conduct of multi-center clinical trials, early detection, and the optimization of routine management of individuals with PD, we have developed a three-tiered approach to evaluation of PD and other neurologic diseases/disorders. One tier is characterized as 'web-based evaluation', consisting of objective performance capacity tests and subjective questionnaires that target history and symptom evaluation. Here, we present the initial evaluation of three representative, self-administered, objective, web-based performance capacity tests (simple visual-hand response speed, rapid alternating movement quality, and upper extremity neuromotor channel capacity). Twenty-one subjects (13 with PD, 8 without neurologic disease) were evaluated. Generally good agreement was obtained with lab-based tests executed with an experienced test administrator. We conclude that objective performance capacity testing is a feasible component of a web-based evaluation for PD, providing a sufficient level of fidelity to be useful.

  17. Transmission of object based fine-granular-scalability video over networks

    Shi, Xu-li; Jin, Zhi-cheng; Teng, Guo-wei; Zhang, Zhao-yang; An, Ping; Xiao, Guang


    It is a hot focus of current researches in video standards that how to transmit video streams over Internet and wireless networks. One of the key methods is FGS(Fine-Granular-Scalability), which can always adapt to the network bandwidth varying but with some sacrifice of coding efficiency, is supported by MPEG-4. Object-based video coding algorithm has been firstly included in MPEG-4 standard that can be applied in interactive video. However, the real time segmentation of VOP(video object plan) is difficult that limit the application of MPEG-4 standard in interactive video. H.264/AVC is the up-to-date video-coding standard, which enhance compression performance and provision a network-friendly video representation. In this paper, we proposed a new Object Based FGS(OBFGS) coding algorithm embedded in H.264/AVC that is different from that in mpeg-4. After the algorithms optimization for the H.264 encoder, the FGS first finish the base-layer coding. Then extract moving VOP using the base-layer information of motion vectors and DCT coefficients. Sparse motion vector field of p-frame composed of 4*4 blocks, 4*8 blocks and 8*4 blocks in base-layer is interpolated. The DCT coefficient of I-frame is calculated by using information of spatial intra-prediction. After forward projecting each p-frame vector to the immediate adjacent I-frame, the method extracts moving VOPs (video object plan) using a recursion 4*4 block classification process. Only the blocks that belong to the moving VOP in 4*4 block-level accuracy is coded to produce enhancement-layer stream. Experimental results show that our proposed system can obtain high interested VOP quality at the cost of fewer coding efficiency.

  18. Research and development of infrared object detection system based on FPGA

    Zhao, Jianhui; He, Jianwei; Wang, Pengpeng; Li, Fan


    Infrared object detection is an important technique of digital image processing. It is widely used in automatic navigation, intelligent video surveillance systems, traffic detection, medical image processing etc. Infrared object detection system requires large storage and high speed processing technology. The current development trend is the system which can be achieved by hardware in real-time with fewer operations and higher performance. As a main large-scale programmable specific integrated circuit, field programmable gate array (FPGA) can meet all the requirements of high speed image processing, with the characteristics of simple algorithm realization, easy programming, good portability and inheritability. So it could get better result by using FPGA to infrared object detection system. According to the requirements, the infrared object detection system is designed on FPGA. By analyzing some of the main algorithms of object detection, two new object detection algorithms called integral compare algorithm (ICA) and gradual approach centroid algorithm (GACA) are presented. The system design applying FPGA in hardware can implement high speed processing technology, which brings the advantage of both performance and flexibility. ICA is a new type of denoising algorithm with advantage of lower computation complexity and less execution time. What is more important is that this algorithm can be implemented in FPGA expediently. Base on image preprocessing of ICA, GACA brings high positioning precision with advantage of insensitivity to the initial value and fewer times of convergence iteration. The experiments indicate that the infrared object detection system can implement high speed infrared object detecting in real-time, with high antijamming ability and high precision. The progress of Verilog-HDL and its architecture are introduced in this paper. Considering the engineering application, this paper gives the particular design idea and the flow of this method

  19. Design of objective lenses to extend the depth of field based on wavefront coding

    Zhao, Tingyu; Ye, Zi; Zhang, Wenzi; Huang, Weiwei; Yu, Feihong


    Wavefront coding extended the depth of field to a great extent with simpler structure compared to confocal microscope. With cubic phase mask (CPM) employed in the STOP of the objective lens, blurred images will be obtained in charge coupled device (CCD), which will be restored to sharp images by Wiener filter. We proposed that one CPM is used in one microscope although there are different objective lenses with different power indices. The microscope proposed here is the wavefront coding one when the CPM is used in the STOP; while it is the traditional one when a plane plate is used in the STOP. Firstly, make the STOP in the last surface of the lens, and then add a plane plate at the STOP with the same material and the same center thickness of the CPM. Traditional objective lenses are designed, based on which wavefront coding system will be designed with the plane plate replaced by a CPM. Secondly, the parameters of CPMs in different objective lenses are optimized to certain ranges based on metric function of stability of modulation transfer function (MTF). The optimal parameter is chosen from these ranges. A set of objective lenses is designed as an example with one CPM. The simulation results shows that the depth of field of 4X, 10X, 40X, 60X and 100X objective lenses with the same CPM can reach to 400um, 40um, 24um, 16um and 2um respectively, which is much larger than 55.5um, 8.5um, 1um, 0.4um and 0.19um of the traditional ones.

  20. Bi-objective optimization for multi-modal transportation routing planning problem based on Pareto optimality

    Yan Sun


    Full Text Available Purpose: The purpose of study is to solve the multi-modal transportation routing planning problem that aims to select an optimal route to move a consignment of goods from its origin to its destination through the multi-modal transportation network. And the optimization is from two viewpoints including cost and time. Design/methodology/approach: In this study, a bi-objective mixed integer linear programming model is proposed to optimize the multi-modal transportation routing planning problem. Minimizing the total transportation cost and the total transportation time are set as the optimization objectives of the model. In order to balance the benefit between the two objectives, Pareto optimality is utilized to solve the model by gaining its Pareto frontier. The Pareto frontier of the model can provide the multi-modal transportation operator (MTO and customers with better decision support and it is gained by the normalized normal constraint method. Then, an experimental case study is designed to verify the feasibility of the model and Pareto optimality by using the mathematical programming software Lingo. Finally, the sensitivity analysis of the demand and supply in the multi-modal transportation organization is performed based on the designed case. Findings: The calculation results indicate that the proposed model and Pareto optimality have good performance in dealing with the bi-objective optimization. The sensitivity analysis also shows the influence of the variation of the demand and supply on the multi-modal transportation organization clearly. Therefore, this method can be further promoted to the practice. Originality/value: A bi-objective mixed integer linear programming model is proposed to optimize the multi-modal transportation routing planning problem. The Pareto frontier based sensitivity analysis of the demand and supply in the multi-modal transportation organization is performed based on the designed case.

  1. Coregistration refinement of hyperspectral images and DSM: An object-based approach using spectral information

    Avbelj, Janja; Iwaszczuk, Dorota; Müller, Rupert; Reinartz, Peter; Stilla, Uwe


    For image fusion in remote sensing applications the georeferencing accuracy using position, attitude, and camera calibration measurements can be insufficient. Thus, image processing techniques should be employed for precise coregistration of images. In this article a method for multimodal object-based image coregistration refinement between hyperspectral images (HSI) and digital surface models (DSM) is presented. The method is divided in three parts: object outline detection in HSI and DSM, matching, and determination of transformation parameters. The novelty of our proposed coregistration refinement method is the use of material properties and height information of urban objects from HSI and DSM, respectively. We refer to urban objects as objects which are typical in urban environments and focus on buildings by describing them with 2D outlines. Furthermore, the geometric accuracy of these detected building outlines is taken into account in the matching step and for the determination of transformation parameters. Hence, a stochastic model is introduced to compute optimal transformation parameters. The feasibility of the method is shown by testing it on two aerial HSI of different spatial and spectral resolution, and two DSM of different spatial resolution. The evaluation is carried out by comparing the accuracies of the transformations parameters to the reference parameters, determined by considering object outlines at much higher resolution, and also by computing the correctness and the quality rate of the extracted outlines before and after coregistration refinement. Results indicate that using outlines of objects instead of only line segments is advantageous for coregistration of HSI and DSM. The extraction of building outlines in comparison to the line cue extraction provides a larger amount of assigned lines between the images and is more robust to outliers, i.e. false matches.

  2. Measurement of a discontinuous object based on a dual-frequency grating

    Qiao Nao-Sheng; Cai Xin-Hua; Yao Chun-Mei


    The dual-frequency grating measurement theory is proposed in order to carry out the measurement of a discontinuous object. Firstly, the reason why frequency spectra are produced by low frequency gratings and high frequency gratings in the field of frequency is analysed, and the relationship between the wrapped-phase and the unwrappingphase is discussed. Secondly, a method to combine the advantages of the two kinds of gratings is proposed: one stripe is produced in the mutation part of the object measured by a suitable low frequency grating designed by MATLAB, then the phase produced by the low frequency grating need not be unfolded. The integer series of stripes is produced by a high frequency grating designed by MATLAB based on the frequency ratio of the two kinds of gratings and the high frequency wrapped-phase, and the high frequency unwrapping-phase is then obtained. In order to verify the correctness of the theoretical analysis, a steep discontinuous object of 600×600 pixels and 10.00 mm in height is simulated and a discontinuous object of ladder shape which is 32.00 mm in height is used in experiment. Both the simulation and the experiment can restore the discontinuous object height accurately by using the dual-frequency grating measurement theory.

  3. The neural bases of crossmodal object recognition in non-human primates and rodents: a review.

    Cloke, Jacob M; Jacklin, Derek L; Winters, Boyer D


    The ability to integrate information from different sensory modalities to form unique multisensory object representations is a highly adaptive cognitive function. Surprisingly, non-human animal studies of the neural substrates of this form of multisensory integration have been somewhat sparse until very recently, and this may be due in part to a relative paucity of viable testing methods. Here we review the historical development and use of various "crossmodal" cognition tasks for non-human primates and rodents, focusing on tests of "crossmodal object recognition", the ability to recognize an object across sensory modalities. Such procedures have great potential to elucidate the cognitive and neural bases of object representation as it pertains to perception and memory. Indeed, these studies have revealed roles in crossmodal cognition for various brain regions (e.g., prefrontal and temporal cortices) and neurochemical systems (e.g., acetylcholine). A recent increase in behavioral and physiological studies of crossmodal cognition in rodents augurs well for the future of this research area, which should provide essential information about the basic mechanisms of object representation in the brain, in addition to fostering a better understanding of the causes of, and potential treatments for, cognitive deficits in human diseases characterized by atypical multisensory integration.

  4. Age-based hiring discrimination as a function of equity norms and self-perceived objectivity.

    Nicole M Lindner

    Full Text Available Participants completed a questionnaire priming them to perceive themselves as either objective or biased, either before or after evaluating a young or old job applicant for a position linked to youthful stereotypes. Participants agreed that they were objective and tended to disagree that they were biased. Extending past research, both the objective and bias priming conditions led to an increase in age discrimination compared to the control condition. We also investigated whether equity norms reduced age discrimination, by manipulating the presence or absence of an equity statement reminding decision-makers of the legal prohibitions against discrimination "on the basis of age, disability, national or ethnic origin, race, religion, or sex." The presence of equity norms increased enthusiasm for both young and old applicants when participants were not already primed to think of themselves as objective, but did not reduce age-based hiring discrimination. Equity norms had no effect when individuals thought of themselves as objective - they preferred the younger more than the older job applicant. However, the presence of equity norms did affect individuals' perceptions of which factors were important to their hiring decisions, increasing the perceived importance of applicants' expertise and decreasing the perceived importance of the applicants' age. The results suggest that interventions that rely exclusively on decision-makers' intentions to behave equitably may be ineffective.

  5. Age-based hiring discrimination as a function of equity norms and self-perceived objectivity.

    Lindner, Nicole M; Graser, Alexander; Nosek, Brian A


    Participants completed a questionnaire priming them to perceive themselves as either objective or biased, either before or after evaluating a young or old job applicant for a position linked to youthful stereotypes. Participants agreed that they were objective and tended to disagree that they were biased. Extending past research, both the objective and bias priming conditions led to an increase in age discrimination compared to the control condition. We also investigated whether equity norms reduced age discrimination, by manipulating the presence or absence of an equity statement reminding decision-makers of the legal prohibitions against discrimination "on the basis of age, disability, national or ethnic origin, race, religion, or sex." The presence of equity norms increased enthusiasm for both young and old applicants when participants were not already primed to think of themselves as objective, but did not reduce age-based hiring discrimination. Equity norms had no effect when individuals thought of themselves as objective - they preferred the younger more than the older job applicant. However, the presence of equity norms did affect individuals' perceptions of which factors were important to their hiring decisions, increasing the perceived importance of applicants' expertise and decreasing the perceived importance of the applicants' age. The results suggest that interventions that rely exclusively on decision-makers' intentions to behave equitably may be ineffective.

  6. Electro-holography display using computer generated hologram of 3D objects based on projection spectra

    Huang, Sujuan; Wang, Duocheng; He, Chao


    A new method of synthesizing computer-generated hologram of three-dimensional (3D) objects is proposed from their projection images. A series of projection images of 3D objects are recorded with one-dimensional azimuth scanning. According to the principles of paraboloid of revolution in 3D Fourier space and 3D central slice theorem, spectra information of 3D objects can be gathered from their projection images. Considering quantization error of horizontal and vertical directions, the spectrum information from each projection image is efficiently extracted in double circle and four circles shape, to enhance the utilization of projection spectra. Then spectra information of 3D objects from all projection images is encoded into computer-generated hologram based on Fourier transform using conjugate-symmetric extension. The hologram includes 3D information of objects. Experimental results for numerical reconstruction of the CGH at different distance validate the proposed methods and show its good performance. Electro-holographic reconstruction can be realized by using an electronic addressing reflective liquid-crystal display (LCD) spatial light modulator. The CGH from the computer is loaded onto the LCD. By illuminating a reference light from a laser source to the LCD, the amplitude and phase information included in the CGH will be reconstructed due to the diffraction of the light modulated by the LCD.

  7. Context-based object-class recognition and retrieval by generalized correlograms.

    Amores, Jaume; Sebe, Nicu; Radeva, Petia


    We present a novel approach for retrieval of object categories based on a novel type of image representation: the Generalized Correlogram (GC). In our image representation, the object is described as a constellation of GCs where each one encodes information about some local part and the spatial relations from this part to others (i.e., the part's context). We show how such a representation can be used with fast procedures that learn the object category with weak supervision and efficiently match the model of the object against large collections of images. In the learning stage, we show that by integrating our representation with Boosting the system is able to obtain a compact model that is represented by very few features, where each feature conveys key properties about the object's parts and their spatial arrangement. In the matching step, we propose direct procedures that exploit our representation for efficiently considering spatial coherence between the matching of local parts. Combined with an appropriate data organization such as Inverted Files, we show that thousands of images can be evaluated efficiently. The framework has been applied to different standard databases and we show that our results are favorably compared against state-of-the-art methods in both computational cost and accuracy.

  8. PE-CMOS based C-scan ultrasound for foreign object detection in soft tissue.

    Liu, Chu-Chuan; Lo, Shih-Chung Ben; Freedman, Matthew T; Lasser, Marvin E; Kula, John; Sarcone, Anita; Wang, Yue


    In this paper, we introduce a C-scan ultrasound prototype and three imaging modalities for the detection of foreign objects inserted in porcine soft tissue. The object materials include bamboo, plastics, glass and aluminum alloys. The images of foreign objects were acquired using the C-scan ultrasound, a portable B-scan ultrasound, film-based radiography, and computerized radiography. The C-scan ultrasound consists of a plane wave transducer, a compound acoustic lens system, and a newly developed ultrasound sensor array based on the complementary metal-oxide semiconductor coated with piezoelectric material (PE-CMOS). The contrast-to-noise ratio (CNR) of the images were analyzed to quantitatively evaluate the detectability using different imaging modalities. The experimental results indicate that the C-scan prototype has better CNR values in 4 out of 7 objects than other modalities. Specifically, the C-scan prototype provides more detail information of the soft tissues without the speckle artifacts that are commonly seen with conventional B-scan ultrasound, and has the same orientation as the standard radiographs but without ionizing radiation.

  9. Internet of Things based on smart objects technology, middleware and applications

    Trunfio, Paolo


    The Internet of Things (IoT) usually refers to a world-wide network of interconnected heterogeneous objects (sensors, actuators, smart devices, smart objects, RFID, embedded computers, etc) uniquely addressable, based on standard communication protocols. Beyond such a definition, it is emerging a new definition of IoT seen as a loosely coupled, decentralized system of cooperating smart objects (SOs). A SO is an autonomous, physical digital object augmented with sensing/actuating, processing, storing, and networking capabilities. SOs are able to sense/actuate, store, and interpret information created within themselves and around the neighbouring external world where they are situated, act on their own, cooperate with each other, and exchange information with other kinds of electronic devices and human users. However, such SO-oriented IoT raises many in-the-small and in-the-large issues involving SO programming, IoT system architecture/middleware and methods/methodologies for the development of SO-based applica...

  10. Multi-Objective Combinatorial Optimization of Trigeneration Plants Based on Metaheuristics

    Mirko M. Stojiljković


    Full Text Available In this paper, a methodology for multi-objective optimization of trigeneration plants is presented. It is primarily applicable to the systems for buildings’ energy supply characterized by high load variations on daily, weekly and annual bases, as well as the components applicable for flexible operation. The idea is that this approach should enable high accuracy and flexibility in mathematical modeling, while remaining efficient enough. The optimization problem is structurally decomposed into two new problems. The main problem of synthesis and design optimization is combinatorial and solved with different metaheuristic methods. For each examined combination of the synthesis and design variables, when calculating the values of the objective functions, the inner, mixed integer linear programming operation optimization problem is solved with the branch-and-cut method. The applicability of the exploited metaheuristic methods is demonstrated. This approach is compared with the alternative, superstructure-based approach. The potential for combining them is also examined. The methodology is applied for multi-objective optimization of a trigeneration plant that could be used for the energy supply of a real residential settlement in Niš, Serbia. Here, two objectives are considered: annual total costs and primary energy consumption. Results are obtained in the form of a Pareto chart using the epsilon-constraint method.

  11. Robust Object-Based Watermarking Using SURF Feature Matching and DFT Domain

    M. Cedillo-Hernandez


    Full Text Available In this paper we propose a robust object-based watermarking method, in which the watermark is embedded into the middle frequencies band of the Discrete Fourier Transform (DFT magnitude of the selected object region, altogether with the Speeded Up Robust Feature (SURF algorithm to allow the correct watermark detection, even if the watermarked image has been distorted. To recognize the selected object region after geometric distortions, during the embedding process the SURF features are estimated and stored in advance to be used during the detection process. In the detection stage, the SURF features of the distorted image are estimated and match them with the stored ones. From the matching result, SURF features are used to compute the Affine-transformation parameters and the object region is recovered. The quality of the watermarked image is measured using the Peak Signal to Noise Ratio (PSNR, Structural Similarity Index (SSIM and the Visual Information Fidelity (VIF. The experimental results show the proposed method provides robustness against several geometric distortions, signal processing operations and combined distortions. The receiver operating characteristics (ROC curves also show the desirable detection performance of the proposed method. The comparison with a previously reported methods based on different techniques is also provided.

  12. Personalised Learning Object System Based on Self-Regulated Learning Theories

    Ali Alharbi


    Full Text Available Self-regulated learning has become an important construct in education research in the last few years. Selfregulated learning in its simple form is the learner’s ability to monitor and control the learning process. There is increasing research in the literature on how to support students become more self-regulated learners. However, the advancement in the information technology has led to paradigm changes in the design and development of educational content. The concept of learning object instructional technology has emerged as a result of this shift in educational technology paradigms. This paper presents the results of a study that investigated the potential educational effectiveness of a pedagogical framework based on the self-regulated learning theories to support the design of learning object systems to help computer science students. A prototype learning object system was developed based on the contemporary research on self-regulated learning. The system was educationally evaluated in a quasi-experimental study over two semesters in a core programming languages concepts course. The evaluation revealed that a learning object system that takes into consideration contemporary research on self-regulated learning can be an effective learning environment to support computer science education.

  13. Evaluation of ALOS PALSAR Imagery for Burned Area Mapping in Greece Using Object-Based Classification

    Anastasia Polychronaki


    Full Text Available In this work, the potential of Advanced Land Observing Satellite (ALOS Phased Array type L-band Synthetic Aperture Radar (PALSAR imagery to map burned areas was evaluated in two study areas in Greece. For this purpose, we developed an object-based classification scheme to map the fire-disturbed areas using the PALSAR imagery acquired before and shortly after fire events. The advantage of employing an object-based approach was not only the use of the temporal variation of the backscatter coefficient, but also the incorporation in the classification of topological features, such as neighbor objects, and class related features, such as objects classified as burned. The classification scheme resulted in mapping the burned areas with satisfactory results: 0.71 and 0.82 probabilities of detection for the two study areas. Our investigation revealed that the pre-fire vegetation conditions and fire severity should be taken in consideration when mapping burned areas using PALSAR in Mediterranean regions. Overall, findings suggest that the developed scheme could be applied for rapid burned area assessment, especially to areas where cloud cover and fire smoke inhibit accurate mapping of burned areas when optical data are used.

  14. Object-Oriented Change Detection for Remote Sensing Images Based on Multi-Scale Fusion

    Feng, Wenqing; Sui, Haigang; Tu, Jihui


    In the process of object-oriented change detection, the determination of the optimal segmentation scale is directly related to the subsequent change information extraction and analysis. Aiming at this problem, this paper presents a novel object-level change detection method based on multi-scale segmentation and fusion. First of all, the fine to coarse segmentation is used to obtain initial objects of different sizes; then, according to the features of the objects, Change Vector Analysis is used to obtain the change detection results of various scales. Furthermore, in order to improve the accuracy of change detection, this paper introduces fuzzy fusion and two kinds of decision level fusion methods to get the results of multi-scale fusion. Based on these methods, experiments are done with SPOT5 multi-spectral remote sensing imagery. Compared with pixel-level change detection methods, the overall accuracy of our method has been improved by nearly 10%, and the experimental results prove the feasibility and effectiveness of the fusion strategies.

  15. Vineyard parcel identification from Worldview-2 images using object-based classification model

    Sertel, Elif; Yay, Irmak


    Accurate identification of spatial distribution and characteristics of vineyard parcels is an important task for the effective management of vineyard areas, precision viticulture, and farmer registries. This study aimed to develop rule sets to be used in object-based classification of Worldview-2 satellite images to accurately delineate the boundaries of vineyards having different plantation styles. Multilevel segmentation was applied to Worldview-2 images to create different sizes of image objects representing different land cover categories with respect to scale parameter. Texture analysis and several new spectral indices were applied to objects at different segmentation levels to accurately classify land cover classes of forest, cultivated areas, harvested areas, impervious, bareland, and vineyards. A specific attention was given to vineyard class to identify vine areas at the parcel level considering their different plantation styles. The results illustrated that the combined usage of a newly developed decision tree and image segmentation during the object-based classification process could provide highly accurate results for the identification of vineyard parcels. Linearly planted vineyards could be classified with 100% producer's accuracy due to their regular textural characteristics, whereas regular gridwise and irregular gridwise (distributed) vineyard parcels could be classified with 94.87% producer's accuracy in this research.

  16. Object Detection and Tracking Method of AUV Based on Acoustic Vision

    ZHANG Tie-dong; WAN Lei; ZENG Wen-jing; XU Yu-ru


    This paper describes a new framework for object detection and tracking of AUV including underwater acoustic data interpolation,underwater acoustic images segmentation and underwater objects tracking.This framework is applied to the design of vision-based method for AUV based on the forward looking sonar sensor.First,the real-time data flow (underwater acoustic images) is pre-processed to form the whole underwater acoustic image,and the relevant position information of objects is extracted and determined.An improved method of double threshold segmentation is proposed to resolve the problem that the threshold cannot be adjusted adaptively in the traditional method.Second,a representation of region information is created in light of the Gaussian particle filter.The weighted integration strategy combining the area and invariant moment is proposed to perfect the weight of particles and to enhance the tracking robustness.Results obtained on the real acoustic vision platform of AUV during sea trials are displayed and discussed.They show that the proposed method can detect and track the moving objects underwater online,and it is effective and robust.

  17. Object detection and tracking method of AUV based on acoustic vision

    Zhang, Tie-dong; Wan, Lei; Zeng, Wen-jing; Xu, Yu-ru


    This paper describes a new framework for object detection and tracking of AUV including underwater acoustic data interpolation, underwater acoustic images segmentation and underwater objects tracking. This framework is applied to the design of vision-based method for AUV based on the forward looking sonar sensor. First, the real-time data flow (underwater acoustic images) is pre-processed to form the whole underwater acoustic image, and the relevant position information of objects is extracted and determined. An improved method of double threshold segmentation is proposed to resolve the problem that the threshold cannot be adjusted adaptively in the traditional method. Second, a representation of region information is created in light of the Gaussian particle filter. The weighted integration strategy combining the area and invariant moment is proposed to perfect the weight of particles and to enhance the tracking robustness. Results obtained on the real acoustic vision platform of AUV during sea trials are displayed and discussed. They show that the proposed method can detect and track the moving objects underwater online, and it is effective and robust.

  18. A Model-Based Approach to Object-Oriented Software Metrics

    梅宏; 谢涛; 杨芙清


    The need to improve software productivity and software quality has put for-ward the research on software metrics technology and the development of software metrics toolto support related activities. To support object-oriented software metrics practice effectively,a model-based approach to object-oriented software metrics is proposed in this paper. Thisapproach guides the metrics users to adopt the quality metrics model to measure the object-oriented software products. The development of the model can be achieved by using a top-downapproach. This approach explicitly proposes the conception of absolute normalization computa-tion and relative normalization computation for a metrics model. Moreover, a generic softwaremetrics tool - Jade Bird Object-Oriented Metrics Tool (JBOOMT) is designed to implementthis approach. The parser-based approach adopted by the tool makes the information of thesource program accurate and complete for measurement. It supports various customizablehierarchical metrics models and provides a flexible user interface for users to manipulate themodels. It also supports absolute and relative normalization mechanisms in different situations.

  19. A 3-Step Algorithm Using Region-Based Active Contours for Video Objects Detection

    Stéphanie Jehan-Besson


    Full Text Available We propose a 3-step algorithm for the automatic detection of moving objects in video sequences using region-based active contours. First, we introduce a very full general framework for region-based active contours with a new Eulerian method to compute the evolution equation of the active contour from a criterion including both region-based and boundary-based terms. This framework can be easily adapted to various applications, thanks to the introduction of functions named descriptors of the different regions. With this new Eulerian method based on shape optimization principles, we can easily take into account the case of descriptors depending upon features globally attached to the regions. Second, we propose a 3-step algorithm for detection of moving objects, with a static or a mobile camera, using region-based active contours. The basic idea is to hierarchically associate temporal and spatial information. The active contour evolves with successively three sets of descriptors: a temporal one, and then two spatial ones. The third spatial descriptor takes advantage of the segmentation of the image in intensity homogeneous regions. User interaction is reduced to the choice of a few parameters at the beginning of the process. Some experimental results are supplied.

  20. A fusion algorithm for joins based on collections in Odra (Object Database for Rapid Application development)

    Satish, Laika


    In this paper we present the functionality of a currently under development database programming methodology called ODRA (Object Database for Rapid Application development) which works fully on the object oriented principles. The database programming language is called SBQL (Stack based query language). We discuss some concepts in ODRA for e.g. the working of ODRA, how ODRA runtime environment operates, the interoperability of ODRA with .net and java .A view of ODRA's working with web services and xml. Currently the stages under development in ODRA are query optimization. So we present the prior work that is done in ODRA related to Query optimization and we also present a new fusion algorithm of how ODRA can deal with joins based on collections like set, lists, and arrays for query optimization.

  1. A 3D City Model with Dynamic Behaviour Based on Geospatial Managed Objects

    Kjems, Erik; Kolář, Jan


    models with consistent object definitions give us the possibility to avoid troublesome abstractions of reality, and design even complex urban systems fusing information from various sources of data. These systems are difficult to design with the traditional software development approach based on major......One of the major development efforts within the GI Science domain are pointing at real time information coming from geographic referenced features in general. At the same time 3D City models are mostly justified as being objects for visualization purposes rather than constituting the foundation...... of a geographic data representation of the world. The combination of 3D city models and real time information based systems though can provide a whole new setup for data fusion within an urban environment and provide time critical information preserving our limited resources in the most sustainable way. Using 3D...

  2. Computation of Edge-Edge-Edge Events Based on Conicoid Theory for 3-D Object Recognition

    WU Chenye; MA Huimin


    The availability of a good viewpoint space partition is crucial in three dimensional (3-D) object rec-ognition on the approach of aspect graph. There are two important events depicted by the aspect graph ap-proach, edge-edge-edge (EEE) events and edge-vertex (EV) events. This paper presents an algorithm to compute EEE events by characteristic analysis based on conicoid theory, in contrast to current algorithms that focus too much on EV events and often overlook the importance of EEE events. Also, the paper provides a standard flowchart for the viewpoint space partitioning based on aspect graph theory that makes it suitable for perspective models. The partitioning result best demonstrates the algorithm's efficiency with more valu-able viewpoints found with the help of EEE events, which can definitely help to achieve high recognition rate for 3-D object recognition.

  3. Empirical analysis of web-based user-object bipartite networks

    Shang, Mingsheng; Zhang, Yi-Cheng; Zhou, Tao


    Understanding the structure and evolution of web-based user-object networks is a significant task since they play a crucial role in e-commerce nowadays. This Letter reports the empirical analysis on two large-scale web sites, and, where users are connected with music groups and bookmarks, respectively. The degree distributions and degree-degree correlations for both users and objects are reported. We propose a new index, named collaborative clustering coefficient, to quantify the clustering behavior based on the collaborative selection. Accordingly, the clustering properties and clustering-degree correlations are investigated. We report some novel phenomena well characterizing the selection mechanism of web users and outline the relevance of these phenomena to the information recommendation problem.

  4. Implementation of a strain energy-based nonlinear finite element in the object-oriented environment

    Wegner, Tadeusz; Pęczak, Andrzej


    The objective of the paper is to describe a novel finite element computational method based on a strain energy density function and to implement it in the object-oriented environment. The original energy-based finite element was put into the known standard framework of classes and handled in a different manner. The nonlinear properties of material are defined with a modified strain energy density function. The local relaxation procedure proposed as a method used to resolve a nonlinear problem is implemented in C++ language. The hexahedral element with eight nodes as well as the adaptation of the nonlinear finite element is introduced. The chosen numerical model is made of nearly incompressible hyperelastic material. The application of the proposed element is shown on the example of a rectangular parallelepiped with a hollow port.

  5. Using a Flexible Skill-Based Approach to Recognize Objects in Industrial Scenarios

    Andersen, Rasmus Skovgaard; Schou, Casper; Damgaard, Jens Skov


    as well as the need for highly specialized staff for setting up modern collaborative robots. This paper proposes a skill for recognition and classification of different objects. The skill is parameterized using manual kinesthetic teaching, and machine learning based on SIFT features, Bag of Words, and SVM......Traditional industrial robots are highly efficient and precise and therefore well suited for carrying out simple, repetitive tasks. They are, however, complicated and time consuming to setup and re-program to perform new tasks. Skill-based programming attempts to reduce both the required time...... is used to classify objects. A user study with 20 test participants shows that robotics novices after only a short introduction are able to instruct the skill and combine it with other skills (pick and place) to program a complete task....

  6. Reconstruction and 3D visualisation based on objective real 3D based documentation.

    Bolliger, Michael J; Buck, Ursula; Thali, Michael J; Bolliger, Stephan A


    Reconstructions based directly upon forensic evidence alone are called primary information. Historically this consists of documentation of findings by verbal protocols, photographs and other visual means. Currently modern imaging techniques such as 3D surface scanning and radiological methods (computer tomography, magnetic resonance imaging) are also applied. Secondary interpretation is based on facts and the examiner's experience. Usually such reconstructive expertises are given in written form, and are often enhanced by sketches. However, narrative interpretations can, especially in complex courses of action, be difficult to present and can be misunderstood. In this report we demonstrate the use of graphic reconstruction of secondary interpretation with supporting pictorial evidence, applying digital visualisation (using 'Poser') or scientific animation (using '3D Studio Max', 'Maya') and present methods of clearly distinguishing between factual documentation and examiners' interpretation based on three cases. The first case involved a pedestrian who was initially struck by a car on a motorway and was then run over by a second car. The second case involved a suicidal gunshot to the head with a rifle, in which the trigger was pushed with a rod. The third case dealt with a collision between two motorcycles. Pictorial reconstruction of the secondary interpretation of these cases has several advantages. The images enable an immediate overview, give rise to enhanced clarity, and compel the examiner to look at all details if he or she is to create a complete image.

  7. Dual objective active suspension system based on a novel nonlinear disturbance compensator

    Deshpande, Vaijayanti S.; Shendge, P. D.; Phadke, S. B.


    This paper proposes an active suspension system to fulfil the dual objective of improving ride comfort while trying to keep the suspension deflection within the limits of the rattle space. The scheme is based on a novel nonlinear disturbance compensator which employs a nonlinear function of the suspension deflection. The scheme is analysed and validated by simulation and experimentation on a laboratory setup. The performance is compared with a passive suspension system for a variety of road profiles.

  8. On the transferability of rule sets for mapping cirques using Object-based feature extraction

    Seijmonsbergen, A. C.; Anders, N.S.; Gabriner, R.; Bouten, W.


    Cirques are complex landforms resulting from glacial erosion and occur in the mountains of western Austria at various topographic levels. After deglaciation they may potentially hold climate proxies, are showcases of vegetation regrowth and play an important role in the regulation of mountain hydrology. Our objective is to develop a workflow to test an object‐based rule‐set that decomposes LiDAR DEMs into the main cirque components: divide, cirque headwall, cirque floor and into the sub‐compo...

  9. Viola-Jones based hybrid framework for real-time object detection in multispectral images

    Kuznetsova, E.; Shvets, E.; Nikolaev, D.


    This paper describes a method for real-time object detection based on a hybrid of a Viola-Jones cascade with a convolutional neural network. This scheme allows flexible trade-offs between detection quality and computational performance. We also propose a generalization of this method to multispectral images that effectively and efficiently utilizes information from each spectral channel. The new scheme is experimentally compared to traditional Viola-Jones, showing improved detection quality with adjustable performance.

  10. Genetic algorithm-based multi-objective model for scheduling of linear construction projects

    Senouci, Ahmed B.; Al-Derham, H.R.


    This paper presents a genetic algorithm-based multi-objective optimization model for the scheduling of linear construction projects. The model allows construction planners to generate and evaluate optimal/near-optimal construction scheduling plans that minimize both project time and cost. The computations in the present model are organized in three major modules. A scheduling module that develops practical schedules for linear construction projects. A cost module that computes the project's c...

  11. An Efficient Role and Object Based Access Control Model Implemented in a PDM System

    HUANG Xiaowen; TAN Jian; HUANG Xiangguo


    An effective and reliable access control is crucial to a PDM system. This article has discussed the commonly used access control models, analyzed their advantages and disadvantages, and proposed a new Role and Object based access control model that suits the particular needs of a PDM system. The new model has been implemented in a commercial PDM system, which has demonstrated enhanced flexibility and convenience.


    A. Hadavand


    Full Text Available In this paper a new object-based framework is developed for automate scale selection in image segmentation. The quality of image objects have an important impact on further analyses. Due to the strong dependency of segmentation results to the scale parameter, choosing the best value for this parameter, for each class, becomes a main challenge in object-based image analysis. We propose a new framework which employs pixel-based land cover map to estimate the initial scale dedicated to each class. These scales are used to build segmentation scale space (SSS, a hierarchy of image objects. Optimization of SSS, respect to NDVI and DSM values in each super object is used to get the best scale in local regions of image scene. Optimized SSS segmentations are finally classified to produce the final land cover map. Very high resolution aerial image and digital surface model provided by ISPRS 2D semantic labelling dataset is used in our experiments. The result of our proposed method is comparable to those of ESP tool, a well-known method to estimate the scale of segmentation, and marginally improved the overall accuracy of classification from 79% to 80%.

  13. Object trajectory-based activity classification and recognition using hidden Markov models.

    Bashir, Faisal I; Khokhar, Ashfaq A; Schonfeld, Dan


    Motion trajectories provide rich spatiotemporal information about an object's activity. This paper presents novel classification algorithms for recognizing object activity using object motion trajectory. In the proposed classification system, trajectories are segmented at points of change in curvature, and the subtrajectories are represented by their principal component analysis (PCA) coefficients. We first present a framework to robustly estimate the multivariate probability density function based on PCA coefficients of the subtrajectories using Gaussian mixture models (GMMs). We show that GMM-based modeling alone cannot capture the temporal relations and ordering between underlying entities. To address this issue, we use hidden Markov models (HMMs) with a data-driven design in terms of number of states and topology (e.g., left-right versus ergodic). Experiments using a database of over 5700 complex trajectories (obtained from UCI-KDD data archives and Columbia University Multimedia Group) subdivided into 85 different classes demonstrate the superiority of our proposed HMM-based scheme using PCA coefficients of subtrajectories in comparison with other techniques in the literature.

  14. Image Processing Strategies Based on a Visual Saliency Model for Object Recognition Under Simulated Prosthetic Vision.

    Wang, Jing; Li, Heng; Fu, Weizhen; Chen, Yao; Li, Liming; Lyu, Qing; Han, Tingting; Chai, Xinyu


    Retinal prostheses have the potential to restore partial vision. Object recognition in scenes of daily life is one of the essential tasks for implant wearers. Still limited by the low-resolution visual percepts provided by retinal prostheses, it is important to investigate and apply image processing methods to convey more useful visual information to the wearers. We proposed two image processing strategies based on Itti's visual saliency map, region of interest (ROI) extraction, and image segmentation. Itti's saliency model generated a saliency map from the original image, in which salient regions were grouped into ROI by the fuzzy c-means clustering. Then Grabcut generated a proto-object from the ROI labeled image which was recombined with background and enhanced in two ways--8-4 separated pixelization (8-4 SP) and background edge extraction (BEE). Results showed that both 8-4 SP and BEE had significantly higher recognition accuracy in comparison with direct pixelization (DP). Each saliency-based image processing strategy was subject to the performance of image segmentation. Under good and perfect segmentation conditions, BEE and 8-4 SP obtained noticeably higher recognition accuracy than DP, and under bad segmentation condition, only BEE boosted the performance. The application of saliency-based image processing strategies was verified to be beneficial to object recognition in daily scenes under simulated prosthetic vision. They are hoped to help the development of the image processing module for future retinal prostheses, and thus provide more benefit for the patients.

  15. Moment feature based fast feature extraction algorithm for moving object detection using aerial images.

    A F M Saifuddin Saif

    Full Text Available Fast and computationally less complex feature extraction for moving object detection using aerial images from unmanned aerial vehicles (UAVs remains as an elusive goal in the field of computer vision research. The types of features used in current studies concerning moving object detection are typically chosen based on improving detection rate rather than on providing fast and computationally less complex feature extraction methods. Because moving object detection using aerial images from UAVs involves motion as seen from a certain altitude, effective and fast feature extraction is a vital issue for optimum detection performance. This research proposes a two-layer bucket approach based on a new feature extraction algorithm referred to as the moment-based feature extraction algorithm (MFEA. Because a moment represents the coherent intensity of pixels and motion estimation is a motion pixel intensity measurement, this research used this relation to develop the proposed algorithm. The experimental results reveal the successful performance of the proposed MFEA algorithm and the proposed methodology.

  16. Wireless Location Determination for Mobile Objects Based on GSM in Intelligent Transportation Systems

    徐志扬; 施鹏飞


    The mobile object (MO) location determination technologies which can be used in intelligent transportation system (ITS) are studied in this paper. The principles and characteristics of wireless location determination technologies are introduced and the characteristics of GSM useful for location determination are also summarized. An experimental positioning system based on GSM is proposed, and the architecture is described. TOA method based on GSM signals and TDOA method are used in the experimental system. Moreover, the methods are simulated. The performance of the positioning methods is assessed in the simulation environment, and the accuracy for 67% mobile stations (MS) is 70m in urban areas.

  17. Fuzzy Concurrent Object Oriented Expert System for Fault Diagnosis in 8085 Microprocessor Based System Board

    Mr.D. V. Kodavade


    Full Text Available With the acceptance of artificial intelligence paradigm, a number of successful artificial intelligence systems were created. Fault diagnosis in microprocessor based boards needs lot of empirical knowledge and expertise and is a true artificial intelligence problem. Research on fault diagnosis in microprocessor based system boards using new fuzzy-object oriented approach is presented in this paper. There are many uncertain situations observed during fault diagnosis. These uncertain situations were handled using fuzzy mathematics properties. Fuzzy inference mechanism is demonstrated using one case study. Some typical faults in 8085 microprocessor board and diagnostic procedures used is presented in this paper.

  18. A novel space-based observation strategy for GEO objects based on daily pointing adjustment of multi-sensors

    Hu, Yun-peng; Li, Ke-bo; Xu, Wei; Chen, Lei; Huang, Jian-yu


    Space-based visible (SBV) program has been proved to be with a large advantage to observe geosynchronous earth orbit (GEO) objects. With the development of SBV observation started from 1996, many strategies have come out for the purpose of observing GEO objects more efficiently. However it is a big challenge to visit all the GEO objects in a relatively short time because of the distribution characteristics of GEO belt and limited field of view (FOV) of sensor. And it's also difficult to keep a high coverage of the GEO belt every day in a whole year. In this paper, a space-based observation strategy for GEO objects is designed based on the characteristics of the GEO belt. The mathematical formula of GEO belt is deduced and the evolvement of GEO objects is illustrated. There are basically two kinds of orientation strategies for most observation satellites, i.e., earth-oriented and inertia-directional. Influences of both strategies to their own observation regions are analyzed and compared with each other. A passive optical instrument with daily attitude-adjusting strategies is proposed to increase the daily coverage rate of GEO objects in a whole year. Furthermore, in order to observe more GEO objects in a relatively short time, the strategy of a satellite with multi-sensors is proposed. The installation parameters between different sensors are optimized, more than 98% of GEO satellites can be observed every day and almost all the GEO satellites can be observed every two days with 3 sensors (FOV: 6° × 6°) on the satellite under the strategy of daily pointing adjustment in a whole year.

  19. Objective Evaluation Method of Steering Comfort Based on Movement Quality Evaluation of Driver Steering Maneuver

    YANG Yiyong; LIU Yahui; WANG Man; JI Run; JI Xuewu


    The existing research of steering comfort mainly focuses on the subjective evaluation, aiming at designing and optimizing the steering system. In the development of steering system, especially the evaluation of steering comfort, the objective evaluation methods considered the kinematic characteristics of driver steering maneuver are not proposed, which means that the objective evaluation of steering cannot be conducted with the evaluation of kinematic characteristics of driver in steering maneuver. In order to propose the objective evaluation methods of steering comfort, the evaluation of steering movement quality of driver is developed on the basis of the study of the kinematic characteristics of steering maneuver. First, the steering motion trajectories of the driver in both comfortable and certain extreme uncomfortable operation conditions are detected using the Vicon motion capture system. The operation conditions are under the restrictions of the vertical height and horizontal distance between steering wheel center and the H-point of driver, and the steering resisting torque else. Next, the movement quality evaluation of driver steering maneuver is assessed using twelve kinds of evaluation indices based on the kinematic analyses of the steering motion trajectories to propose an objective evaluation method. Finally, an integrated discomfort index of steering maneuver is proposed on the basis of the regression analysis of subjective evaluation rating and the movement quality evaluation indices, including the Jerk, Discomfort and Joint Torque indices. The test results show that the proposed integrated discomfort index gives a good fitting with the subjective evaluation of discomfort, which means it can be used to evaluate or predict the discomfort level of steering maneuver. This paper proposes an objective evaluation method of steering comfort based on the movement quality evaluation of driver steering maneuver.

  20. 基于UniNet的对象概念研究%Object Concepts Research Based on UniNet

    周国富; 余鹏; 袁崇义


    This paper discusses concepts on object from UniNet view and shows that there exists the flow of controlin object system except for exchanging of messages between objects. Meanwhile, the paper presents an independent mechanism of object communication separated from object that will result in a more general reuse of object. With helpof the control flow and the data flow, UniNet can describe not only the static features, but also the dynamic featuresof object system, which naturally solve the inheritance anomaly and the flow of data and control. In addition, based on UniNet specification, the object system can be verified easily and create the program code automatically.

  1. Superquadric Based Hierarchical Reconstruction for Virtualizing Free Form Objects from 3D Data

    LIU Weibin; YUAN Baozong


    The superquadric description is usedin modeling the virtual objects in AVR (from ActualReality to Virtual Reality).However,due to the in-trinsic property,the superquadric and its deforma-tion extensions (DSQ) are not flexible enough to de-scribe precisely the complex objects with asymmetryand free form surface.To solve the problem,a hierar-chical reconstruction approach in AVR for virtualizingthe objects with superquadric based models from 3Ddata is developed.Firstly,an initial approximation isproduced by a superquadric fit to the 3D data.Then,the crude superquadric fit is refined by fitting theresidue (distance map) with global and local DirectManipulation of Free-Form Deformation (DMFFD).The key elements of the hierarchical method,includ-ing superquadric fit to 3D data,mathematical detailsand the recursive-fitting algorithm for DMFFD,com-putation of distance maps,adaptive refinement anddecimation of polygon mesh under DMFFD,are pro-posed.An implementation example of hierarchicalreconstruction is presented.The proposed approachis shown competent and efficient for virtualizing thecomplex objects into virtual environment.

  2. Persistent spatial information in the frontal eye field during object-based short-term memory.

    Clark, Kelsey L; Noudoost, Behrad; Moore, Tirin


    Spatial attention is known to gate entry into visual short-term memory, and some evidence suggests that spatial signals may also play a role in binding features or protecting object representations during memory maintenance. To examine the persistence of spatial signals during object short-term memory, the activity of neurons in the frontal eye field (FEF) of macaque monkeys was recorded during an object-based delayed match-to-sample task. In this task, monkeys were trained to remember an object image over a brief delay, regardless of the locations of the sample or target presentation. FEF neurons exhibited visual, delay, and target period activity, including selectivity for sample location and target location. Delay period activity represented the sample location throughout the delay, despite the irrelevance of spatial information for successful task completion. Furthermore, neurons continued to encode sample position in a variant of the task in which the matching stimulus never appeared in their response field, confirming that FEF maintains sample location independent of subsequent behavioral relevance. FEF neurons also exhibited target-position-dependent anticipatory activity immediately before target onset, suggesting that monkeys predicted target position within blocks. These results show that FEF neurons maintain spatial information during short-term memory, even when that information is irrelevant for task performance.

  3. 3-D Laser-Based Multiclass and Multiview Object Detection in Cluttered Indoor Scenes.

    Zhang, Xuesong; Zhuang, Yan; Hu, Huosheng; Wang, Wei


    This paper investigates the problem of multiclass and multiview 3-D object detection for service robots operating in a cluttered indoor environment. A novel 3-D object detection system using laser point clouds is proposed to deal with cluttered indoor scenes with a fewer and imbalanced training data. Raw 3-D point clouds are first transformed to 2-D bearing angle images to reduce the computational cost, and then jointly trained multiple object detectors are deployed to perform the multiclass and multiview 3-D object detection. The reclassification technique is utilized on each detected low confidence bounding box in the system to reduce false alarms in the detection. The RUS-SMOTEboost algorithm is used to train a group of independent binary classifiers with imbalanced training data. Dense histograms of oriented gradients and local binary pattern features are combined as a feature set for the reclassification task. Based on the dalian university of technology (DUT)-3-D data set taken from various office and household environments, experimental results show the validity and good performance of the proposed method.

  4. Digital Image Watermarking for Arbitrarily Shaped Objects Based On SA-DWT

    F. Regragui


    Full Text Available Many image watermarking schemes have been proposed in recent years, but they usually involve embedding a watermark to the entire image without considering only a particular object in the image, which the image owner may be interested in. This paper proposes a watermarking scheme that can embed a watermark to an arbitrarily shaped object in an image. Before embedding, the image owner specifies an object of arbitrary shape that is of a concern to him. Then the object is transformed into the wavelet domain using in place lifting shape adaptive DWT(SADWT and a watermark is embedded by modifying the wavelet coefficients. In order to make the watermark robust and transparent, the watermark is embedded in the average of wavelet blocks using the visual model based on the human visual system. Wavelet coefficients n least significant bits (LSBs are adjusted in concert with the average. Simulation results shows that the proposed watermarking scheme is perceptually invisible and robust against many attacks such as lossy compression (e.g. JPEG, JPEG2000, scaling, adding noise, filtering, etc.

  5. Digital Image Watermarking for Arbitrarily Shaped Objects Based On SA-DWT

    Essaouabi, A; Fegragui, F


    Many image watermarking schemes have been proposed in recent years, but they usually involve embedding a watermark to the entire image without considering only a particular object in the image, which the image owner may be interested in. This paper proposes a watermarking scheme that can embed a watermark to an arbitrarily shaped object in an image. Before embedding, the image owner specifies an object of arbitrary shape that is of a concern to him. Then the object is transformed into the wavelet domain using in place lifting shape adaptive DWT(SADWT) and a watermark is embedded by modifying the wavelet coefficients. In order to make the watermark robust and transparent, the watermark is embedded in the average of wavelet blocks using the visual model based on the human visual system. Wavelet coefficients n least significant bits (LSBs) are adjusted in concert with the average. Simulation results shows that the proposed watermarking scheme is perceptually invisible and robust against many attacks such as loss...

  6. Fast template matching based on grey prediction for real-time object tracking

    Lv, Mingming; Hou, Yuanlong; Liu, Rongzhong; Hou, Runmin


    Template matching is a basic algorithm for image processing, and real-time is a crucial requirement of object tracking. For real-time tracking, a fast template matching algorithm based on grey prediction is presented, where computation cost can be reduced dramatically by minimizing search range. First, location of the tracked object in the current image is estimated by Grey Model (GM). GM(1,1), which is the basic model of grey prediction, can use some known information to foretell the location. Second, the precise position of the object in the frame is computed by template matching. Herein, Sequential Similarity Detection Algorithm (SSDA) with a self-adaptive threshold is employed to obtain the matching position in the neighborhood of the predicted location. The role of threshold in SSDA is important, as a proper threshold can make template matching fast and accurate. Moreover, a practical weighted strategy is utilized to handle scale and rotation changes of the object, as well as illumination changes. The experimental results show the superior performance of the proposed algorithm over the conventional full-search method, especially in terms of executive time.

  7. Defining Simple nD Operations Based on Prosmatic nD Objects

    Arroyo Ohori, K.; Ledoux, H.; Stoter, J.


    An alternative to the traditional approaches to model separately 2D/3D space, time, scale and other parametrisable characteristics in GIS lies in the higher-dimensional modelling of geographic information, in which a chosen set of non-spatial characteristics, e.g. time and scale, are modelled as extra geometric dimensions perpendicular to the spatial ones, thus creating a higher-dimensional model. While higher-dimensional models are undoubtedly powerful, they are also hard to create and manipulate due to our lack of an intuitive understanding in dimensions higher than three. As a solution to this problem, this paper proposes a methodology that makes nD object generation easier by splitting the creation and manipulation process into three steps: (i) constructing simple nD objects based on nD prismatic polytopes - analogous to prisms in 3D -, (ii) defining simple modification operations at the vertex level, and (iii) simple postprocessing to fix errors introduced in the model. As a use case, we show how two sets of operations can be defined and implemented in a dimension-independent manner using this methodology: the most common transformations (i.e. translation, scaling and rotation) and the collapse of objects. The nD objects generated in this manner can then be used as a basis for an nD GIS.

  8. An effective docking strategy for virtual screening based on multi-objective optimization algorithm

    Kang Ling


    Full Text Available Abstract Background Development of a fast and accurate scoring function in virtual screening remains a hot issue in current computer-aided drug research. Different scoring functions focus on diverse aspects of ligand binding, and no single scoring can satisfy the peculiarities of each target system. Therefore, the idea of a consensus score strategy was put forward. Integrating several scoring functions, consensus score re-assesses the docked conformations using a primary scoring function. However, it is not really robust and efficient from the perspective of optimization. Furthermore, to date, the majority of available methods are still based on single objective optimization design. Results In this paper, two multi-objective optimization methods, called MOSFOM, were developed for virtual screening, which simultaneously consider both the energy score and the contact score. Results suggest that MOSFOM can effectively enhance enrichment and performance compared with a single score. For three different kinds of binding sites, MOSFOM displays an excellent ability to differentiate active compounds through energy and shape complementarity. EFMOGA performed particularly well in the top 2% of database for all three cases, whereas MOEA_Nrg and MOEA_Cnt performed better than the corresponding individual scoring functions if the appropriate type of binding site was selected. Conclusion The multi-objective optimization method was successfully applied in virtual screening with two different scoring functions that can yield reasonable binding poses and can furthermore, be ranked with the potentially compromised conformations of each compound, abandoning those conformations that can not satisfy overall objective functions.

  9. Line-Based Object Recognition using Hausdorff Distance: From Range Images to Molecular Secondary Structure

    Guerra, C; Pascucci, V


    Object recognition algorithms are fundamental tools in automatic matching of geometric shapes within a background scene. Many approaches have been proposed in the past to solve the object recognition problem. Two of the key aspects that distinguish them in terms of their practical usability are: (i) the type of input model description and (ii) the comparison criteria used. In this paper we introduce a novel scheme for 3D object recognition based on line segment representation of the input shapes and comparison using the Hausdor distance. This choice of model representation provides the flexibility to apply the scheme in different application areas. We define several variants of the Hausdor distance to compare the models within the framework of well defined metric spaces. We present a matching algorithm that efficiently finds a pattern in a 3D scene. The algorithm approximates a minimization procedure of the Hausdor distance. The output error due to the approximation is guaranteed to be within a known constant bound. Practical results are presented for two classes of objects: (i) polyhedral shapes extracted from segmented range images and (ii) secondary structures of large molecules. In both cases the use of our approximate algorithm allows to match correctly the pattern in the background while achieving the efficiency necessary for practical use of the scheme. In particular the performance is improved substantially with minor degradation of the quality of the matching.

  10. Global stability-based design optimization of truss structures using multiple objectives

    Tugrul Talaslioglu


    This paper discusses the effect of global stability on the optimal size and shape of truss structures taking into account of a nonlinear critical load, truss weight and serviceability at the same time. The nonlinear critical load is computed by arc-length method. In order to increase the accuracy of the estimation of critical load (ignoring material nonlinearity), an eigenvalue analysis is implemented into the arc-length method. Furthermore, a pure pareto-ranking based multi-objective optimization model is employed for the design optimization of the truss structure with multiple objectives. The computational performance of the optimization model is increased by implementing an island model into its evolutionary search mechanism. The proposed design optimization approach is applied for both size and shape optimization of real world trusses including 101, 224 and 444 bars and successful in generating feasible designations in a large and complex design space. It is observed that the computational performance of pareto-ranking based island model is better than the pure pareto-ranking based model. Therefore, pareto-ranking based island model is recommended to optimize the design of truss structure possessing geometric nonlinearity

  11. Object-Based Arctic Sea Ice Feature Extraction through High Spatial Resolution Aerial photos

    Miao, X.; Xie, H.


    High resolution aerial photographs used to detect and classify sea ice features can provide accurate physical parameters to refine, validate, and improve climate models. However, manually delineating sea ice features, such as melt ponds, submerged ice, water, ice/snow, and pressure ridges, is time-consuming and labor-intensive. An object-based classification algorithm is developed to automatically extract sea ice features efficiently from aerial photographs taken during the Chinese National Arctic Research Expedition in summer 2010 (CHINARE 2010) in the MIZ near the Alaska coast. The algorithm includes four steps: (1) the image segmentation groups the neighboring pixels into objects based on the similarity of spectral and textural information; (2) the random forest classifier distinguishes four general classes: water, general submerged ice (GSI, including melt ponds and submerged ice), shadow, and ice/snow; (3) the polygon neighbor analysis separates melt ponds and submerged ice based on spatial relationship; and (4) pressure ridge features are extracted from shadow based on local illumination geometry. The producer's accuracy of 90.8% and user's accuracy of 91.8% are achieved for melt pond detection, and shadow shows a user's accuracy of 88.9% and producer's accuracies of 91.4%. Finally, pond density, pond fraction, ice floes, mean ice concentration, average ridge height, ridge profile, and ridge frequency are extracted from batch processing of aerial photos, and their uncertainties are estimated.

  12. Discriminative boosted forest with convolutional neural network-based patch descriptor for object detection

    Xiang, Tao; Li, Tao; Ye, Mao; Li, Xudong


    Object detection with intraclass variations is challenging. The existing methods have not achieved the optimal combinations of classifiers and features, especially features learned by convolutional neural networks (CNNs). To solve this problem, we propose an object-detection method based on improved random forest and local image patches represented by CNN features. First, we compute CNN-based patch descriptors for each sample by modified CNNs. Then, the random forest is built whose split functions are defined by patch selector and linear projection learned by linear support vector machine. To improve the classification accuracy, the split functions in each depth of the forest make up a local classifier, and all local classifiers are assembled in a layer-wise manner by a boosting algorithm. The main contributions of our approach are summarized as follows: (1) We propose a new local patch descriptor based on CNN features. (2) We define a patch-based split function which is optimized with maximum class-label purity and minimum classification error over the samples of the node. (3) Each local classifier is assembled by minimizing the global classification error. We evaluate the method on three well-known challenging datasets: TUD pedestrians, INRIA pedestrians, and UIUC cars. The experiments demonstrate that our method achieves state-of-the-art or competitive performance.

  13. A building extraction approach for Airborne Laser Scanner data utilizing the Object Based Image Analysis paradigm

    Tomljenovic, Ivan; Tiede, Dirk; Blaschke, Thomas


    In the past two decades Object-Based Image Analysis (OBIA) established itself as an efficient approach for the classification and extraction of information from remote sensing imagery and, increasingly, from non-image based sources such as Airborne Laser Scanner (ALS) point clouds. ALS data is represented in the form of a point cloud with recorded multiple returns and intensities. In our work, we combined OBIA with ALS point cloud data in order to identify and extract buildings as 2D polygons representing roof outlines in a top down mapping approach. We performed rasterization of the ALS data into a height raster for the purpose of the generation of a Digital Surface Model (DSM) and a derived Digital Elevation Model (DEM). Further objects were generated in conjunction with point statistics from the linked point cloud. With the use of class modelling methods, we generated the final target class of objects representing buildings. The approach was developed for a test area in Biberach an der Riß (Germany). In order to point out the possibilities of the adaptation-free transferability to another data set, the algorithm has been applied "as is" to the ISPRS Benchmarking data set of Toronto (Canada). The obtained results show high accuracies for the initial study area (thematic accuracies of around 98%, geometric accuracy of above 80%). The very high performance within the ISPRS Benchmark without any modification of the algorithm and without any adaptation of parameters is particularly noteworthy.

  14. Building and Programming a Smart Robotic System for Distinguishing Objects Based on their Shape and Colour

    Sharari T. M.


    Full Text Available This paper presents a robotic system designed for holding and placing objects based on their colour and shape. The presented robot is given a complete set of instructions of positions and orientation angles for each manipulation motion. The main feature in this paper is that the developed robot used a combination of vision and motion systems for holding and placing the work-objects, mounted on the flat work-plane, based on their shapes and colors. This combination improves the flexibility of manipulation which may help eliminate the use of some expensive manipulation tasks in a variety of industrial applications. The robotic system presented in this paper is designed as an educational robot that possesses the ability for holding-and-placing operations with limited load. To process the various instructions for holding and placing the work objects, a main control unit - Manipulation Control Unit (MCU is used as well as a slave unit that performed the actual instructions from the MCU.

  15. A System based on Adaptive Background Subtraction Approach for Moving Object Detection and Tracking in Videos

    Bahadır KARASULU


    Full Text Available Video surveillance systems are based on video and image processing research areas in the scope of computer science. Video processing covers various methods which are used to browse the changes in existing scene for specific video. Nowadays, video processing is one of the important areas of computer science. Two-dimensional videos are used to apply various segmentation and object detection and tracking processes which exists in multimedia content-based indexing, information retrieval, visual and distributed cross-camera surveillance systems, people tracking, traffic tracking and similar applications. Background subtraction (BS approach is a frequently used method for moving object detection and tracking. In the literature, there exist similar methods for this issue. In this research study, it is proposed to provide a more efficient method which is an addition to existing methods. According to model which is produced by using adaptive background subtraction (ABS, an object detection and tracking system’s software is implemented in computer environment. The performance of developed system is tested via experimental works with related video datasets. The experimental results and discussion are given in the study

  16. Moving Object Tracking and Avoidance Algorithm for Differential Driving AGV Based on Laser Measurement Technology

    Pandu Sandi Pratama


    Full Text Available This paper proposed an algorithm to track the obstacle position and avoid the moving objects for differential driving Automatic Guided Vehicles (AGV system in industrial environment. This algorithm has several abilities such as: to detect the moving objects, to predict the velocity and direction of moving objects, to predict the collision possibility and to plan the avoidance maneuver. For sensing the local environment and positioning, the laser measurement system LMS-151 and laser navigation system NAV-200 are applied. Based on the measurement results of the sensors, the stationary and moving obstacles are detected and the collision possibility is calculated. The velocity and direction of the obstacle are predicted using Kalman filter algorithm. Collision possibility, time, and position can be calculated by comparing the AGV movement and obstacle prediction result obtained by Kalman filter. Finally the avoidance maneuver using the well known tangent Bug algorithm is decided based on the calculation data. The effectiveness of proposed algorithm is verified using simulation and experiment. Several examples of experiment conditions are presented using stationary obstacle, and moving obstacles. The simulation and experiment results show that the AGV can detect and avoid the obstacles successfully in all experimental condition. [Keywords— Obstacle avoidance, AGV, differential drive, laser measurement system, laser navigation system].

  17. Simulated Prosthetic Vision: The Benefits of Computer-Based Object Recognition and Localization.

    Macé, Marc J-M; Guivarch, Valérian; Denis, Grégoire; Jouffrais, Christophe


    Clinical trials with blind patients implanted with a visual neuroprosthesis showed that even the simplest tasks were difficult to perform with the limited vision restored with current implants. Simulated prosthetic vision (SPV) is a powerful tool to investigate the putative functions of the upcoming generations of visual neuroprostheses. Recent studies based on SPV showed that several generations of implants will be required before usable vision is restored. However, none of these studies relied on advanced image processing. High-level image processing could significantly reduce the amount of information required to perform visual tasks and help restore visuomotor behaviors, even with current low-resolution implants. In this study, we simulated a prosthetic vision device based on object localization in the scene. We evaluated the usability of this device for object recognition, localization, and reaching. We showed that a very low number of electrodes (e.g., nine) are sufficient to restore visually guided reaching movements with fair timing (10 s) and high accuracy. In addition, performance, both in terms of accuracy and speed, was comparable with 9 and 100 electrodes. Extraction of high level information (object recognition and localization) from video images could drastically enhance the usability of current visual neuroprosthesis. We suggest that this method-that is, localization of targets of interest in the scene-may restore various visuomotor behaviors. This method could prove functional on current low-resolution implants. The main limitation resides in the reliability of the vision algorithms, which are improving rapidly.

  18. [An object-based information extraction technology for dominant tree species group types].

    Tian, Tian; Fan, Wen-yi; Lu, Wei; Xiao, Xiang


    Information extraction for dominant tree group types is difficult in remote sensing image classification, howevers, the object-oriented classification method using high spatial resolution remote sensing data is a new method to realize the accurate type information extraction. In this paper, taking the Jiangle Forest Farm in Fujian Province as the research area, based on the Quickbird image data in 2013, the object-oriented method was adopted to identify the farmland, shrub-herbaceous plant, young afforested land, Pinus massoniana, Cunninghamia lanceolata and broad-leave tree types. Three types of classification factors including spectral, texture, and different vegetation indices were used to establish a class hierarchy. According to the different levels, membership functions and the decision tree classification rules were adopted. The results showed that the method based on the object-oriented method by using texture, spectrum and the vegetation indices achieved the classification accuracy of 91.3%, which was increased by 5.7% compared with that by only using the texture and spectrum.

  19. Resident Space Object Characterization and Behavior Understanding via Machine Learning and Ontology-based Bayesian Networks

    Furfaro, R.; Linares, R.; Gaylor, D.; Jah, M.; Walls, R.


    In this paper, we present an end-to-end approach that employs machine learning techniques and Ontology-based Bayesian Networks (BN) to characterize the behavior of resident space objects. State-of-the-Art machine learning architectures (e.g. Extreme Learning Machines, Convolutional Deep Networks) are trained on physical models to learn the Resident Space Object (RSO) features in the vectorized energy and momentum states and parameters. The mapping from measurements to vectorized energy and momentum states and parameters enables behavior characterization via clustering in the features space and subsequent RSO classification. Additionally, Space Object Behavioral Ontologies (SOBO) are employed to define and capture the domain knowledge-base (KB) and BNs are constructed from the SOBO in a semi-automatic fashion to execute probabilistic reasoning over conclusions drawn from trained classifiers and/or directly from processed data. Such an approach enables integrating machine learning classifiers and probabilistic reasoning to support higher-level decision making for space domain awareness applications. The innovation here is to use these methods (which have enjoyed great success in other domains) in synergy so that it enables a "from data to discovery" paradigm by facilitating the linkage and fusion of large and disparate sources of information via a Big Data Science and Analytics framework.

  20. Multi-objective scheduling in an agent based Holonic manufacturing system

    T. K. Jana


    Full Text Available The present paper is aimed at multi-objective scheduling in an agent based holonic manufacturing system to satisfy the goal of several communities namely the product, the resource, and the organization simultaneously. In this attempt, first a multi criteria based priority rule is developed following Simple Additive Weight (SAW method under Multi Criteria Decision Making (MCDM environment to rank the products. Accordingly, the products are allowed to select a particular resource for execution by negotiation considering minimum time as criterion. The interests of different communities are accomplished by allocating the ordered rank of products to the ordered rank of resources. Conflict, if arises between products and resources, are resolved by introducing the concept of Early Finish Time (EFT as criterion for task allocation. A scheduling algorithm is proposed for execution of the rule. In view of machine failure, a cooperation strategy is evolved that also optimizes reallocation of the incomplete task. It is concluded that the proposed scheduling algorithm together with the disturbance handling algorithm are poised to satisfy the agent’s local objective as well as organization’s global objective concurrently and are commensurable with multi agent paradigm.

  1. Object-Based Analysis of Aerial Photogrammetric Point Cloud and Spectral Data for Land Cover Mapping

    Debella-Gilo, M.; Bjørkelo, K.; Breidenbach, J.; Rahlf, J.


    The acquisition of 3D point data with the use of both aerial laser scanning (ALS) and matching of aerial stereo images coupled with advances in image processing algorithms in the past years provide opportunities to map land cover types with better precision than before. The present study applies Object-Based Image Analysis (OBIA) to 3D point cloud data obtained from matching of stereo aerial images together with spectral data to map land cover types of the Nord-Trøndelag county of Norway. The multi-resolution segmentation algorithm of the Definiens eCognition™ software is used to segment the scenes into homogenous objects. The objects are then classified into different land cover types using rules created based on the definitions given for each land cover type by the Norwegian Forest and Landscape Institute. The quality of the land cover map was evaluated using data collected in the field as part of the Norwegian National Forest Inventory. The results show that the classification has an overall accuracy of about 80% and a kappa index of about 0.65. OBIA is found to be a suitable method for utilizing 3D remote sensing data for land cover mapping in an effort to replace manual delineation methods.

  2. A web product data management system based on Simple Object Access Protocol


    A new web product data management architecture is presented. The three-tier web architecture and Simple Object Access Protocol (SOAP) are combined to build the web-based product data management (PDM) system which includes three tiers: the user services tier, the business services tier, and the data services tier. The client service component uses the serverside technology, and Extensible Markup Language (XML) web service which uses SOAP as the communication protocol is chosen as the business service component. To illustrate how to build a web-based PDM system using the proposed architecture,a case PDM system which included three logical tires was built. To use the security and central management features of the database, a stored procedure was recommended in the data services tier. The business object was implemented as an XML web service so that client could use standard internet protocols to communicate with the business object from any platform. In order to satisfy users using all sorts of browser, the server-side technology and Microsoft ASP.NET was used to create the dynamic user interface.

  3. Job-shop Scheduling with Multi-objectives Based on Genetic Algorithms

    周亚勤; 李蓓智; 陈革


    The technology of production planning and scheduling is one of the critical technologies that decide whether the automated manufacturing systems can get the expected economy. Job shop scheduling belongs to the special class of NP-hard problems. Most of the algorithms used to optimize this class of problems have an exponential time; that is, the computation time increases exponentially with problem size. In scheduling study, makespan is often considered as the main objective. In this paper, makespan, the due date request of the key jobs, the availability of the key machine, the average wait-time of the jobs, and the similarities between the jobs and so on are taken into accotmt based on the application of mechanical engineering. The job shop scheduling problem with multi-objectives is analyzed and studied by using genetic algorithms based on the mechanics of genetics and natural selection. In this research, the tactics of the coding and decoding and the design of the genetic operators, along with the description of the mathematic model of the multi-objective functions,are presented. Finally an illu-strative example is given to testify the validity of this algorithm.

  4. Fusion of Pixel-based and Object-based Features for Road Centerline Extraction from High-resolution Satellite Imagery

    CAO Yungang


    Full Text Available A novel approach for road centerline extraction from high spatial resolution satellite imagery is proposed by fusing both pixel-based and object-based features. Firstly, texture and shape features are extracted at the pixel level, and spectral features are extracted at the object level based on multi-scale image segmentation maps. Then, extracted multiple features are utilized in the fusion framework of Dempster-Shafer evidence theory to roughly identify the road network regions. Finally, an automatic noise removing algorithm combined with the tensor voting strategy is presented to accurately extract the road centerline. Experimental results using high-resolution satellite imageries with different scenes and spatial resolutions showed that the proposed approach compared favorably with the traditional methods, particularly in the aspect of eliminating the salt noise and conglutination phenomenon.

  5. Detection and object-based classification of offshore oil slicks using ENVISAT-ASAR images.

    Akar, Sertac; Süzen, Mehmet Lutfi; Kaymakci, Nuretdin


    The aim of this study is to propose and test a multi-level methodology for detection of oil slicks in ENVISAT Advanced Synthetic Aperture Radar (ASAR) imagery, which can be used to support the identification of hydrocarbon seeps. We selected Andrusov Ridge in the Central Black Sea as the test study area where extensive hydrocarbon seepages were known to occur continuously. Hydrocarbon seepage from tectonic or stratigraphic origin at the sea floor causes oily gas plumes to rise up to the sea surface and form thin oil films called oil slicks. Microwave sensors like synthetic aperture radar (SAR) are very suitable for ocean remote sensing as they measure the backscattered radiation from the surface and show the roughness of the terrain. Oil slicks dampen the sea waves creating dark patches in the SAR image. The proposed and applied methodology includes three levels: visual interpretation, image filtering and object-based oil spill detection. Level I, after data preparation with visual interpretation, includes dark spots identification and subsets/scenes creation. After this process, the procedure continues with categorization of subsets/scenes into three cases based on contrast difference of dark spots to the surroundings. In level II, by image and morphological filtering, it includes preparation of subsets/scenes for segmentation. Level III includes segmentation and feature extraction which is followed by object-based classification. The object-based classification is applied with the fuzzy membership functions defined by extracted features of ASAR subsets/scenes, where the parameters of the detection algorithms are tuned specifically for each case group. As a result, oil slicks are discriminated from look-alikes with an overall classification accuracy of 83% for oil slicks and 77% for look-alikes obtained by averaging three different cases.

  6. The implementation of contour-based object orientation estimation algorithm in FPGA-based on-board vision system

    Alpatov, Boris; Babayan, Pavel; Ershov, Maksim; Strotov, Valery


    This paper describes the implementation of the orientation estimation algorithm in FPGA-based vision system. An approach to estimate an orientation of objects lacking axial symmetry is proposed. Suggested algorithm is intended to estimate orientation of a specific known 3D object based on object 3D model. The proposed orientation estimation algorithm consists of two stages: learning and estimation. Learning stage is devoted to the exploring of studied object. Using 3D model we can gather set of training images by capturing 3D model from viewpoints evenly distributed on a sphere. Sphere points distribution is made by the geosphere principle. Gathered training image set is used for calculating descriptors, which will be used in the estimation stage of the algorithm. The estimation stage is focusing on matching process between an observed image descriptor and the training image descriptors. The experimental research was performed using a set of images of Airbus A380. The proposed orientation estimation algorithm showed good accuracy in all case studies. The real-time performance of the algorithm in FPGA-based vision system was demonstrated.

  7. Monitoring Post Disturbance Forest Regeneration with Hierarchical Object-Based Image Analysis

    L. Monika Moskal


    Full Text Available The main goal of this exploratory project was to quantify seedling density in post fire regeneration sites, with the following objectives: to evaluate the application of second order image texture (SOIT in image segmentation, and to apply the object-based image analysis (OBIA approach to develop a hierarchical classification. With the utilization of image texture we successfully developed a methodology to classify hyperspatial (high-spatial imagery to fine detail level of tree crowns, shadows and understory, while still allowing discrimination between density classes and mature forest versus burn classes. At the most detailed hierarchical Level I classification accuracies reached 78.8%, a Level II stand density classification produced accuracies of 89.1% and the same accuracy was achieved by the coarse general classification at Level III. Our interpretation of these results suggests hyperspatial imagery can be applied to post-fire forest density and regeneration mapping.

  8. ROCIT : a visual object recognition algorithm based on a rank-order coding scheme.

    Gonzales, Antonio Ignacio; Reeves, Paul C.; Jones, John J.; Farkas, Benjamin D.


    This document describes ROCIT, a neural-inspired object recognition algorithm based on a rank-order coding scheme that uses a light-weight neuron model. ROCIT coarsely simulates a subset of the human ventral visual stream from the retina through the inferior temporal cortex. It was designed to provide an extensible baseline from which to improve the fidelity of the ventral stream model and explore the engineering potential of rank order coding with respect to object recognition. This report describes the baseline algorithm, the model's neural network architecture, the theoretical basis for the approach, and reviews the history of similar implementations. Illustrative results are used to clarify algorithm details. A formal benchmark to the 1998 FERET fafc test shows above average performance, which is encouraging. The report concludes with a brief review of potential algorithmic extensions for obtaining scale and rotational invariance.

  9. Design for Sustainability of Industrial Symbiosis based on Emergy and Multi-objective Particle Swarm Optimization

    Ren, Jingzheng; Liang, Hanwei; Dong, Liang


    approach for supporting decision-making in the design for the sustainability with the implementation of industrial symbiosis in chemical complex. Through incorporating the emergy theory, the model is formulated as a multi-objective approach that can optimize both the economic benefit and sustainable...... performance of the integrated industrial system. A set of emergy based evaluation index are designed. Multi-objective Particle Swarm Algorithm is proposed to solve the model, and the decision-makers are allowed to choose the suitable solutions form the Pareto solutions. An illustrative case has been studied...... by the proposed method, a few of compromises between high profitability and high sustainability can be obtained for the decision-makers/stakeholders to make decision....

  10. Extracting Objects and Events from MPEG Videos for Highlight-based Indexing and Retrieval

    Jinchang Ren


    Full Text Available Automatic recognition of highlights from videos is a fundamental and challenging problem for content-based indexing and retrieval applications. In this paper, we propose techniques to solve this problem using knowledge supported extraction of semantics, and compressed-domain processing is employed for efficiency. Firstly, knowledgebased rules are utilized for shot detection on extracted DCimages, and statistical skin detection is applied for human object detection. Secondly, through filtering outliers in motion vectors, improved detection of camera motions like zooming, panning and tilting are achieved. Video highlight high-level semantics are then automatically extracted via low-level analysis in the detection of human objects and camera motion events, and finally these highlights are taken for shot-level annotation, indexing and retrieval. Results using a large test video data set have demonstrated the accuracy and robustness of the proposed techniques.

  11. Comparison of Planar Parallel Manipulator Architectures based on a Multi-objective Design Optimization Approach

    Chablat, Damien; Ur-Rehman, Raza; Wenger, Philippe


    This paper deals with the comparison of planar parallel manipulator architectures based on a multi-objective design optimization approach. The manipulator architectures are compared with regard to their mass in motion and their regular workspace size, i.e., the objective functions. The optimization problem is subject to constraints on the manipulator dexterity and stiffness. For a given external wrench, the displacements of the moving platform have to be smaller than given values throughout the obtained maximum regular dexterous workspace. The contributions of the paper are highlighted with the study of 3-RPR, 3-RPR and 3-RPR planar parallel manipulator architectures, which are compared by means of their Pareto frontiers obtained with a genetic algorithm.

  12. Multi-Objective Reinforcement Learning for Cognitive Radio-Based Satellite Communications

    Ferreira, Paulo Victor R.; Paffenroth, Randy; Wyglinski, Alexander M.; Hackett, Timothy M.; Bilen, Sven G.; Reinhart, Richard C.; Mortensen, Dale J.


    Previous research on cognitive radios has addressed the performance of various machine-learning and optimization techniques for decision making of terrestrial link properties. In this paper, we present our recent investigations with respect to reinforcement learning that potentially can be employed by future cognitive radios installed onboard satellite communications systems specifically tasked with radio resource management. This work analyzes the performance of learning, reasoning, and decision making while considering multiple objectives for time-varying communications channels, as well as different cross-layer requirements. Based on the urgent demand for increased bandwidth, which is being addressed by the next generation of high-throughput satellites, the performance of cognitive radio is assessed considering links between a geostationary satellite and a fixed ground station operating at Ka-band (26 GHz). Simulation results show multiple objective performance improvements of more than 3.5 times for clear sky conditions and 6.8 times for rain conditions.

  13. Observational Iinearization and tracking objective excitation control strategy based on phasor measurement unit

    QIU Xiaoyan; LI Xingyuan; WANG Xiaoyan


    To improve the transient stability ofmultimachine power systems,observational linearization and tracking objective excitation control laws were derived from the phasor measurement unit (PMU),observational linearization,and tracking objective control theory based on synchronized coordinates and reference generator coordinates.The control strategies utilized real-time state variables obtained by PMU to linearize the state equations of the system,and then the linear optimal control strategy was used to design excitation controllers.The inaccuracy of the local linearization method and the complexity of the system models designed in the exact linearization method for nonlinear systems were avoided.Therefore,the control strategies were applied in real time.Simulation results show that the proposed method can improve the transient stability of power systems more efficiently than nonlinear optimal excitation control.



    Taking AutoCAD2000 as platform, an algorithm for the reconstruction of surface from scattered data points based on VBA is presented. With this core technology customers can be free from traditional AutoCAD as an electronic board and begin to create actual presentation of real-world objects. VBA is not only a very powerful tool of development, but with very simple syntax. Associating with those solids, objects and commands of AutoCAD 2000, VBA notably simplifies previous complex algorithms, graphical presentations and processing, etc. Meanwhile, it can avoid appearance of complex data structure and data format in reverse design with other modeling software. Applying VBA to reverse engineering can greatly improve modeling efficiency and facilitate surface reconstruction.

  15. Validation of a computer based objective structured clinical examination in the assessment of undergraduate dermatology courses

    Feroze Kaliyadan


    Full Text Available Many teaching centers have now adopted objective structured clinical examination (OSCE as an assessment method for undergraduate dermatology courses. A modification of the standard OSCE in dermatology is computer based or electronic OSCE (eOSCE. We attempted to validate the use of a computer-based OSCE in dermatology in a group of fifth year medical students. The scores of the students in the computer-based OSCE showed a strong positive correlation with the scores on the clinical presentation (Pearson′s co-efficient - 0.923, P value <0.000, significant at the 0.01 level and a good correlation with overall scores of the student (Pearson′s co-efficient - 0.728, P value <0.000, significant at the 0.01 level, indicating that this is a reliable method for assessment in dermatology. Generally, the students′ feedback regarding the methods was positive.

  16. Incremental Structured Dictionary Learning for Video Sensor-Based Object Tracking

    Ming Xue


    Full Text Available To tackle robust object tracking for video sensor-based applications, an online discriminative algorithm based on incremental discriminative structured dictionary learning (IDSDL-VT is presented. In our framework, a discriminative dictionary combining both positive, negative and trivial patches is designed to sparsely represent the overlapped target patches. Then, a local update (LU strategy is proposed for sparse coefficient learning. To formulate the training and classification process, a multiple linear classifier group based on a K-combined voting (KCV function is proposed. As the dictionary evolves, the models are also trained to timely adapt the target appearance variation. Qualitative and quantitative evaluations on challenging image sequences compared with state-of-the-art algorithms demonstrate that the proposed tracking algorithm achieves a more favorable performance. We also illustrate its relay application in visual sensor networks.

  17. Multi-Objective Fuzzy Optimum Design Based on Reliability for Offshore Jacket Platforms

    康海贵; 刘未; 翟钢军; 徐发淙; 封盛


    In consideration of the fuzzy constraint boundary and through analysis of structural reliability, a model of structural fuzzy optimum design is established based on reliability for offshore jacket platforms. According to the characteristics of offshore jacket platforms, the tolerance coefficient of the constraint boundary is determined with the fuzzy optimization method. The optimum level cut set λ *, which is the intersection of the fuzzy constraint set and fuzzy objective set, is determined with the bound search method, and then the fuzzy optimum solution to the fuzzy optimization problem is obtained. The central offshore platform SZ36-1 is designed with the fuzzy optimum model based on reliability; the results are compared with those from deterministic optimum design and fuzzy optimum design. The tendency of design variables in the above three methods and its reasons are analyzed. The results of an example show that the fuzzy optimum design based on reliability is stable and reliable.

  18. Ecosystem-based management objectives for the North Sea: riding the forage fish rollercoaster

    Dickey-Collas, Mark; Engelhard, Georg H.; Rindorf, Anna


    The North Sea provides a useful model for considering forage fish (FF) within ecosystem-based management as it has a complex assemblage of FF species. This paper is designed to encourage further debate and dialogue between stakeholders about management objectives. Changing the management...... of fisheries on FF will have economic consequences for all fleets in the North Sea. The predators that are vulnerable to the depletion of FF are Sandwich terns, great skua and common guillemots, and to a lesser extent, marine mammals. Comparative evaluations of management strategies are required to consider......-based pool of biomass and as species components of the system by managers and modellers. Policy developers should not consider the knowledge base robust enough to embark on major projects of ecosystem engineering. Management plans appear able to maintain sustainable exploitation in the short term. Changes...

  19. A Grid Based Cooperative Co-evolutionary Multi-Objective Algorithm

    Fard, Sepehr Meshkinfam; Hamzeh, Ali; Ziarati, Koorush

    In this paper, a well performing approach in the context of Multi-Objective Evolutionary Algorithm (MOEA) is investigated due to its complexity. This approach called NSCCGA is based on previously introduced approach called NSGA-II. NSCCGA performs better than NSGA-II but with a heavy load of computational complexity. Here, a novel approach called GBCCGA is introduced based on MOCCGA with some modifications. The main difference between GBCCGA and MOCCGA is in their niching technique which instead of the traditional sharing mechanism in MOCCGA, a novel grid-based technique is used in GBCCGA. The reported results show that GBCCGA performs roughly the same as NSCCGA but with very low computational complexity with respect to the original MOCCGA.

  20. Segmentation and Classification of Remotely Sensed Images: Object-Based Image Analysis

    Syed, Abdul Haleem

    Land-use-and-land-cover (LULC) mapping is crucial in precision agriculture, environmental monitoring, disaster response, and military applications. The demand for improved and more accurate LULC maps has led to the emergence of a key methodology known as Geographic Object-Based Image Analysis (GEOBIA). The core idea of the GEOBIA for an object-based classification system (OBC) is to change the unit of analysis from single-pixels to groups-of-pixels called `objects' through segmentation. While this new paradigm solved problems and improved global accuracy, it also raised new challenges such as the loss of accuracy in categories that are less abundant, but potentially important. Although this trade-off may be acceptable in some domains, the consequences of such an accuracy loss could be potentially fatal in others (for instance, landmine detection). This thesis proposes a method to improve OBC performance by eliminating such accuracy losses. Specifically, we examine the two key players of an OBC system: Hierarchical Segmentation and Supervised Classification. Further, we propose a model to understand the source of accuracy errors in minority categories and provide a method called Scale Fusion to eliminate those errors. This proposed fusion method involves two stages. First, the characteristic scale for each category is estimated through a combination of segmentation and supervised classification. Next, these estimated scales (segmentation maps) are fused into one combined-object-map. Classification performance is evaluated by comparing results of the multi-cut-and-fuse approach (proposed) to the traditional single-cut (SC) scale selection strategy. Testing on four different data sets revealed that our proposed algorithm improves accuracy on minority classes while performing just as well on abundant categories. Another active obstacle, presented by today's remotely sensed images, is the volume of information produced by our modern sensors with high spatial and

  1. Balancing Multiple Objectives Using a Classification-Based Forest Management System in Changbai Mountains, China

    Zhao, Fuqiang; Yang, Jian; Liu, Zhihua; Dai, Limin; He, Hong S.


    Contemporary forest management often consists of multiple objectives, including restoration of human-impacted forested landscapes toward their range of natural variability (RNV) and sustainable levels of timber production. Balancing multiple management objectives is often challenging due to intrinsic conflicts between these objectives and a lack of reference conditions for evaluating the effectiveness of forest restoration efforts. We used a spatially explicit forest landscape model to assess how well a classification-based forest management (CFM) system could achieve multiple objectives in a Korean pine broadleaf mixed forest ecosystem at Changbai Mountain in Northeast China. The CFM system divided the forest landscape into three management areas (Commercial Forest, Special Ecological Welfare Forest, and General Ecological Welfare Forest), each with its own management objectives and prescriptions, but with an overall goal of increasing the ecological and economic sustainability of the entire landscape. The zoning approach adopted in the Chinese CFM system is very similar to the TRIAD approach that is being advocated for managing public forests in Canada. In this study, a natural disturbance scenario and seven harvest scenarios (one identical to the current harvest regime and six alternative scenarios) were simulated to examine how tree species composition, age structure, and timber production at the landscape level can be affected by different strategies under the CFM system. The results indicated that the current forest management regime would not only fail to reach the designated timber production level but also move the forest landscape far away from its RNV. In order to return the currently altered forest landscape to approach its RNV while providing a stable level of timber production over time, harvest intensities should be reduced to a level that is equivalent to the amount of timber removals that would occur under the natural disturbances; and the

  2. Automated multi-objective calibration of biological agent-based simulations.

    Read, Mark N; Alden, Kieran; Rose, Louis M; Timmis, Jon


    Computational agent-based simulation (ABS) is increasingly used to complement laboratory techniques in advancing our understanding of biological systems. Calibration, the identification of parameter values that align simulation with biological behaviours, becomes challenging as increasingly complex biological domains are simulated. Complex domains cannot be characterized by single metrics alone, rendering simulation calibration a fundamentally multi-metric optimization problem that typical calibration techniques cannot handle. Yet calibration is an essential activity in simulation-based science; the baseline calibration forms a control for subsequent experimentation and hence is fundamental in the interpretation of results. Here, we develop and showcase a method, built around multi-objective optimization, for calibrating ABSs against complex target behaviours requiring several metrics (termed objectives) to characterize. Multi-objective calibration (MOC) delivers those sets of parameter values representing optimal trade-offs in simulation performance against each metric, in the form of a Pareto front. We use MOC to calibrate a well-understood immunological simulation against both established a priori and previously unestablished target behaviours. Furthermore, we show that simulation-borne conclusions are broadly, but not entirely, robust to adopting baseline parameter values from different extremes of the Pareto front, highlighting the importance of MOC's identification of numerous calibration solutions. We devise a method for detecting overfitting in a multi-objective context, not previously possible, used to save computational effort by terminating MOC when no improved solutions will be found. MOC can significantly impact biological simulation, adding rigour to and speeding up an otherwise time-consuming calibration process and highlighting inappropriate biological capture by simulations that cannot be well calibrated. As such, it produces more accurate

  3. Web of Objects Based Ambient Assisted Living Framework for Emergency Psychiatric State Prediction

    Alam, Md Golam Rabiul; Abedin, Sarder Fakhrul; Al Ameen, Moshaddique; Hong, Choong Seon


    Ambient assisted living can facilitate optimum health and wellness by aiding physical, mental and social well-being. In this paper, patients’ psychiatric symptoms are collected through lightweight biosensors and web-based psychiatric screening scales in a smart home environment and then analyzed through machine learning algorithms to provide ambient intelligence in a psychiatric emergency. The psychiatric states are modeled through a Hidden Markov Model (HMM), and the model parameters are estimated using a Viterbi path counting and scalable Stochastic Variational Inference (SVI)-based training algorithm. The most likely psychiatric state sequence of the corresponding observation sequence is determined, and an emergency psychiatric state is predicted through the proposed algorithm. Moreover, to enable personalized psychiatric emergency care, a service a web of objects-based framework is proposed for a smart-home environment. In this framework, the biosensor observations and the psychiatric rating scales are objectified and virtualized in the web space. Then, the web of objects of sensor observations and psychiatric rating scores are used to assess the dweller’s mental health status and to predict an emergency psychiatric state. The proposed psychiatric state prediction algorithm reported 83.03 percent prediction accuracy in an empirical performance study. PMID:27608023

  4. Multi-objective Decision Based Available Transfer Capability in Deregulated Power System Using Heuristic Approaches

    Pasam, Gopi Krishna; Manohar, T. Gowri


    Determination of available transfer capability (ATC) requires the use of experience, intuition and exact judgment in order to meet several significant aspects in the deregulated environment. Based on these points, this paper proposes two heuristic approaches to compute ATC. The first proposed heuristic algorithm integrates the five methods known as continuation repeated power flow, repeated optimal power flow, radial basis function neural network, back propagation neural network and adaptive neuro fuzzy inference system to obtain ATC. The second proposed heuristic model is used to obtain multiple ATC values. Out of these, a specific ATC value will be selected based on a number of social, economic, deregulated environmental constraints and related to specific applications like optimization, on-line monitoring, and ATC forecasting known as multi-objective decision based optimal ATC. The validity of results obtained through these proposed methods are scrupulously verified on various buses of the IEEE 24-bus reliable test system. The results presented and derived conclusions in this paper are very useful for planning, operation, maintaining of reliable power in any power system and its monitoring in an on-line environment of deregulated power system. In this way, the proposed heuristic methods would contribute the best possible approach to assess multiple objective ATC using integrated methods.

  5. Multi-Objective Predictive Balancing Control of Battery Packs Based on Predictive Current

    Wenbiao Li


    Full Text Available Various balancing topology and control methods have been proposed for the inconsistency problem of battery packs. However, these strategies only focus on a single objective, ignore the mutual interaction among various factors and are only based on the external performance of the battery pack inconsistency, such as voltage balancing and state of charge (SOC balancing. To solve these problems, multi-objective predictive balancing control (MOPBC based on predictive current is proposed in this paper, namely, in the driving process of an electric vehicle, using predictive control to predict the battery pack output current the next time. Based on this information, the impact of the battery pack temperature caused by the output current can be obtained. Then, the influence is added to the battery pack balancing control, which makes the present degradation, temperature, and SOC imbalance achieve balance automatically due to the change of the output current the next moment. According to MOPBC, the simulation model of the balancing circuit is built with four cells in Matlab/Simulink. The simulation results show that MOPBC is not only better than the other traditional balancing control strategies but also reduces the energy loss in the balancing process.

  6. Preliminary Results of Earthquake-Induced Building Damage Detection with Object-Based Image Classification

    Sabuncu, A.; Uca Avci, Z. D.; Sunar, F.


    Earthquakes are the most destructive natural disasters, which result in massive loss of life, infrastructure damages and financial losses. Earthquake-induced building damage detection is a very important step after earthquakes since earthquake-induced building damage is one of the most critical threats to cities and countries in terms of the area of damage, rate of collapsed buildings, the damage grade near the epicenters and also building damage types for all constructions. Van-Ercis (Turkey) earthquake (Mw= 7.1) was occurred on October 23th, 2011; at 10:41 UTC (13:41 local time) centered at 38.75 N 43.36 E that places the epicenter about 30 kilometers northern part of the city of Van. It is recorded that, 604 people died and approximately 4000 buildings collapsed or seriously damaged by the earthquake. In this study, high-resolution satellite images of Van-Ercis, acquired by Quickbird-2 (Digital Globe Inc.) after the earthquake, were used to detect the debris areas using an object-based image classification. Two different land surfaces, having homogeneous and heterogeneous land covers, were selected as case study areas. As a first step of the object-based image processing, segmentation was applied with a convenient scale parameter and homogeneity criterion parameters. As a next step, condition based classification was used. In the final step of this preliminary study, outputs were compared with streetview/ortophotos for the verification and evaluation of the classification accuracy.

  7. Robust skin color-based moving object detection for video surveillance

    Kaliraj, Kalirajan; Manimaran, Sudha


    Robust skin color-based moving object detection for video surveillance is proposed. The objective of the proposed algorithm is to detect and track the target under complex situations. The proposed framework comprises four stages, which include preprocessing, skin color-based feature detection, feature classification, and target localization and tracking. In the preprocessing stage, the input image frame is smoothed using averaging filter and transformed into YCrCb color space. In skin color detection, skin color regions are detected using Otsu's method of global thresholding. In the feature classification, histograms of both skin and nonskin regions are constructed and the features are classified into foregrounds and backgrounds based on Bayesian skin color classifier. The foreground skin regions are localized by a connected component labeling process. Finally, the localized foreground skin regions are confirmed as a target by verifying the region properties, and nontarget regions are rejected using the Euler method. At last, the target is tracked by enclosing the bounding box around the target region in all video frames. The experiment was conducted on various publicly available data sets and the performance was evaluated with baseline methods. It evidently shows that the proposed algorithm works well against slowly varying illumination, target rotations, scaling, fast, and abrupt motion changes.

  8. Web of Objects Based Ambient Assisted Living Framework for Emergency Psychiatric State Prediction.

    Alam, Md Golam Rabiul; Abedin, Sarder Fakhrul; Al Ameen, Moshaddique; Hong, Choong Seon


    Ambient assisted living can facilitate optimum health and wellness by aiding physical, mental and social well-being. In this paper, patients' psychiatric symptoms are collected through lightweight biosensors and web-based psychiatric screening scales in a smart home environment and then analyzed through machine learning algorithms to provide ambient intelligence in a psychiatric emergency. The psychiatric states are modeled through a Hidden Markov Model (HMM), and the model parameters are estimated using a Viterbi path counting and scalable Stochastic Variational Inference (SVI)-based training algorithm. The most likely psychiatric state sequence of the corresponding observation sequence is determined, and an emergency psychiatric state is predicted through the proposed algorithm. Moreover, to enable personalized psychiatric emergency care, a service a web of objects-based framework is proposed for a smart-home environment. In this framework, the biosensor observations and the psychiatric rating scales are objectified and virtualized in the web space. Then, the web of objects of sensor observations and psychiatric rating scores are used to assess the dweller's mental health status and to predict an emergency psychiatric state. The proposed psychiatric state prediction algorithm reported 83.03 percent prediction accuracy in an empirical performance study.

  9. Common toxicities and objective response rate in metastatic colorectal cancer patients treated with irinotecan based regimens

    Liu Huang; Xin Liao; Qianqian Yu; Qiang Fu; Kai Qin; Huanlei Wu; Lihong Zhang; Xianglin Yuan


    Objective: The aim of our study was to investigate if common toxicities are correlated to objective response rate (ORR) in metastatic colorectal cancer (mCRC) patients treated by irinotecan based regimens. Methods: Univariate and multivariate logistic regression analyses were performed to evaluate correlations between common toxicities and binary ORR in 106 mCRC patients from a prospective cohort treated with irinotecan based regimens. Results: The most frequent severe toxicities (Grade 3/4) were as follows: neutropenia (27.4%), diarrhea (16.9%), leucopenia (12.6%), vomiting (3.2%) and thrombocytopenia (2.1%). Thrombocytosis was observed in 25 (26.3%) patients. ORR was 25.3%. Thrombocytopenia (P = 0.014), line of chemotherapy (P = 0.028) and thrombocytosis (P = 0.033) were correlated with ORR in univariate analysis. In multivariate analysis, thrombocytopenia (odds ratio [OR] = 8.600, 95% confidence interval [CI] = 1.705–43.385, P = 0.009) and first line chemotherapy (OR = 5.155, 95% CI = 1.153–23.256, P = 0.032) positively related to ORR. Conclusion: Throm-bocytopenia may be an indicator of ORR in mCRC patients treated by irinotecan plus 5-fluorouracil/capecitabine. Evidence is not strong enough to prove that irinotecan based regimens-induced diarrhea, leucopenia, neutropenia or vomiting is associ-ated with ORR.

  10. Microsoft Kinect-based Continuous Performance Test: An Objective Attention Deficit Hyperactivity Disorder Assessment

    Delgado-Gomez, David; Masó-Besga, Antonio Eduardo; Vallejo-Oñate, Silvia; Baltasar Tello, Itziar; Arrua Duarte, Elsa; Vera Varela, María Constanza; Carballo, Juan; Baca-García, Enrique


    Background One of the major challenges in mental medical care is finding out new instruments for an accurate and objective evaluation of the attention deficit hyperactivity disorder (ADHD). Early ADHD identification, severity assessment, and prompt treatment are essential to avoid the negative effects associated with this mental condition. Objective The aim of our study was to develop a novel ADHD assessment instrument based on Microsoft Kinect, which identifies ADHD cardinal symptoms in order to provide a more accurate evaluation. Methods A group of 30 children, aged 8-12 years (10.3 [SD 1.4]; male 70% [21/30]), who were referred to the Child and Adolescent Psychiatry Unit of the Department of Psychiatry at Fundación Jiménez Díaz Hospital (Madrid, Spain), were included in this study. Children were required to meet the Diagnostic and Statistical Manual of Mental Disorders (DSM-5) criteria of ADHD diagnosis. One of the parents or guardians of the children filled the Spanish version of the Strengths and Weaknesses of ADHD Symptoms and Normal Behavior (SWAN) rating scale used in clinical practice. Each child conducted a Kinect-based continuous performance test (CPT) in which the reaction time (RT), the commission errors, and the time required to complete the reaction (CT) were calculated. The correlations of the 3 predictors, obtained using Kinect methodology, with respect to the scores of the SWAN scale were calculated. Results The RT achieved a correlation of -.11, -.29, and -.37 with respect to the inattention, hyperactivity, and impulsivity factors of the SWAN scale. The correlations of the commission error with respect to these 3 factors were -.03, .01, and .24, respectively. Conclusions Our findings show a relation between the Microsoft Kinect-based version of the CPT and ADHD symptomatology assessed through parental report. Results point out the importance of future research on the development of objective measures for the diagnosis of ADHD among children

  11. BATMAN: a DMD-based multi-object spectrograph on Galileo telescope

    Zamkotsian, Frederic; Spano, Paolo; Lanzoni, Patrick; Ramarijaona, Harald; Moschetti, Manuele; Riva, Marco; Bon, William; Nicastro, Luciano; Molinari, Emilio; Cosentino, Rosario; Ghedina, Adriano; Gonzalez, Manuel; Di Marcantonio, Paolo; Coretti, Igor; Cirami, Roberto; Zerbi, Filippo; Valenziano, Luca


    Next-generation infrared astronomical instrumentation for ground-based and space telescopes could be based on MOEMS programmable slit masks for multi-object spectroscopy (MOS). This astronomical technique is used extensively to investigate the formation and evolution of galaxies. We are developing a 2048x1080 Digital-Micromirror-Device-based (DMD) MOS instrument to be mounted on the Galileo telescope and called BATMAN. A two-arm instrument has been designed for providing in parallel imaging and spectroscopic capabilities. The field of view (FOV) is 6.8 arcmin x 3.6 arcmin with a plate scale of 0.2 arcsec per micromirror. The wavelength range is in the visible and the spectral resolution is R=560 for 1 arcsec object (typical slit size). The two arms will have 2k x 4k CCD detectors. ROBIN, a BATMAN demonstrator, has been designed, realized and integrated. It permits to determine the instrument integration procedure, including optics and mechanics integration, alignment procedure and optical quality. First images and spectra have been obtained and measured: typical spot diameters are within 1.5 detector pixels, and spectra generated by one micro-mirror slits are displayed with this optical quality over the whole visible wavelength range. Observation strategies are studied and demonstrated for the scientific optimization strategy over the whole FOV. BATMAN on the sky is of prime importance for characterizing the actual performance of this new family of MOS instruments, as well as investigating the operational procedures on astronomical objects. This instrument will be placed on the Telescopio Nazionale Galileo mid-2015.

  12. An operational framework for object-based land use classification of heterogeneous rural landscapes

    Watmough, Gary Richard; Palm, Cheryl; Sullivan, Clare


    and transferable land use classification definitions and algorithms. We present an operational framework for classifying VHR satellite data in heterogeneous rural landscapes using an object-based and random forest classifier. The framework overcomes the challenges of classifying VHR data in anthropogenic......The characteristics of very high resolution (VHR) satellite data are encouraging development agencies to investigate its use in monitoring and evaluation programmes. VHR data pose challenges for land use classification of heterogeneous rural landscapes as it is not possible to develop generalised...

  13. Multi-agent reinforcement learning based on policies of global objective


    In general-sum games, taking all agent's collective rationality into account, we define agents' global objective,and propose a novel multi-agent reinforcement learning(RL) algorithm based on global policy. In each learning step, all agents commit to select the global policy to achieve the global goal. We prove this learning algorithm converges given certain restrictions on stage games of learned Q values, and show that it has quite lower computation time complexity than already developed multi-agent learning algorithms for general-sum games. An example is analyzed to show the algorithm' s merits.


    XU Jiuping


    This paper presents a general solution procedure and an interactive fuzzy satisfying method for a kind of fuzzy multi-objective linear programming problems based on interval valued fuzzy sets. Firstly, a fuzzy set of the fuzzy solutions, which can be focused on providing complete information for the final decision, can be obtained by the proposed tolerance analysis of a non-dominated set. Secondly, the satisfying solution for the decisionmaker can be derived from Pareto optimal solutions by updating the current reference membership levels on the basis of the current levels of the membership functions together with the trade-off rates between the membership functions.

  15. Object-based mental rotation and visual perspective-taking in typical development and Williams syndrome.

    Broadbent, Hannah J; Farran, Emily K; Tolmie, Andrew


    This study examined Object-based (OB) rotation and Visual Perspective-Taking (VPT) abilities in Williams syndrome (WS) compared to typically developing (TD) 5-10-year-olds. Extensive difficulties with both types of imagined rotation were observed in WS; WS performance was in line with the level of ability observed in TD 5-year-olds. However, an atypical pattern of errors on OB and VPT tasks was observed in WS compared to TD groups. Deficits in imagined rotations are consistent with known atypical cortical development in WS. Such difficulties in updating the position of the self following movement in WS may have implications for large-scale spatial navigation.

  16. A character recognition scheme based on object oriented design for Tibetan buddhist texts

    Chen-Yuan Liu


    Full Text Available The purpose of this study is to develop a plausible method to code and compile Buddhist texts from original Tibetan scripts into Romanized form. Using GUI (Graphical User Interface based on Object Oriented Design, a dictionary of Tibetan characters can be easily made for Buddhist literature researchers. It is hoped that a computer system capable of highly accurate character recognition will be actively used by all scholars engaged in Buddhist literature research. In the present study, an efficient automatic recognition method for Tibetan characters is established. The result of the experiments performed is that the recognition rate achieved is 99.4% for 28,954 characters.

  17. Method of Designing Missile Controller Based on Multi-Objective Optimization

    LIN Bo; MENG Xiu-yun; LIU Zao-zhen


    A method of designing robust controller based on genetic algorithm is presented in order to overcome the drawback of manual modification and trial in designing the control system of missile. Specification functions which reflect the dynamic performance in time domain and robustness in frequency domain are presented,then dynamic/static performance, control cost and robust stability are incorporated into a multi-objective optimization problem. Genetic algorithm is used to solve the problem and achieve the optimal controller directly.Simulation results show that the controller provides a good stability and offers a good dynamic performance in a large flight envelope. The results also validate the effectiveness of the method.

  18. An image-tracking algorithm based on object center distance-weighting and image feature recognition

    JIANG Shuhong; WANG Qin; ZHANG Jianqiu; HU Bo


    Areal-time image-tracking algorithm is proposed.which gives small weights to pixels farther from the object center and uses the quantized image gray scales as a template.It identifies the target's location by the mean-shift iteration method and arrives at the target's scale by using image feature recognition.It improves the kernel-based algorithm in tracking scale-changing targets.A decimation mcthod is proposed to track large-sized targets and real-time experimental results verify the effectiveness of the proposed algorithm.


    Zhang Lele; Tan Nanlin; Zhang Huadi; Liu Cai


    To simulate the process of cold roll-forming process, a new method is adopted.The theoretical foundation of this method is an elastic-plastic large deformation spline finite strip method based on object-oriented programming.Combined with the computer graphics technology, the visual simulation of cold roll-forming is completed and the system is established.By analyzing common channel steel, the process is shown and explained including theory method, model and result display.So the simulation system is already a kind of mature and effective tool to analyze the process of cold roll forming.

  20. Browse evaluation of tall shrubs based on direct measurement of a management objective

    Keigley, R.B.; Frisina, M.R.; Kitchen, Stanley G.; Pendleton, Rosemary L.; Monaco, Thomas A.; Vernon, Jason


    The monitoring of Geyer willow was based on the following management objective: Browsing will prevent fewer than 50 percent of Geyer willow shrubs from growing taller than 3 m . Three questions were addressed: (1) Is browsing a potential factor? (2) If so, can young plants grow taller than 3 meters? (3) If not, is browsing the dominant factor? All shrubs were intensely browsed. With a post-browsing growth rate of 5.0 cm per yr, no shrub could grow 3 m tall. Analyses of stem growth rate excluded dominant roles for climate and plant vigor. Browsing and stem age were the dominant factors that limited growth to 3 m tall.

  1. Multi-objective optimization of a 3D vaneless diffuser based on fuzzy theory

    Chuang GAO; Chuangang GU; Tong WANG; Xinwei SHU


    An optimization model based on fuzzy theory was set up and the corresponding Interactive modified simplex (IMS) method was developed to solve it. Both static pressure recovery and total pressure loss were considered in the model. Computational fluid dynamics (CFD) method was applied to solve the Reynolds-Averaged Navier-Stokes equation (RANS) and to find flow field distribution to get the value of the object function. After receiving the new shroud curve, grid movement and redrawing technology were adopted to avoid grid-line crossing and negative cells. The shroud curve was fitted with B-spline. The optimized results concur with the results reported in references.

  2. Intuitive Terrain Reconstruction Using Height Observation-Based Ground Segmentation and 3D Object Boundary Estimation

    Sungdae Sim


    Full Text Available Mobile robot operators must make rapid decisions based on information about the robot’s surrounding environment. This means that terrain modeling and photorealistic visualization are required for the remote operation of mobile robots. We have produced a voxel map and textured mesh from the 2D and 3D datasets collected by a robot’s array of sensors, but some upper parts of objects are beyond the sensors’ measurements and these parts are missing in the terrain reconstruction result. This result is an incomplete terrain model. To solve this problem, we present a new ground segmentation method to detect non-ground data in the reconstructed voxel map. Our method uses height histograms to estimate the ground height range, and a Gibbs-Markov random field model to refine the segmentation results. To reconstruct a complete terrain model of the 3D environment, we develop a 3D boundary estimation method for non-ground objects. We apply a boundary detection technique to the 2D image, before estimating and refining the actual height values of the non-ground vertices in the reconstructed textured mesh. Our proposed methods were tested in an outdoor environment in which trees and buildings were not completely sensed. Our results show that the time required for ground segmentation is faster than that for data sensing, which is necessary for a real-time approach. In addition, those parts of objects that were not sensed are accurately recovered to retrieve their real-world appearances.

  3. Intuitive terrain reconstruction using height observation-based ground segmentation and 3D object boundary estimation.

    Song, Wei; Cho, Kyungeun; Um, Kyhyun; Won, Chee Sun; Sim, Sungdae


    Mobile robot operators must make rapid decisions based on information about the robot's surrounding environment. This means that terrain modeling and photorealistic visualization are required for the remote operation of mobile robots. We have produced a voxel map and textured mesh from the 2D and 3D datasets collected by a robot's array of sensors, but some upper parts of objects are beyond the sensors' measurements and these parts are missing in the terrain reconstruction result. This result is an incomplete terrain model. To solve this problem, we present a new ground segmentation method to detect non-ground data in the reconstructed voxel map. Our method uses height histograms to estimate the ground height range, and a Gibbs-Markov random field model to refine the segmentation results. To reconstruct a complete terrain model of the 3D environment, we develop a 3D boundary estimation method for non-ground objects. We apply a boundary detection technique to the 2D image, before estimating and refining the actual height values of the non-ground vertices in the reconstructed textured mesh. Our proposed methods were tested in an outdoor environment in which trees and buildings were not completely sensed. Our results show that the time required for ground segmentation is faster than that for data sensing, which is necessary for a real-time approach. In addition, those parts of objects that were not sensed are accurately recovered to retrieve their real-world appearances.

  4. CFD-based multi-objective optimization method for ship design

    Tahara, Yusuke; Tohyama, Satoshi; Katsui, Tokihiro


    This paper concerns development and demonstration of a computational fluid dynamics (CFD)-based multi-objective optimization method for ship design. Three main components of the method, i.e. computer-aided design (CAD), CFD, and optimizer modules are functionally independent and replaceable. The CAD used in the present study is NAPA system, which is one of the leading CAD systems in ship design. The CFD method is FLOWPACK version 2004d, a Reynolds-averaged Navier-Stokes (RaNS) solver developed by the present authors. The CFD method is implemented into a self-propulsion simulator, where the RaNS solver is coupled with a propeller-performance program. In addition, a maneuvering simulation model is developed and applied to predict ship maneuverability performance. Two nonlinear optimization algorithms are used in the present study, i.e. the successive quadratic programming and the multi-objective genetic algorithm, while the former is mainly used to verify the results from the latter. For demonstration of the present method, a multi-objective optimization problem is formulated where ship propulsion and maneuverability performances are considered. That is, the aim is to simultaneously minimize opposite hydrodynamic performances in design tradeoff. In the following, an overview of the present method is given, and results are presented and discussed for tanker stern optimization problem including detailed verification work on the present numerical schemes.

  5. Real-time framework for tensor-based image enhancement for object classification

    Cyganek, Bogusław; Smołka, Bogdan


    In many practical situations visual pattern recognition is vastly burdened by low quality of input images due to noise, geometrical distortions, as well as low quality of the acquisition hardware. However, although there are techniques of image quality improvements, such as nonlinear filtering, there are only few attempts reported in the literature that try to build these enhancement methods into a complete chain for multi-dimensional object recognition such as color video or hyperspectral images. In this work we propose a joint multilinear signal filtering and classification system built upon the multi-dimensional (tensor) approach. Tensor filtering is performed by the multi-dimensional input signal projection into the tensor subspace spanned by the best-rank tensor decomposition method. On the other hand, object classification is done by construction of the tensor sub-space constructed based on the Higher-Order Singular Value Decomposition method applied to the prototype patters. In the experiments we show that the proposed chain allows high object recognition accuracy in the real-time even from the poor quality prototypes. Even more importantly, the proposed framework allows unified classification of signals of any dimensions, such as color images or video sequences which are exemplars of 3D and 4D tensors, respectively. The paper discussed also some practical issues related to implementation of the key components of the proposed system.

  6. Computer-aided design–computer-aided engineering associative feature-based heterogeneous object modeling

    Jikai Liu


    Full Text Available Conventionally, heterogeneous object modeling methods paid limited attention to the concurrent modeling of geometry design and material composition distribution. Procedural method was normally employed to generate the geometry first and then determine the heterogeneous material distribution, which ignores the mutual influence. Additionally, limited capability has been established about irregular material composition distribution modeling with strong local discontinuities. This article overcomes these limitations by developing the computer-aided design–computer-aided engineering associative feature-based heterogeneous object modeling method. Level set functions are applied to model the geometry within computer-aided design module, which enables complex geometry modeling. Finite element mesh is applied to store the local material compositions within computer-aided engineering module, which allows any local discontinuities. Then, the associative feature concept builds the correspondence relationship between these modules. Additionally, the level set geometry and material optimization method are developed to concurrently generate the geometry and material information which fills the contents of the computer-aided design–computer-aided engineering associative feature model. Micro-geometry is investigated as well, instead of only the local material composition. A few cases are studied to prove the effectiveness of this new heterogeneous object modeling method.

  7. Associating optical measurements of MEO and GEO objects using Population-Based Meta-Heuristic methods

    Zittersteijn, M.; Vananti, A.; Schildknecht, T.; Dolado Perez, J. C.; Martinot, V.


    Currently several thousands of objects are being tracked in the MEO and GEO regions through optical means. The problem faced in this framework is that of Multiple Target Tracking (MTT). The MTT problem quickly becomes an NP-hard combinatorial optimization problem. This means that the effort required to solve the MTT problem increases exponentially with the number of tracked objects. In an attempt to find an approximate solution of sufficient quality, several Population-Based Meta-Heuristic (PBMH) algorithms are implemented and tested on simulated optical measurements. These first results show that one of the tested algorithms, namely the Elitist Genetic Algorithm (EGA), consistently displays the desired behavior of finding good approximate solutions before reaching the optimum. The results further suggest that the algorithm possesses a polynomial time complexity, as the computation times are consistent with a polynomial model. With the advent of improved sensors and a heightened interest in the problem of space debris, it is expected that the number of tracked objects will grow by an order of magnitude in the near future. This research aims to provide a method that can treat the association and orbit determination problems simultaneously, and is able to efficiently process large data sets with minimal manual intervention.

  8. Object-Based Classification of Abandoned Logging Roads under Heavy Canopy Using LiDAR

    Jason Sherba


    Full Text Available LiDAR-derived slope models may be used to detect abandoned logging roads in steep forested terrain. An object-based classification approach of abandoned logging road detection was employed in this study. First, a slope model of the study site in Marin County, California was created from a LiDAR derived DEM. Multiresolution segmentation was applied to the slope model and road seed objects were iteratively grown into candidate objects. A road classification accuracy of 86% was achieved using this fully automated procedure and post processing increased this accuracy to 90%. In order to assess the sensitivity of the road classification to LiDAR ground point spacing, the LiDAR ground point cloud was repeatedly thinned by a fraction of 0.5 and the classification procedure was reapplied. The producer’s accuracy of the road classification declined from 79% with a ground point spacing of 0.91 to below 50% with a ground point spacing of 2, indicating the importance of high point density for accurate classification of abandoned logging roads.

  9. Surrogate-based Multi-Objective Optimization and Uncertainty Quantification Methods for Large, Complex Geophysical Models

    Gong, Wei; Duan, Qingyun


    Parameterization scheme has significant influence to the simulation ability of large, complex dynamic geophysical models, such as distributed hydrological models, land surface models, weather and climate models, etc. with the growing knowledge of physical processes, the dynamic geophysical models include more and more processes and producing more output variables. Consequently the parameter optimization / uncertainty quantification algorithms should also be multi-objective compatible. Although such algorithms have long been available, they usually require a large number of model runs and are therefore computationally expensive for large, complex dynamic models. In this research, we have developed surrogate-based multi-objective optimization method (MO-ASMO) and Markov Chain Monte Carlo method (MC-ASMO) for uncertainty quantification for these expensive dynamic models. The aim of MO-ASMO and MC-ASMO is to reduce the total number of model runs with appropriate adaptive sampling strategy assisted by surrogate modeling. Moreover, we also developed a method that can steer the search process with the help of prior parameterization scheme derived from the physical processes involved, so that all of the objectives can be improved simultaneously. The proposed algorithms have been evaluated with test problems and a land surface model - the Common Land Model (CoLM). The results demonstrated their effectiveness and efficiency.

  10. A Mixed Land Cover Spatio-temporal Data Model Based on Object-oriented and Snapshot

    LI Yinchao


    Full Text Available Spatio-temporal data model (STDM is one of the hot topics in the domains of spatio-temporal database and data analysis. There is a common view that a universal STDM is always of high complexity due to the various situation of spatio-temporal data. In this article, a mixed STDM is proposed based on object-oriented and snapshot models for modelling and analyzing landcover change (LCC. This model uses the object-oriented STDM to describe the spatio-temporal processes of land cover patches and organize their spatial and attributive properties. In the meantime, it uses the snapshot STDM to present the spatio-temporal distribution of LCC on the whole via snapshot images. The two types of models are spatially and temporally combined into a mixed version. In addition to presenting the spatio-temporal events themselves, this model could express the transformation events between different classes of spatio-temporal objects. It can be used to create database for historical data of LCC, do spatio-temporal statistics, simulation and data mining with the data. In this article, the LCC data in Heilongjiang province is used for case study to validate spatio-temporal data management and analysis abilities of mixed STDM, including creating database, spatio-temporal query, global evolution analysis and patches spatio-temporal process expression.

  11. Cooperative Moving Object Segmentation using Two Cameras based on Background Subtraction and Image Registration

    Zhigao Cui


    Full Text Available Moving camera, such as PTZ (pan-tilt-zoom camera, has been widely applied in visual surveillance system. However, it’s difficult to extract moving objects because of the dynamic background caused by the camera motion. In this paper, a novel framework for moving object segmentation exploiting two cameras collaboration is presented by combining background subtraction and image registration method. The proposed method uses one static camera to capture large-view images at low resolution, and one moving camera (i.e. PTZ camera to capture local-view images at high resolution. Different with methods using a single moving camera, the moving objects can be effectively segmented in the static camera image by background subtraction method. Then image registration method can be applied to extract moving region in the moving camera image. To deal with the resolution and intensity discrepancy between two synchronized images, we design a practical three-step image registration method, which has higher registration accuracy than traditional feature based method. Experimental results on outdoor scene demonstrate the effectiveness and robustness of proposed approach.

  12. A novel objective sour taste evaluation method based on near-infrared spectroscopy.

    Hoshi, Ayaka; Aoki, Soichiro; Kouno, Emi; Ogasawara, Masashi; Onaka, Takashi; Miura, Yutaka; Mamiya, Kanji


    One of the most important themes in the development of foods and drinks is the accurate evaluation of taste properties. In general, a sensory evaluation system is frequently used for evaluating food and drink. This method, which is dependent on human senses, is highly sensitive but is influenced by the eating experience and food palatability of individuals, leading to subjective results. Therefore, a more effective method for objectively estimating taste properties is required. Here we show that salivary hemodynamic signals, as measured by near-infrared spectroscopy, are a useful objective indicator for evaluating sour taste stimulus. In addition, the hemodynamic responses of the parotid gland are closely correlated to the salivary secretion volume of the parotid gland in response to basic taste stimuli and respond to stimuli independently of the hedonic aspect. Moreover, we examined the hemodynamic responses to complex taste stimuli in food-based solutions and demonstrated for the first time that the complicated phenomenon of the "masking effect," which decreases taste intensity despite the additional taste components, can be successfully detected by near-infrared spectroscopy. In summary, this study is the first to demonstrate near-infrared spectroscopy as a novel tool for objectively evaluating complex sour taste properties in foods and drinks.

  13. Wearable sensor-based objective assessment of motor symptoms in Parkinson's disease.

    Ossig, Christiana; Antonini, Angelo; Buhmann, Carsten; Classen, Joseph; Csoti, Ilona; Falkenburger, Björn; Schwarz, Michael; Winkler, Jürgen; Storch, Alexander


    Effective management and development of new treatment strategies of motor symptoms in Parkinson's disease (PD) largely depend on clinical rating instruments like the Unified PD rating scale (UPDRS) and the modified abnormal involuntary movement scale (mAIMS). Regarding inter-rater variability and continuous monitoring, clinical rating scales have various limitations. Patient-administered questionnaires such as the PD home diary to assess motor stages and fluctuations in late-stage PD are frequently used in clinical routine and as clinical trial endpoints, but diary/questionnaire are tiring, and recall bias impacts on data quality, particularly in patients with cognitive dysfunction or depression. Consequently, there is a strong need for continuous and objective monitoring of motor symptoms in PD for improving therapeutic regimen and for usage in clinical trials. Recent advances in battery technology, movement sensors such as gyroscopes, accelerometers and information technology boosted the field of objective measurement of movement in everyday life and medicine using wearable sensors allowing continuous (long-term) monitoring. This systematic review summarizes the current wearable sensor-based devices to objectively assess the various motor symptoms of PD.

  14. Moving object detection using a background modeling based on entropy theory and quad-tree decomposition

    Elharrouss, Omar; Moujahid, Driss; Elkah, Samah; Tairi, Hamid


    A particular algorithm for moving object detection using a background subtraction approach is proposed. We generate the background model by combining quad-tree decomposition with entropy theory. In general, many background subtraction approaches are sensitive to sudden illumination change in the scene and cannot update the background image in scenes. The proposed background modeling approach analyzes the illumination change problem. After performing the background subtraction based on the proposed background model, the moving targets can be accurately detected at each frame of the image sequence. In order to produce high accuracy for the motion detection, the binary motion mask can be computed by the proposed threshold function. The experimental analysis based on statistical measurements proves the efficiency of our proposed method in terms of quality and quantity. And it even outperforms substantially existing methods by perceptional evaluation.

  15. An object-oriented programming system for the integration of internet-based bioinformatics resources.

    Beveridge, Allan


    The Internet consists of a vast inhomogeneous reservoir of data. Developing software that can integrate a wide variety of different data sources is a major challenge that must be addressed for the realisation of the full potential of the Internet as a scientific research tool. This article presents a semi-automated object-oriented programming system for integrating web-based resources. We demonstrate that the current Internet standards (HTML, CGI [common gateway interface], Java, etc.) can be exploited to develop a data retrieval system that scans existing web interfaces and then uses a set of rules to generate new Java code that can automatically retrieve data from the Web. The validity of the software has been demonstrated by testing it on several biological databases. We also examine the current limitations of the Internet and discuss the need for the development of universal standards for web-based data.

  16. Research on Object Model-Based Architecture for Service Robot System

    邵鹏鸣; 李成刚; 吴翰声


    An object model-based software architecture for service robot system is presented, which addresses both software engineering issues such as reuse, extensibility, and management of complexity as well as system engineering issues like scalability, reactivity, and robustness. A novel approach to the service robot system architecture is discussed. Cognitive psychology is considered in designing the software system, i.e., a humans way of vision and planning is applied. The planner can incorporate the users request into its task selection mechanism and generate plans biased toward picking the most reliable task execution in a given situation, and the planner can alter task selection based on changes that occur in dynamic and uncertain environments.

  17. An adaptive, object oriented strategy for base calling in DNA sequence analysis.

    Giddings, M C; Brumley, R L; Haker, M; Smith, L M


    An algorithm has been developed for the determination of nucleotide sequence from data produced in fluorescence-based automated DNA sequencing instruments employing the four-color strategy. This algorithm takes advantage of object oriented programming techniques for modularity and extensibility. The algorithm is adaptive in that data sets from a wide variety of instruments and sequencing conditions can be used with good results. Confidence values are provided on the base calls as an estimate of accuracy. The algorithm iteratively employs confidence determinations from several different modules, each of which examines a different feature of the data for accurate peak identification. Modules within this system can be added or removed for increased performance or for application to a different task. In comparisons with commercial software, the algorithm performed well. Images PMID:8233787

  18. Objective evaluation of pronunciation of standard Chinese final based on formant pattern

    DONG Bin; ZHAO Qingwei; YAN Yonghong


    A method used for objective evaluation of pronunciation of finals in standard Chinese is presented. The formant pattern of final is selected as the main feature and an improved evaluation algorithm based on Support Vector Machine is proposed. In this algorithm, two-level classification strategy is employed. A full-classification model and a sub-classification model are trained for each final. The pronunciation quality is evaluated based on the classification results of this two-level strategy with scoring model of each final. The new evaluation method is compared with traditional methods such as Hidden Markov Model (HMM) posterior probability scoring method and feature of Mel-Frequency Cepstrum Coefficients (MFCC), and the results show that the performance is effectively improved by the proposed method. The correlation of scores between human testers and machine has achieved 82%.

  19. Real-time object tracking based on scale-invariant features employing bio-inspired hardware.

    Yasukawa, Shinsuke; Okuno, Hirotsugu; Ishii, Kazuo; Yagi, Tetsuya


    We developed a vision sensor system that performs a scale-invariant feature transform (SIFT) in real time. To apply the SIFT algorithm efficiently, we focus on a two-fold process performed by the visual system: whole-image parallel filtering and frequency-band parallel processing. The vision sensor system comprises an active pixel sensor, a metal-oxide semiconductor (MOS)-based resistive network, a field-programmable gate array (FPGA), and a digital computer. We employed the MOS-based resistive network for instantaneous spatial filtering and a configurable filter size. The FPGA is used to pipeline process the frequency-band signals. The proposed system was evaluated by tracking the feature points detected on an object in a video.

  20. Parts and Relations in Young Children's Shape-Based Object Recognition

    Augustine, Elaine; Smith, Linda B.; Jones, Susan S.


    The ability to recognize common objects from sparse information about geometric shape emerges during the same period in which children learn object names and object categories. Hummel and Biederman's (1992) theory of object recognition proposes that the geometric shapes of objects have two components--geometric volumes representing major object…