WorldWideScience

Sample records for video text detection

  1. Automatic inpainting scheme for video text detection and removal.

    Science.gov (United States)

    Mosleh, Ali; Bouguila, Nizar; Ben Hamza, Abdessamad

    2013-11-01

    We present a two stage framework for automatic video text removal to detect and remove embedded video texts and fill-in their remaining regions by appropriate data. In the video text detection stage, text locations in each frame are found via an unsupervised clustering performed on the connected components produced by the stroke width transform (SWT). Since SWT needs an accurate edge map, we develop a novel edge detector which benefits from the geometric features revealed by the bandlet transform. Next, the motion patterns of the text objects of each frame are analyzed to localize video texts. The detected video text regions are removed, then the video is restored by an inpainting scheme. The proposed video inpainting approach applies spatio-temporal geometric flows extracted by bandlets to reconstruct the missing data. A 3D volume regularization algorithm, which takes advantage of bandlet bases in exploiting the anisotropic regularities, is introduced to carry out the inpainting task. The method does not need extra processes to satisfy visual consistency. The experimental results demonstrate the effectiveness of both our proposed video text detection approach and the video completion technique, and consequently the entire automatic video text removal and restoration process.

  2. Sign Language Video Processing for Text Detection in Hindi Language

    Directory of Open Access Journals (Sweden)

    Rashmi B Hiremath

    2016-10-01

    Full Text Available Sign language is a way of expressing yourself with your body language, where every bit of ones expressions, goals, or sentiments are conveyed by physical practices, for example, outward appearances, body stance, motions, eye movements, touch and the utilization of space. Non-verbal communication exists in both creatures and people, yet this article concentrates on elucidations of human non-verbal or sign language interpretation into Hindi textual expression. The proposed method of implementation utilizes the image processing methods and synthetic intelligence strategies to get the goal of sign video recognition. To carry out the proposed task implementation it uses image processing methods such as frame analysing based tracking, edge detection, wavelet transform, erosion, dilation, blur elimination, noise elimination, on training videos. It also uses elliptical Fourier descriptors called SIFT for shape feature extraction and most important part analysis for feature set optimization and reduction. For result analysis, this paper uses different category videos such as sign of weeks, months, relations etc. Database of extracted outcomes are compared with the video fed to the system as a input of the signer by a trained unclear inference system.

  3. A Unified Framework for Tracking Based Text Detection and Recognition from Web Videos.

    Science.gov (United States)

    Tian, Shu; Yin, Xu-Cheng; Su, Ya; Hao, Hong-Wei

    2017-04-12

    Video text extraction plays an important role for multimedia understanding and retrieval. Most previous research efforts are conducted within individual frames. A few of recent methods, which pay attention to text tracking using multiple frames, however, do not effectively mine the relations among text detection, tracking and recognition. In this paper, we propose a generic Bayesian-based framework of Tracking based Text Detection And Recognition (T2DAR) from web videos for embedded captions, which is composed of three major components, i.e., text tracking, tracking based text detection, and tracking based text recognition. In this unified framework, text tracking is first conducted by tracking-by-detection. Tracking trajectories are then revised and refined with detection or recognition results. Text detection or recognition is finally improved with multi-frame integration. Moreover, a challenging video text (embedded caption text) database (USTB-VidTEXT) is constructed and publicly available. A variety of experiments on this dataset verify that our proposed approach largely improves the performance of text detection and recognition from web videos.

  4. AViTExt: Automatic Video Text Extraction, A new Approach for video content indexing Application

    OpenAIRE

    Bouaziz, Baseem; Zlitni, Tarek; Walid MAHDI

    2013-01-01

    In this paper, we propose a spatial temporal video-text detection technique which proceed in two principal steps:potential text region detection and a filtering process. In the first step we divide dynamically each pair of consecutive video frames into sub block in order to detect change. A significant difference between homologous blocks implies the appearance of an important object which may be a text region. The temporal redundancy is then used to filter these regions and forms an effectiv...

  5. Robust Adaptable Video Copy Detection

    DEFF Research Database (Denmark)

    Assent, Ira; Kremer, Hardy

    2009-01-01

    Video copy detection should be capable of identifying video copies subject to alterations e.g. in video contrast or frame rates. We propose a video copy detection scheme that allows for adaptable detection of videos that are altered temporally (e.g. frame rate change) and/or visually (e.g. change...... in contrast). Our query processing combines filtering and indexing structures for efficient multistep computation of video copies under this model. We show that our model successfully identifies altered video copies and does so more reliably than existing models....

  6. Video library for video imaging detection at intersection stop lines.

    Science.gov (United States)

    2010-04-01

    The objective of this activity was to record video that could be used for controlled : evaluation of video image vehicle detection system (VIVDS) products and software upgrades to : existing products based on a list of conditions that might be diffic...

  7. Advanced text and video analytics for proactive decision making

    Science.gov (United States)

    Bowman, Elizabeth K.; Turek, Matt; Tunison, Paul; Porter, Reed; Thomas, Steve; Gintautas, Vadas; Shargo, Peter; Lin, Jessica; Li, Qingzhe; Gao, Yifeng; Li, Xiaosheng; Mittu, Ranjeev; Rosé, Carolyn Penstein; Maki, Keith; Bogart, Chris; Choudhari, Samrihdi Shree

    2017-05-01

    Today's warfighters operate in a highly dynamic and uncertain world, and face many competing demands. Asymmetric warfare and the new focus on small, agile forces has altered the framework by which time critical information is digested and acted upon by decision makers. Finding and integrating decision-relevant information is increasingly difficult in data-dense environments. In this new information environment, agile data algorithms, machine learning software, and threat alert mechanisms must be developed to automatically create alerts and drive quick response. Yet these advanced technologies must be balanced with awareness of the underlying context to accurately interpret machine-processed indicators and warnings and recommendations. One promising approach to this challenge brings together information retrieval strategies from text, video, and imagery. In this paper, we describe a technology demonstration that represents two years of tri-service research seeking to meld text and video for enhanced content awareness. The demonstration used multisource data to find an intelligence solution to a problem using a common dataset. Three technology highlights from this effort include 1) Incorporation of external sources of context into imagery normalcy modeling and anomaly detection capabilities, 2) Automated discovery and monitoring of targeted users from social media text, regardless of language, and 3) The concurrent use of text and imagery to characterize behaviour using the concept of kinematic and text motifs to detect novel and anomalous patterns. Our demonstration provided a technology baseline for exploiting heterogeneous data sources to deliver timely and accurate synopses of data that contribute to a dynamic and comprehensive worldview.

  8. Moving Shadow Detection in Video Using Cepstrum

    Directory of Open Access Journals (Sweden)

    Fuat Cogun

    2013-01-01

    Full Text Available Moving shadows constitute problems in various applications such as image segmentation and object tracking. The main cause of these problems is the misclassification of the shadow pixels as target pixels. Therefore, the use of an accurate and reliable shadow detection method is essential to realize intelligent video processing applications. In this paper, a cepstrum-based method for moving shadow detection is presented. The proposed method is tested on outdoor and indoor video sequences using well-known benchmark test sets. To show the improvements over previous approaches, quantitative metrics are introduced and comparisons based on these metrics are made.

  9. Robust Shot Boundary Detection from Video Using Dynamic Texture

    Directory of Open Access Journals (Sweden)

    Peng Taile

    2014-03-01

    Full Text Available Video boundary detection belongs to a basis subject in computer vision. It is more important to video analysis and video understanding. The existing video boundary detection methods always are effective to certain types of video data. These methods have relatively low generalization ability. We present a novel shot boundary detection algorithm based on video dynamic texture. Firstly, the two adjacent frames are read from a given video. We normalize the two frames to get the same size frame. Secondly, we divide these frames into some sub-domain on the same standard. The following thing is to calculate the average gradient direction of sub-domain and form dynamic texture. Finally, the dynamic texture of adjacent frames is compared. We have done some experiments in different types of video data. These experimental results show that our method has high generalization ability. To different type of videos, our algorithm can achieve higher average precision and average recall relative to some algorithms.

  10. Defect detection on videos using neural network

    Directory of Open Access Journals (Sweden)

    Sizyakin Roman

    2017-01-01

    Full Text Available In this paper, we consider a method for defects detection in a video sequence, which consists of three main steps; frame compensation, preprocessing by a detector, which is base on the ranking of pixel values, and the classification of all pixels having anomalous values using convolutional neural networks. The effectiveness of the proposed method shown in comparison with the known techniques on several frames of the video sequence with damaged in natural conditions. The analysis of the obtained results indicates the high efficiency of the proposed method. The additional use of machine learning as postprocessing significantly reduce the likelihood of false alarm.

  11. Anomaly Detection with Text Mining

    Data.gov (United States)

    National Aeronautics and Space Administration — Many existing complex space systems have a significant amount of historical maintenance and problem data bases that are stored in unstructured text forms. The...

  12. ALOGORITHMS FOR AUTOMATIC RUNWAY DETECTION ON VIDEO SEQUENCES

    Directory of Open Access Journals (Sweden)

    A. I. Logvin

    2015-01-01

    Full Text Available The article discusses algorithm for automatic runway detection on video sequences. The main stages of algorithm are represented. Some methods to increase reliability of recognition are described.

  13. Indexing Motion Detection Data for Surveillance Video

    DEFF Research Database (Denmark)

    Vind, Søren Juhl; Bille, Philip; Gørtz, Inge Li

    2014-01-01

    We show how to compactly index video data to support fast motion detection queries. A query specifies a time interval T, a area A in the video and two thresholds v and p. The answer to a query is a list of timestamps in T where ≥ p% of A has changed by ≥ v values. Our results show that by building...... a small index, we can support queries with a speedup of two to three orders of magnitude compared to motion detection without an index. For high resolution video, the index size is about 20% of the compressed video size....

  14. Text-Attentional Convolutional Neural Network for Scene Text Detection.

    Science.gov (United States)

    He, Tong; Huang, Weilin; Qiao, Yu; Yao, Jian

    2016-06-01

    Recent deep learning models have demonstrated strong capabilities for classifying text and non-text components in natural images. They extract a high-level feature globally computed from a whole image component (patch), where the cluttered background information may dominate true text features in the deep representation. This leads to less discriminative power and poorer robustness. In this paper, we present a new system for scene text detection by proposing a novel text-attentional convolutional neural network (Text-CNN) that particularly focuses on extracting text-related regions and features from the image components. We develop a new learning mechanism to train the Text-CNN with multi-level and rich supervised information, including text region mask, character label, and binary text/non-text information. The rich supervision information enables the Text-CNN with a strong capability for discriminating ambiguous texts, and also increases its robustness against complicated background components. The training process is formulated as a multi-task learning problem, where low-level supervised information greatly facilitates the main task of text/non-text classification. In addition, a powerful low-level detector called contrast-enhancement maximally stable extremal regions (MSERs) is developed, which extends the widely used MSERs by enhancing intensity contrast between text patterns and background. This allows it to detect highly challenging text patterns, resulting in a higher recall. Our approach achieved promising results on the ICDAR 2013 data set, with an F-measure of 0.82, substantially improving the state-of-the-art results.

  15. Text-Attentional Convolutional Neural Networks for Scene Text Detection.

    Science.gov (United States)

    He, Tong; Huang, Weilin; Qiao, Yu; Yao, Jian

    2016-03-28

    Recent deep learning models have demonstrated strong capabilities for classifying text and non-text components in natural images. They extract a high-level feature computed globally from a whole image component (patch), where the cluttered background information may dominate true text features in the deep representation. This leads to less discriminative power and poorer robustness. In this work, we present a new system for scene text detection by proposing a novel Text-Attentional Convolutional Neural Network (Text-CNN) that particularly focuses on extracting text-related regions and features from the image components. We develop a new learning mechanism to train the Text-CNN with multi-level and rich supervised information, including text region mask, character label, and binary text/nontext information. The rich supervision information enables the Text-CNN with a strong capability for discriminating ambiguous texts, and also increases its robustness against complicated background components. The training process is formulated as a multi-task learning problem, where low-level supervised information greatly facilitates main task of text/non-text classification. In addition, a powerful low-level detector called Contrast- Enhancement Maximally Stable Extremal Regions (CE-MSERs) is developed, which extends the widely-used MSERs by enhancing intensity contrast between text patterns and background. This allows it to detect highly challenging text patterns, resulting in a higher recall. Our approach achieved promising results on the ICDAR 2013 dataset, with a F-measure of 0.82, improving the state-of-the-art results substantially.

  16. Video enhancement effectiveness for target detection

    Science.gov (United States)

    Simon, Michael; Fischer, Amber; Petrov, Plamen

    2011-05-01

    Unmanned aerial vehicles (UAVs) capture real-time video data of military targets while keeping the warfighter at a safe distance. This keeps soldiers out of harm's way while they perform intelligence, surveillance and reconnaissance (ISR) and close-air support troops in contact (CAS-TIC) situations. The military also wants to use UAV video to achieve force multiplication. One method of achieving effective force multiplication involves fielding numerous UAVs with cameras and having multiple videos processed simultaneously by a single operator. However, monitoring multiple video streams is difficult for operators when the videos are of low quality. To address this challenge, we researched several promising video enhancement algorithms that focus on improving video quality. In this paper, we discuss our video enhancement suite and provide examples of video enhancement capabilities, focusing on stabilization, dehazing, and denoising. We provide results that show the effects of our enhancement algorithms on target detection and tracking algorithms. These results indicate that there is potential to assist the operator in identifying and tracking relevant targets with aided target recognition even on difficult video, increasing the force multiplier effect of UAVs. This work also forms the basis for human factors research into the effects of enhancement algorithms on ISR missions.

  17. Video Shot Boundary Detection based on Multifractal Analisys

    Directory of Open Access Journals (Sweden)

    B. D. Reljin

    2011-11-01

    Full Text Available Extracting video shots is an essential preprocessing step to almost all video analysis, indexing, and other content-based operations. This process is equivalent to detecting the shot boundaries in a video. In this paper we presents video Shot Boundary Detection (SBD based on Multifractal Analysis (MA. Low-level features (color and texture features are extracted from each frame in video sequence. Features are concatenated in feature vectors (FVs and stored in feature matrix. Matrix rows correspond to FVs of frames from video sequence, while columns are time series of particular FV component. Multifractal analysis is applied to FV component time series, and shot boundaries are detected as high singularities of time series above pre defined treshold. Proposed SBD method is tested on real video sequence with 64 shots, with manually labeled shot boundaries. Detection accuracy depends on number FV components used. For only one FV component detection accuracy lies in the range 76-92% (depending on selected threshold, while by combining two FV components all shots are detected completely (accuracy of 100%.

  18. The efficiency and economy of two learning modes: text with illustration and video with narration.

    Science.gov (United States)

    Gordon, Stuart

    2015-01-01

    The aim of this study was to determine whether video or text was more effective at knowledge transfer and retention. In this study, knowledge transfer with video and text was similar, and text consumed fewer resources to create.

  19. Text2Video: text-driven facial animation using MPEG-4

    Science.gov (United States)

    Rurainsky, J.; Eisert, P.

    2005-07-01

    We present a complete system for the automatic creation of talking head video sequences from text messages. Our system converts the text into MPEG-4 Facial Animation Parameters and synthetic voice. A user selected 3D character will perform lip movements synchronized to the speech data. The 3D models created from a single image vary from realistic people to cartoon characters. A voice selection for different languages and gender as well as a pitch shift component enables a personalization of the animation. The animation can be shown on different displays and devices ranging from 3GPP players on mobile phones to real-time 3D render engines. Therefore, our system can be used in mobile communication for the conversion of regular SMS messages to MMS animations.

  20. Video change detection for fixed wing UAVs

    Science.gov (United States)

    Bartelsen, Jan; Müller, Thomas; Ring, Jochen; Mück, Klaus; Brüstle, Stefan; Erdnüß, Bastian; Lutz, Bastian; Herbst, Theresa

    2017-10-01

    In this paper we proceed the work of Bartelsen et al.1 We present the draft of a process chain for an image based change detection which is designed for videos acquired by fixed wing unmanned aerial vehicles (UAVs). From our point of view, automatic video change detection for aerial images can be useful to recognize functional activities which are typically caused by the deployment of improvised explosive devices (IEDs), e.g. excavations, skid marks, footprints, left-behind tooling equipment, and marker stones. Furthermore, in case of natural disasters, like flooding, imminent danger can be recognized quickly. Due to the necessary flight range, we concentrate on fixed wing UAVs. Automatic change detection can be reduced to a comparatively simple photogrammetric problem when the perspective change between the "before" and "after" image sets is kept as small as possible. Therefore, the aerial image acquisition demands a mission planning with a clear purpose including flight path and sensor configuration. While the latter can be enabled simply by a fixed and meaningful adjustment of the camera, ensuring a small perspective change for "before" and "after" videos acquired by fixed wing UAVs is a challenging problem. Concerning this matter, we have performed tests with an advanced commercial off the shelf (COTS) system which comprises a differential GPS and autopilot system estimating the repetition accuracy of its trajectory. Although several similar approaches have been presented,23 as far as we are able to judge, the limits for this important issue are not estimated so far. Furthermore, we design a process chain to enable the practical utilization of video change detection. It consists of a front-end of a database to handle large amounts of video data, an image processing and change detection implementation, and the visualization of the results. We apply our process chain on the real video data acquired by the advanced COTS fixed wing UAV and synthetic data. For the

  1. Multilingual Text Detection with Nonlinear Neural Network

    Directory of Open Access Journals (Sweden)

    Lin Li

    2015-01-01

    Full Text Available Multilingual text detection in natural scenes is still a challenging task in computer vision. In this paper, we apply an unsupervised learning algorithm to learn language-independent stroke feature and combine unsupervised stroke feature learning and automatically multilayer feature extraction to improve the representational power of text feature. We also develop a novel nonlinear network based on traditional Convolutional Neural Network that is able to detect multilingual text regions in the images. The proposed method is evaluated on standard benchmarks and multilingual dataset and demonstrates improvement over the previous work.

  2. Design and Implementation of Video Shot Detection on Field Programmable Gate Arrays

    Directory of Open Access Journals (Sweden)

    Jharna Majumdar

    2012-09-01

    Full Text Available Video has become an interactive medium of communication in everyday life. The sheer volume of video makes it extremely difficult to browse through and find the required data. Hence extraction of key frames from the video which represents the abstract of the entire video becomes necessary. The aim of the video shot detection is to find the position of the shot boundaries, so that key frames can be selected from each shot for subsequent processing such as video summarization, indexing etc. For most of the surveillance applications like video summery, face recognition etc., the hardware (real time implementation of these algorithms becomes necessary. Here in this paper we present the architecture for simultaneous accessing of consecutive frames, which are then used for the implementation of various Video Shot Detection algorithms. We also present the real time implementation of three video shot detection algorithms using the above mentioned architecture on FPGA (Field Programmable Gate Arrays.

  3. Evaluation of experimental UAV video change detection

    Science.gov (United States)

    Bartelsen, J.; Saur, G.; Teutsch, C.

    2016-10-01

    During the last ten years, the availability of images acquired from unmanned aerial vehicles (UAVs) has been continuously increasing due to the improvements and economic success of flight and sensor systems. From our point of view, reliable and automatic image-based change detection may contribute to overcoming several challenging problems in military reconnaissance, civil security, and disaster management. Changes within a scene can be caused by functional activities, i.e., footprints or skid marks, excavations, or humidity penetration; these might be recognizable in aerial images, but are almost overlooked when change detection is executed manually. With respect to the circumstances, these kinds of changes may be an indication of sabotage, terroristic activity, or threatening natural disasters. Although image-based change detection is possible from both ground and aerial perspectives, in this paper we primarily address the latter. We have applied an extended approach to change detection as described by Saur and Kr uger,1 and Saur et al.2 and have built upon the ideas of Saur and Bartelsen.3 The commercial simulation environment Virtual Battle Space 3 (VBS3) is used to simulate aerial "before" and "after" image acquisition concerning flight path, weather conditions and objects within the scene and to obtain synthetic videos. Video frames, which depict the same part of the scene, including "before" and "after" changes and not necessarily from the same perspective, are registered pixel-wise against each other by a photogrammetric concept, which is based on a homography. The pixel-wise registration is used to apply an automatic difference analysis, which, to a limited extent, is able to suppress typical errors caused by imprecise frame registration, sensor noise, vegetation and especially parallax effects. The primary concern of this paper is to seriously evaluate the possibilities and limitations of our current approach for image-based change detection with respect

  4. Target detection and tracking in infrared video

    Science.gov (United States)

    Deng, Zhihui; Zhu, Jihong

    2017-07-01

    In this paper, we propose a method for target detection and tracking in infrared video. The target is defined by its location and extent in a single frame. In the initialization process, we use an adaptive threshold to segment the target and then extract the fern feature and normalize it as a template. The detector uses the random forest and fern to detect the target in the infrared video. The random forest and fern is a random combination of 2bit Binary Pattern, which is robust to infrared targets with blurred and unknown contours. The tracker uses the gray-value weighted mean-Shift algorithm to track the infrared target which is always brighter than the background. And the tracker can track the deformed target efficiently and quickly. When the target disappears, the detector will redetect the target in the coming infrared image. Finally, we verify the algorithm on the real-time infrared target detection and tracking platform. The result shows that our algorithm performs better than TLD in terms of recall and runtime in infrared video.

  5. Polyp Detection and Segmentation from Video Capsule Endoscopy: A Review

    Directory of Open Access Journals (Sweden)

    V. B. Surya Prasath

    2016-12-01

    Full Text Available Video capsule endoscopy (VCE is used widely nowadays for visualizing the gastrointestinal (GI tract. Capsule endoscopy exams are prescribed usually as an additional monitoring mechanism and can help in identifying polyps, bleeding, etc. To analyze the large scale video data produced by VCE exams, automatic image processing, computer vision, and learning algorithms are required. Recently, automatic polyp detection algorithms have been proposed with various degrees of success. Though polyp detection in colonoscopy and other traditional endoscopy procedure based images is becoming a mature field, due to its unique imaging characteristics, detecting polyps automatically in VCE is a hard problem. We review different polyp detection approaches for VCE imagery and provide systematic analysis with challenges faced by standard image processing and computer vision methods.

  6. Automatic blood detection in capsule endoscopy video

    Science.gov (United States)

    Novozámský, Adam; Flusser, Jan; Tachecí, Ilja; Sulík, Lukáš; Bureš, Jan; Krejcar, Ondřej

    2016-12-01

    We propose two automatic methods for detecting bleeding in wireless capsule endoscopy videos of the small intestine. The first one uses solely the color information, whereas the second one incorporates the assumptions about the blood spot shape and size. The original idea is namely the definition of a new color space that provides good separability of blood pixels and intestinal wall. Both methods can be applied either individually or their results can be fused together for the final decision. We evaluate their individual performance and various fusion rules on real data, manually annotated by an endoscopist.

  7. Vehicle Plate Detection in Car Black Box Video

    Directory of Open Access Journals (Sweden)

    Dongjin Park

    2017-01-01

    Full Text Available Internet services that share vehicle black box videos need a way to obfuscate license plates in uploaded videos because of privacy issues. Thus, plate detection is one of the critical functions that such services rely on. Even though various types of detection methods are available, they are not suitable for black box videos because no assumption about size, number of plates, and lighting conditions can be made. We propose a method to detect Korean vehicle plates from black box videos. It works in two stages: the first stage aims to locate a set of candidate plate regions and the second stage identifies only actual plates from candidates by using a support vector machine classifier. The first stage consists of five sequential substeps. At first, it produces candidate regions by combining single character areas and then eliminates candidate regions that fail to meet plate conditions through the remaining substeps. For the second stage, we propose a feature vector that captures the characteristics of plates in texture and color. For performance evaluation, we compiled our dataset which contains 2,627 positive and negative images. The evaluation results show that the proposed method improves accuracy and sensitivity by at least 5% and is 30 times faster compared with an existing method.

  8. Highlight detection for video content analysis through double filters

    Science.gov (United States)

    Sun, Zhonghua; Chen, Hexin; Chen, Mianshu

    2005-07-01

    Highlight detection is a form of video summarization techniques aiming at including the most expressive or attracting parts in the video. Most video highlights selection research work has been performed on sports video, detecting certain objects or events such as goals in soccer video, touch down in football and others. In this paper, we present a highlight detection method for film video. Highlight section in a film video is not like that in sports video that usually has certain objects or events. The methods to determine a highlight part in a film video can exhibit as three aspects: (a) locating obvious audio event, (b) detecting expressive visual content around the obvious audio location, (c) selecting the preferred portion of the extracted audio-visual highlight segments. We define a double filters model to detect the potential highlights in video. First obvious audio location is determined through filtering the obvious audio features, and then we perform the potential visual salience detection around the potential audio highlight location. Finally the production from the audio-visual double filters is compared with a preference threshold to determine the final highlights. The user study results indicate that the double filters detection approach is an effective method for highlight detection for video content analysis.

  9. The Relative Efficacy of Video and Text Tutorials in Online Computing Education

    Science.gov (United States)

    Lang, Guido

    2016-01-01

    This study tests the effects of tutorial format (i.e. video vs. text) on student attitudes and performance in online computing education. A one-factor within-subjects experiment was conducted in an undergraduate Computer Information Systems course. Subjects were randomly assigned to complete two Excel exercises online: one with a video tutorial…

  10. Video as Text of Teaching: Toward More Deliberate Literacy Field Experience Supervision

    Science.gov (United States)

    Gelfuso, Andrea; Dennis, Danielle V.

    2017-01-01

    In this article, we theoretically explore how the deliberate use of video during literacy field experiences creates a text that can be read by triad members and can ameliorate the problem of relying on memory to engage in reflective conversations about literacy teaching and learning. The use of video, tools, and interactions with knowledgeable…

  11. Video behavior profiling for anomaly detection.

    Science.gov (United States)

    Xiang, Tao; Gong, Shaogang

    2008-05-01

    This paper aims to address the problem of modelling video behaviour captured in surveillancevideos for the applications of online normal behaviour recognition and anomaly detection. A novelframework is developed for automatic behaviour profiling and online anomaly sampling/detectionwithout any manual labelling of the training dataset. The framework consists of the followingkey components: (1) A compact and effective behaviour representation method is developed basedon discrete scene event detection. The similarity between behaviour patterns are measured basedon modelling each pattern using a Dynamic Bayesian Network (DBN). (2) Natural grouping ofbehaviour patterns is discovered through a novel spectral clustering algorithm with unsupervisedmodel selection and feature selection on the eigenvectors of a normalised affinity matrix. (3) Acomposite generative behaviour model is constructed which is capable of generalising from asmall training set to accommodate variations in unseen normal behaviour patterns. (4) A run-timeaccumulative anomaly measure is introduced to detect abnormal behaviour while normal behaviourpatterns are recognised when sufficient visual evidence has become available based on an onlineLikelihood Ratio Test (LRT) method. This ensures robust and reliable anomaly detection and normalbehaviour recognition at the shortest possible time. The effectiveness and robustness of our approachis demonstrated through experiments using noisy and sparse datasets collected from both indoorand outdoor surveillance scenarios. In particular, it is shown that a behaviour model trained usingan unlabelled dataset is superior to those trained using the same but labelled dataset in detectinganomaly from an unseen video. The experiments also suggest that our online LRT based behaviourrecognition approach is advantageous over the commonly used Maximum Likelihood (ML) methodin differentiating ambiguities among different behaviour classes observed online.

  12. Online scene change detection of multicast (MBone) video

    Science.gov (United States)

    Zhou, Wensheng; Shen, Ye; Vellaikal, Asha; Kuo, C.-C. Jay

    1998-10-01

    Many multimedia applications, such as multimedia data management systems and communication systems, require efficient representation of multimedia content. Thus semantic interpretation of video content has been a popular research area. Currently, most content-based video representation involves the segmentation of video based on key frames which are generated using scene change detection techniques as well as camera/object motion. Then, video features can be extracted from key frames. However most of such research performs off-line video processing in which the whole video scope is known as a priori which allows multiple scans of the stored video files during video processing. In comparison, relatively not much research has been done in the area of on-line video processing, which is crucial in video communication applications such as on-line collaboration, news broadcasts and so on. Our research investigates on-line real-time scene change detection of multicast video over the Internet. Our on-line processing system are designed to meet the requirements of real-time video multicasting over the Internet and to utilize the successful video parsing techniques available today. The proposed algorithms extract key frames from video bitstreams sent through the MBone network, and the extracted key frames are multicasted as annotations or metadata over a separate channel to assist in content filtering such as those anticipated to be in use by on-line filtering proxies in the Internet. The performance of the proposed algorithms are demonstrated and discussed in this paper.

  13. Video Salient Object Detection via Fully Convolutional Networks.

    Science.gov (United States)

    Wang, Wenguan; Shen, Jianbing; Shao, Ling

    This paper proposes a deep learning model to efficiently detect salient regions in videos. It addresses two important issues: 1) deep video saliency model training with the absence of sufficiently large and pixel-wise annotated video data and 2) fast video saliency training and detection. The proposed deep video saliency network consists of two modules, for capturing the spatial and temporal saliency information, respectively. The dynamic saliency model, explicitly incorporating saliency estimates from the static saliency model, directly produces spatiotemporal saliency inference without time-consuming optical flow computation. We further propose a novel data augmentation technique that simulates video training data from existing annotated image data sets, which enables our network to learn diverse saliency information and prevents overfitting with the limited number of training videos. Leveraging our synthetic video data (150K video sequences) and real videos, our deep video saliency model successfully learns both spatial and temporal saliency cues, thus producing accurate spatiotemporal saliency estimate. We advance the state-of-the-art on the densely annotated video segmentation data set (MAE of .06) and the Freiburg-Berkeley Motion Segmentation data set (MAE of .07), and do so with much improved speed (2 fps with all steps).This paper proposes a deep learning model to efficiently detect salient regions in videos. It addresses two important issues: 1) deep video saliency model training with the absence of sufficiently large and pixel-wise annotated video data and 2) fast video saliency training and detection. The proposed deep video saliency network consists of two modules, for capturing the spatial and temporal saliency information, respectively. The dynamic saliency model, explicitly incorporating saliency estimates from the static saliency model, directly produces spatiotemporal saliency inference without time-consuming optical flow computation. We further

  14. Encoding Concept Prototypes for Video Event Detection and Summarization

    NARCIS (Netherlands)

    Mazloom, M.; Habibian, A.; Liu, D.; Snoek, C.G.M.; Chang, S.F.

    2015-01-01

    This paper proposes a new semantic video representation for few and zero example event detection and unsupervised video event summarization. Different from existing works, which obtain a semantic representation by training concepts over images or entire video clips, we propose an algorithm that

  15. GRADUAL TRANSITION DETECTION FOR VIDEO PARTITIONING USING MORPHOLOGICAL OPERATORS

    Directory of Open Access Journals (Sweden)

    Valery Naranjo

    2011-05-01

    Full Text Available Temporal segmentation of video data for partitioning the sequence into shots is a prerequisite in many applications: automatic video indexing and editing, old flm restoration, perceptual coding, etc. The detection of abrupt transitions or cuts has been thoroughly studied in previous works. In this paper we present a scheme to identify the most common gradual transitions, i.e., dissolves and wipes, which relies on mathematical morphology operators. The approach is restricted to fast techniques which require low computation (without motion estimation and adapted to compressed sequences and are able to cope with random brightness variations (often occurring in old flms. The present study illustrates how the morphological operators can be used to analyze temporal series for detecting particular events, either working directly on the 1D signal or building an intermediate 2D image from the 1D signals to take advantage of the spatial operators.

  16. Performance evaluation software moving object detection and tracking in videos

    CERN Document Server

    Karasulu, Bahadir

    2013-01-01

    Performance Evaluation Software: Moving Object Detection and Tracking in Videos introduces a software approach for the real-time evaluation and performance comparison of the methods specializing in moving object detection and/or tracking (D&T) in video processing. Digital video content analysis is an important item for multimedia content-based indexing (MCBI), content-based video retrieval (CBVR) and visual surveillance systems. There are some frequently-used generic algorithms for video object D&T in the literature, such as Background Subtraction (BS), Continuously Adaptive Mean-shift (CMS),

  17. ViTexOCR; a script to extract text overlays from digital video

    Data.gov (United States)

    U.S. Geological Survey, Department of the Interior — The ViTexOCR script presents a new method for extracting navigation data from videos with text overlays using optical character recognition (OCR) software. Over the...

  18. Deteksi Perubahan Citra Pada Video Menggunakan Illumination Invariant Change Detection

    Directory of Open Access Journals (Sweden)

    Adri Priadana

    2017-01-01

    Full Text Available There is still a lot of juvenile delinquency in the middle of the community, especially people in urban areas, in the modern era. Juvenile delinquency may be fights, wild racing, gambling, and graffiti on the walls without permission. Vandalized wall is usually done on walls of office buildings and on public or private property. Results from vandalized walls can be seen from the image of the change between the initial image with the image after a motion. This study develops a image change detection system in video to detect the action of graffiti on the wall via a Closed-Circuit Television camera (CCTV which is done by simulation using the webcam camera. Motion detection process with Accumulative Differences Images (ADI method and image change detection process with Illumination Invariant Change Detection method coupled with image cropping method which carried out a comparison between the a reference image or image before any movement with the image after there is movement. Detection system testing one by different times variations, ie in the morning, noon, afternoon, and evening. The proposed method for image change detection in video give results with an accuracy rate of 92.86%.

  19. Automatic polyp detection in colonoscopy videos

    Science.gov (United States)

    Yuan, Zijie; IzadyYazdanabadi, Mohammadhassan; Mokkapati, Divya; Panvalkar, Rujuta; Shin, Jae Y.; Tajbakhsh, Nima; Gurudu, Suryakanth; Liang, Jianming

    2017-02-01

    Colon cancer is the second cancer killer in the US [1]. Colonoscopy is the primary method for screening and prevention of colon cancer, but during colonoscopy, a significant number (25% [2]) of polyps (precancerous abnormal growths inside of the colon) are missed; therefore, the goal of our research is to reduce the polyp miss-rate of colonoscopy. This paper presents a method to detect polyp automatically in a colonoscopy video. Our system has two stages: Candidate generation and candidate classification. In candidate generation (stage 1), we chose 3,463 frames (including 1,718 with-polyp frames) from real-time colonoscopy video database. We first applied processing procedures, namely intensity adjustment, edge detection and morphology operations, as pre-preparation. We extracted each connected component (edge contour) as one candidate patch from the pre-processed image. With the help of ground truth (GT) images, 2 constraints were implemented on each candidate patch, dividing and saving them into polyp group and non-polyp group. In candidate classification (stage 2), we trained and tested convolutional neural networks (CNNs) with AlexNet architecture [3] to classify each candidate into with-polyp or non-polyp class. Each with-polyp patch was processed by rotation, translation and scaling for invariant to get a much robust CNNs system. We applied leave-2-patients-out cross-validation on this model (4 of 6 cases were chosen as training set and the rest 2 were as testing set). The system accuracy and sensitivity are 91.47% and 91.76%, respectively.

  20. Video Pedestrian Detection Based on Orthogonal Scene Motion Pattern

    Directory of Open Access Journals (Sweden)

    Jianming Qu

    2014-01-01

    Full Text Available In fixed video scenes, scene motion patterns can be a very useful prior knowledge for pedestrian detection which is still a challenge at present. A new approach of cascade pedestrian detection using an orthogonal scene motion pattern model in a general density video is developed in this paper. To statistically model the pedestrian motion pattern, a probability grid overlaying the whole scene is set up to partition the scene into paths and holding areas. Features extracted from different pattern areas are classified by a group of specific strategies. Instead of using a unitary classifier, the employed classifier is composed of two directional subclassifiers trained, respectively, with different samples which are selected by two orthogonal directions. Considering that the negative images from the detection window scanning are much more than the positive ones, the cascade AdaBoost technique is adopted by the subclassifiers to reduce the negative image computations. The proposed approach is proved effectively by static classification experiments and surveillance video experiments.

  1. Object detection in surveillance video from dense trajectories

    OpenAIRE

    Zhai, Mengyao

    2015-01-01

    Detecting objects such as humans or vehicles is a central problem in surveillance video. Myriad standard approaches exist for this problem. At their core, approaches consider either the appearance of people, patterns of their motion, or differences from the background. In this paper we build on dense trajectories, a state-of-the-art approach for describing spatio-temporal patterns in video sequences. We demonstrate an application of dense trajectories to object detection in surveillance video...

  2. Logo detection and classification in a sport video: video indexing for sponsorship revenue control

    Science.gov (United States)

    Kovar, Bohumil; Hanjalic, Alan

    2001-12-01

    This paper presents a novel approach to detecting and classifying a trademark logo in frames of a sport video. In view of the fact that we attempt to detect and recognize a logo in a natural scene, the algorithm developed in this paper differs from traditional techniques for logo detection and classification that are applicable either to well-structured general text documents (e.g. invoices, memos, bank cheques) or to specialized trademark logo databases, where logos appear isolated on a clear background and where their detection and classification is not disturbed by the surrounding visual detail. Although the development of our algorithm is still in its starting phase, experimental results performed so far on a set of soccer TV broadcasts are very encouraging.

  3. Low-Complexity Saliency Detection Algorithm for Fast Perceptual Video Coding

    Directory of Open Access Journals (Sweden)

    Pengyu Liu

    2013-01-01

    Full Text Available A low-complexity saliency detection algorithm for perceptual video coding is proposed; low-level encoding information is adopted as the characteristics of visual perception analysis. Firstly, this algorithm employs motion vector (MV to extract temporal saliency region through fast MV noise filtering and translational MV checking procedure. Secondly, spatial saliency region is detected based on optimal prediction mode distributions in I-frame and P-frame. Then, it combines the spatiotemporal saliency detection results to define the video region of interest (VROI. The simulation results validate that the proposed algorithm can avoid a large amount of computation work in the visual perception characteristics analysis processing compared with other existing algorithms; it also has better performance in saliency detection for videos and can realize fast saliency detection. It can be used as a part of the video standard codec at medium-to-low bit-rates or combined with other algorithms in fast video coding.

  4. Video Salient Object Detection via Fully Convolutional Networks

    Science.gov (United States)

    Wang, Wenguan; Shen, Jianbing; Shao, Ling

    2018-01-01

    This paper proposes a deep learning model to efficiently detect salient regions in videos. It addresses two important issues: (1) deep video saliency model training with the absence of sufficiently large and pixel-wise annotated video data, and (2) fast video saliency training and detection. The proposed deep video saliency network consists of two modules, for capturing the spatial and temporal saliency information, respectively. The dynamic saliency model, explicitly incorporating saliency estimates from the static saliency model, directly produces spatiotemporal saliency inference without time-consuming optical flow computation. We further propose a novel data augmentation technique that simulates video training data from existing annotated image datasets, which enables our network to learn diverse saliency information and prevents overfitting with the limited number of training videos. Leveraging our synthetic video data (150K video sequences) and real videos, our deep video saliency model successfully learns both spatial and temporal saliency cues, thus producing accurate spatiotemporal saliency estimate. We advance the state-of-the-art on the DAVIS dataset (MAE of .06) and the FBMS dataset (MAE of .07), and do so with much improved speed (2fps with all steps).

  5. The Impact of Video-Based Materials on Chinese-Speaking Learners' English Text Comprehension

    Science.gov (United States)

    Lin, Lu-Fang

    2016-01-01

    This study investigated whether video-based materials can facilitate second language learners' text comprehension at the levels of macrostructure and microstructure. Three classes inclusive of 98 Chinese-speaking university students joined this study. The three classes were randomly assigned to three treatment groups: on-screen text (T Group),…

  6. Making sense with multimedia. A text theoretical study of a digital format integrating writing and video

    Directory of Open Access Journals (Sweden)

    Martin Engebretsen

    2006-03-01

    Full Text Available Digital text formats that allow a close interaction between writing and video represent new possibilities and challenges for the communication of educational content. What are the premises for functional and appropriate communication through web-based, multimedial text formats?This article explores the digital writing-video format from a structural, theoretical perspective. To begin with, the two media’s respective characteristics are discussed and compared as carriers of complex signs. Thereafter, the focus is upon how writing and video elements can be accommodated to web media. Finally, the article discusses the conditions for optimal co-ordination and interaction between the two media types within the framework of an integrated design. A design example is presented.

  7. Fast compressed domain motion detection in H.264 video streams for video surveillance applications

    DEFF Research Database (Denmark)

    Szczerba, Krzysztof; Forchhammer, Søren; Støttrup-Andersen, Jesper

    2009-01-01

    numbers of video streams on a single server. The focus of the work is on using the information in coded video streams to reduce the computational complexity and memory requirements, which translates into reduced hardware requirements and costs. The devised algorithm detects and segments activity based...

  8. FPGA-Based Real-Time Motion Detection for Automated Video Surveillance Systems

    Directory of Open Access Journals (Sweden)

    Sanjay Singh

    2016-03-01

    Full Text Available Design of automated video surveillance systems is one of the exigent missions in computer vision community because of their ability to automatically select frames of interest in incoming video streams based on motion detection. This research paper focuses on the real-time hardware implementation of a motion detection algorithm for such vision based automated surveillance systems. A dedicated VLSI architecture has been proposed and designed for clustering-based motion detection scheme. The working prototype of a complete standalone automated video surveillance system, including input camera interface, designed motion detection VLSI architecture, and output display interface, with real-time relevant motion detection capabilities, has been implemented on Xilinx ML510 (Virtex-5 FX130T FPGA platform. The prototyped system robustly detects the relevant motion in real-time in live PAL (720 × 576 resolution video streams directly coming from the camera.

  9. Automatic Emotional State Detection using Facial Expression Dynamic in Videos

    Directory of Open Access Journals (Sweden)

    Hongying Meng

    2014-11-01

    Full Text Available In this paper, an automatic emotion detection system is built for a computer or machine to detect the emotional state from facial expressions in human computer communication. Firstly, dynamic motion features are extracted from facial expression videos and then advanced machine learning methods for classification and regression are used to predict the emotional states. The system is evaluated on two publicly available datasets, i.e. GEMEP_FERA and AVEC2013, and satisfied performances are achieved in comparison with the baseline results provided. With this emotional state detection capability, a machine can read the facial expression of its user automatically. This technique can be integrated into applications such as smart robots, interactive games and smart surveillance systems.

  10. Design of an online video edge detection device for bottle caps based on FPGA

    Directory of Open Access Journals (Sweden)

    Donghui LIU

    2015-06-01

    Full Text Available An online video edge detection device for bottle caps is designed and implemented using OV7670 video module and FPGA based control unit. By Verilog language programming, the device realizes the menu type parametric setting of the external VGA display, and completes the Roberts edge detection of real-time video image, which improves the speed of image processing. By improving the detection algorithm, the noise is effectively suppressed, and clear and coherent edge images are derived. The design improves the working environment, and avoids the harm to human body.

  11. Semantic-driven Generation of Hyperlapse from 360[Formula: see text] Video.

    Science.gov (United States)

    Lai, Wei-Sheng; Huang, Yujia; Joshi, Neel; Buehler, Christopher; Yang, Ming-Hsuan; Kang, Sing Bing

    2017-09-11

    We present a system for converting a fully panoramic (360[Formula: see text]) video into a normal field-of-view (NFOV) hyperlapse for an optimal viewing experience. Our system exploits visual saliency and semantics to non-uniformly sample in space and time for generating hyperlapses. In addition, users can optionally choose objects of interest for customizing the hyperlapses. We first stabilize an input 360[Formula: see text] video by smoothing the rotation between adjacent frames and then compute regions of interest and saliency scores. An initial hyperlapse is generated by optimizing the saliency and motion smoothness followed by the saliency-aware frame selection. We further smooth the result using an efficient 2D video stabilization approach that adaptively selects the motion model to generate the final hyperlapse. We validate the design of our system by showing results for a variety of scenes and comparing against the state-of-the-art method through a large-scale user study.

  12. The Impact of Text versus Video Communication on Instructor Feedback in Blended Courses

    Science.gov (United States)

    Borup, Jered; West, Richard E.; Thomas, Rebecca

    2015-01-01

    In this study we examined student and instructor perceptions of text and video feedback in technology integration courses that combined face-to-face with online instruction for teacher candidates. Items from the Feedback Environment Scale (Steelman et al. 2004) were used to measure student perceptions of feedback quality and delivery. Independent…

  13. Using Text Mining to Uncover Students' Technology-Related Problems in Live Video Streaming

    Science.gov (United States)

    Abdous, M'hammed; He, Wu

    2011-01-01

    Because of their capacity to sift through large amounts of data, text mining and data mining are enabling higher education institutions to reveal valuable patterns in students' learning behaviours without having to resort to traditional survey methods. In an effort to uncover live video streaming (LVS) students' technology related-problems and to…

  14. Students' Learning Experiences from Didactic Teaching Sessions Including Patient Case Examples as Either Text or Video

    DEFF Research Database (Denmark)

    Pedersen, Kamilla; Moeller, Martin Holdgaard; Paltved, Charlotte

    2017-01-01

    OBJECTIVES: The aim of this study was to explore medical students' learning experiences from the didactic teaching formats using either text-based patient cases or video-based patient cases with similar content. The authors explored how the two different patient case formats influenced students....... Students taught with video-based patient cases, in contrast, often referred to the patient cases when highlighting new insights, including the importance of patient perspectives when communicating with patients. CONCLUSION: The format of patient cases included in teaching may have a substantial impact...

  15. A baseline algorithm for face detection and tracking in video

    Science.gov (United States)

    Manohar, Vasant; Soundararajan, Padmanabhan; Korzhova, Valentina; Boonstra, Matthew; Goldgof, Dmitry; Kasturi, Rangachar

    2007-10-01

    Establishing benchmark datasets, performance metrics and baseline algorithms have considerable research significance in gauging the progress in any application domain. These primarily allow both users and developers to compare the performance of various algorithms on a common platform. In our earlier works, we focused on developing performance metrics and establishing a substantial dataset with ground truth for object detection and tracking tasks (text and face) in two video domains -- broadcast news and meetings. In this paper, we present the results of a face detection and tracking algorithm on broadcast news videos with the objective of establishing a baseline performance for this task-domain pair. The detection algorithm uses a statistical approach that was originally developed by Viola and Jones and later extended by Lienhart. The algorithm uses a feature set that is Haar-like and a cascade of boosted decision tree classifiers as a statistical model. In this work, we used the Intel Open Source Computer Vision Library (OpenCV) implementation of the Haar face detection algorithm. The optimal values for the tunable parameters of this implementation were found through an experimental design strategy commonly used in statistical analyses of industrial processes. Tracking was accomplished as continuous detection with the detected objects in two frames mapped using a greedy algorithm based on the distances between the centroids of bounding boxes. Results on the evaluation set containing 50 sequences (~ 2.5 mins.) using the developed performance metrics show good performance of the algorithm reflecting the state-of-the-art which makes it an appropriate choice as the baseline algorithm for the problem.

  16. Key Frame Extraction for Text Based Video Retrieval Using Maximally Stable Extremal Regions

    Directory of Open Access Journals (Sweden)

    Werachard Wattanarachothai

    2015-04-01

    Full Text Available This paper presents a new approach for text-based video content retrieval system. The proposed scheme consists of three main processes that are key frame extraction, text localization and keyword matching. For the key-frame extraction, we proposed a Maximally Stable Extremal Region (MSER based feature which is oriented to segment shots of the video with different text contents. In text localization process, in order to form the text lines, the MSERs in each key frame are clustered based on their similarity in position, size, color, and stroke width. Then, Tesseract OCR engine is used for recognizing the text regions. In this work, to improve the recognition results, we input four images obtained from different pre-processing methods to Tesseract engine. Finally, the target keyword for querying is matched with OCR results based on an approximate string search scheme. The experiment shows that, by using the MSER feature, the videos can be segmented by using efficient number of shots and provide the better precision and recall in comparison with a sum of absolute difference and edge based method.

  17. Moving Shadow Detection in Video Using Cepstrum Regular Paper

    OpenAIRE

    Cogun, Fuat; Cetin, Ahmet Enis

    2013-01-01

    Moving shadows constitute problems in various applications such as image segmentation and object tracking. The main cause of these problems is the misclassification of the shadow pixels as target pixels. Therefore, the use of an accurate and reliable shadow detection method is essential to realize intelligent video processing applications. In this paper, a cepstrum‐based method for moving shadow detection is presented. The proposed method is tested on outdoor and indoor video sequences using ...

  18. Detection and Recognition of Abnormal Running Behavior in Surveillance Video

    Directory of Open Access Journals (Sweden)

    Ying-Ying Zhu

    2012-01-01

    Full Text Available Abnormal running behavior frequently happen in robbery cases and other criminal cases. In order to identity these abnormal behaviors a method to detect and recognize abnormal running behavior, is presented based on spatiotemporal parameters. Meanwhile, to obtain more accurate spatiotemporal parameters and improve the real-time performance of the algorithm, a multitarget tracking algorithm, based on the intersection area among the minimum enclosing rectangle of the moving objects, is presented. The algorithm can judge and exclude effectively the intersection of multitarget and the interference, which makes the tracking algorithm more accurate and of better robustness. Experimental results show that the combination of these two algorithms can detect and recognize effectively the abnormal running behavior in surveillance videos.

  19. Transition logo detection for sports videos highlight extraction

    Science.gov (United States)

    Su, Po-Chyi; Wang, Yu-Wei; Chen, Chien-Chang

    2006-10-01

    This paper presents a highlight extraction scheme for sports videos. The approach makes use of the transition logos inserted preceding and following the slow motion replays by the broadcaster, which demonstrate highlights of the game. First, the features of a MPEG compressed video are retrieved for subsequent processing. After the shot boundary detection procedure, the processing units are formed and the units with fast moving scenes are then selected. Finally, the detection of overlaying objects is performed to signal the appearance of a transition logo. Experimental results show the feasibility of this promising method for sports videos highlight extraction.

  20. An Indoor Video Surveillance System with Intelligent Fall Detection Capability

    Directory of Open Access Journals (Sweden)

    Ming-Chih Chen

    2013-01-01

    Full Text Available This work presents a novel indoor video surveillance system, capable of detecting the falls of humans. The proposed system can detect and evaluate human posture as well. To evaluate human movements, the background model is developed using the codebook method, and the possible position of moving objects is extracted using the background and shadow eliminations method. Extracting a foreground image produces more noise and damage in this image. Additionally, the noise is eliminated using morphological and size filters and this damaged image is repaired. When the image object of a human is extracted, whether or not the posture has changed is evaluated using the aspect ratio and height of a human body. Meanwhile, the proposed system detects a change of the posture and extracts the histogram of the object projection to represent the appearance. The histogram becomes the input vector of K-Nearest Neighbor (K-NN algorithm and is to evaluate the posture of the object. Capable of accurately detecting different postures of a human, the proposed system increases the fall detection accuracy. Importantly, the proposed method detects the posture using the frame ratio and the displacement of height in an image. Experimental results demonstrate that the proposed system can further improve the system performance and the fall down identification accuracy.

  1. A novel video dataset for change detection benchmarking.

    Science.gov (United States)

    Goyette, Nil; Jodoin, Pierre-Marc; Porikli, Fatih; Konrad, Janusz; Ishwar, Prakash

    2014-11-01

    Change detection is one of the most commonly encountered low-level tasks in computer vision and video processing. A plethora of algorithms have been developed to date, yet no widely accepted, realistic, large-scale video data set exists for benchmarking different methods. Presented here is a unique change detection video data set consisting of nearly 90 000 frames in 31 video sequences representing six categories selected to cover a wide range of challenges in two modalities (color and thermal infrared). A distinguishing characteristic of this benchmark video data set is that each frame is meticulously annotated by hand for ground-truth foreground, background, and shadow area boundaries-an effort that goes much beyond a simple binary label denoting the presence of change. This enables objective and precise quantitative comparison and ranking of video-based change detection algorithms. This paper discusses various aspects of the new data set, quantitative performance metrics used, and comparative results for over two dozen change detection algorithms. It draws important conclusions on solved and remaining issues in change detection, and describes future challenges for the scientific community. The data set, evaluation tools, and algorithm rankings are available to the public on a website and will be updated with feedback from academia and industry in the future.

  2. Shadow Detection Based on Regions of Light Sources for Object Extraction in Nighttime Video

    Directory of Open Access Journals (Sweden)

    Gil-beom Lee

    2017-03-01

    Full Text Available Intelligent video surveillance systems detect pre-configured surveillance events through background modeling, foreground and object extraction, object tracking, and event detection. Shadow regions inside video frames sometimes appear as foreground objects, interfere with ensuing processes, and finally degrade the event detection performance of the systems. Conventional studies have mostly used intensity, color, texture, and geometric information to perform shadow detection in daytime video, but these methods lack the capability of removing shadows in nighttime video. In this paper, a novel shadow detection algorithm for nighttime video is proposed; this algorithm partitions each foreground object based on the object’s vertical histogram and screens out shadow objects by validating their orientations heading toward regions of light sources. From the experimental results, it can be seen that the proposed algorithm shows more than 93.8% shadow removal and 89.9% object extraction rates for nighttime video sequences, and the algorithm outperforms conventional shadow removal algorithms designed for daytime videos.

  3. Real-time Multiple Abnormality Detection in Video Data

    DEFF Research Database (Denmark)

    Have, Simon Hartmann; Ren, Huamin; Moeslund, Thomas B.

    2013-01-01

    Automatic abnormality detection in video sequences has recently gained an increasing attention within the research community. Although progress has been seen, there are still some limitations in current research. While most systems are designed at detecting specific abnormality, others which...... are capable of detecting more than two types of abnormalities rely on heavy computation. Therefore, we provide a framework for detecting abnormalities in video surveillance by using multiple features and cascade classifiers, yet achieve above real-time processing speed. Experimental results on two datasets...

  4. A video surveillance system designed to detect multiple falls

    Directory of Open Access Journals (Sweden)

    Ming-Chih Chen

    2016-04-01

    Full Text Available This work presents a fall detection system that is based on image processing technology. The system can detect falling by various humans via analysis of video frame. First, the system utilizes the method of mixture and Gaussian background model to generate information about the background, and the noise and shadow of background are eliminated to extract the possible positions of moving objects. The extraction of a foreground image generates more noise and damage. Therefore, morphological and size filters are utilized to eliminate this noise and repair the damage to the image. Extraction of the foreground image yields the locations of human heads in the image. The median point, height, and aspect ratio of the people in the image are calculated. These characteristics are utilized to trace objects. The change of the characteristics of objects among various consecutive images can be used to evaluate those persons enter or leave the scene. The method of fall detection uses the height and aspect ratio of the human body, analyzes the image in which one person overlaps with another, and detects whether a human has fallen or not. Experimental results demonstrate that the proposed method can efficiently detect falls by multiple persons.

  5. A Study of Vehicle Detection and Counting System Based on Video

    Directory of Open Access Journals (Sweden)

    Shuang XU

    2014-10-01

    Full Text Available About the video image processing's vehicle detection and counting system research, which has video vehicle detection, vehicle targets' image processing, and vehicle counting function. Vehicle detection is the use of inter-frame difference method and vehicle shadow segmentation techniques for vehicle testing. Image processing functions is the use of color image gray processing, image segmentation, mathematical morphology analysis and image fills, etc. on target detection to be processed, and then the target vehicle extraction. Counting function is to count the detected vehicle. The system is the use of inter-frame video difference method to detect vehicle and the use of the method of adding frame to vehicle and boundary comparison method to complete the counting function, with high recognition rate, fast, and easy operation. The purpose of this paper is to enhance traffic management modernization and automation levels. According to this study, it can provide a reference for the future development of related applications.

  6. Automatic video surveillance of outdoor scenes using track before detect

    DEFF Research Database (Denmark)

    Hansen, Morten; Sørensen, Helge Bjarup Dissing; Birkemark, Christian M.

    2005-01-01

    This paper concerns automatic video surveillance of outdoor scenes using a single camera. The first step in automatic interpretation of the video stream is activity detection based on background subtraction. Usually, this process will generate a large number of false alarms in outdoor scenes due ...... if a detected object shows a pattern of movement consistent with predefined rules. The method is tested on a number of video sequences and a substantial reduction in the number of false alarms is demonstrated.......This paper concerns automatic video surveillance of outdoor scenes using a single camera. The first step in automatic interpretation of the video stream is activity detection based on background subtraction. Usually, this process will generate a large number of false alarms in outdoor scenes due...... to e.g. movement of thicket and changes in illumination. To reduce the number of false alarms a Track Before Detect (TBD) approach is suggested. In this TBD implementation all objects detected in the background subtraction process are followed over a number of frames. An alarm is given only...

  7. A novel visual saliency detection method for infrared video sequences

    Science.gov (United States)

    Wang, Xin; Zhang, Yuzhen; Ning, Chen

    2017-12-01

    Infrared video applications such as target detection and recognition, moving target tracking, and so forth can benefit a lot from visual saliency detection, which is essentially a method to automatically localize the ;important; content in videos. In this paper, a novel visual saliency detection method for infrared video sequences is proposed. Specifically, for infrared video saliency detection, both the spatial saliency and temporal saliency are considered. For spatial saliency, we adopt a mutual consistency-guided spatial cues combination-based method to capture the regions with obvious luminance contrast and contour features. For temporal saliency, a multi-frame symmetric difference approach is proposed to discriminate salient moving regions of interest from background motions. Then, the spatial saliency and temporal saliency are combined to compute the spatiotemporal saliency using an adaptive fusion strategy. Besides, to highlight the spatiotemporal salient regions uniformly, a multi-scale fusion approach is embedded into the spatiotemporal saliency model. Finally, a Gestalt theory-inspired optimization algorithm is designed to further improve the reliability of the final saliency map. Experimental results demonstrate that our method outperforms many state-of-the-art saliency detection approaches for infrared videos under various backgrounds.

  8. Euclidean Distance Based Classifier for Recognition and Generating Kannada Text Description from Live Sign Language Video

    Directory of Open Access Journals (Sweden)

    Ramesh Mahadev Kagalkar

    2017-10-01

    Full Text Available Sign language recognition has emerged in concert of the vital space of analysis in computer Vision. The problem long-faced by the researchers is that the instances of signs vary with each motion and look. Thus, during this paper a completely unique approach for recognizing varied alphabets of Kannada linguistic communication is projected wherever continuous video sequences of the signs are thought of. The system includes of three stages: Preprocessing stage, Feature Extraction and Classification. Preprocessing stage includes skin filtering, bar histogram matching. Eigen values and Eigen Vectors were thought of for feature extraction stage and at last Eigen value weighted Euclidean distance is employed to acknowledge the sign. It deals with vacant hands, so permitting the user to act with the system in natural manner. We have got thought of completely different alphabets within the video sequences and earned a hit rate of 95.25%.

  9. Segmentation Based Video Steganalysis to Detect Motion Vector Modification

    Directory of Open Access Journals (Sweden)

    Peipei Wang

    2017-01-01

    Full Text Available This paper presents a steganalytic approach against video steganography which modifies motion vector (MV in content adaptive manner. Current video steganalytic schemes extract features from fixed-length frames of the whole video and do not take advantage of the content diversity. Consequently, the effectiveness of the steganalytic feature is influenced by video content and the problem of cover source mismatch also affects the steganalytic performance. The goal of this paper is to propose a steganalytic method which can suppress the differences of statistical characteristics caused by video content. The given video is segmented to subsequences according to block’s motion in every frame. The steganalytic features extracted from each category of subsequences with close motion intensity are used to build one classifier. The final steganalytic result can be obtained by fusing the results of weighted classifiers. The experimental results have demonstrated that our method can effectively improve the performance of video steganalysis, especially for videos of low bitrate and low embedding ratio.

  10. A System based on Adaptive Background Subtraction Approach for Moving Object Detection and Tracking in Videos

    Directory of Open Access Journals (Sweden)

    Bahadır KARASULU

    2013-04-01

    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

  11. Heterogeneous CPU-GPU moving targets detection for UAV video

    Science.gov (United States)

    Li, Maowen; Tang, Linbo; Han, Yuqi; Yu, Chunlei; Zhang, Chao; Fu, Huiquan

    2017-07-01

    Moving targets detection is gaining popularity in civilian and military applications. On some monitoring platform of motion detection, some low-resolution stationary cameras are replaced by moving HD camera based on UAVs. The pixels of moving targets in the HD Video taken by UAV are always in a minority, and the background of the frame is usually moving because of the motion of UAVs. The high computational cost of the algorithm prevents running it at higher resolutions the pixels of frame. Hence, to solve the problem of moving targets detection based UAVs video, we propose a heterogeneous CPU-GPU moving target detection algorithm for UAV video. More specifically, we use background registration to eliminate the impact of the moving background and frame difference to detect small moving targets. In order to achieve the effect of real-time processing, we design the solution of heterogeneous CPU-GPU framework for our method. The experimental results show that our method can detect the main moving targets from the HD video taken by UAV, and the average process time is 52.16ms per frame which is fast enough to solve the problem.

  12. Forest Fire Smoke Video Detection Using Spatiotemporal and Dynamic Texture Features

    Directory of Open Access Journals (Sweden)

    Yaqin Zhao

    2015-01-01

    Full Text Available Smoke detection is a very key part of fire recognition in a forest fire surveillance video since the smoke produced by forest fires is visible much before the flames. The performance of smoke video detection algorithm is often influenced by some smoke-like objects such as heavy fog. This paper presents a novel forest fire smoke video detection based on spatiotemporal features and dynamic texture features. At first, Kalman filtering is used to segment candidate smoke regions. Then, candidate smoke region is divided into small blocks. Spatiotemporal energy feature of each block is extracted by computing the energy features of its 8-neighboring blocks in the current frame and its two adjacent frames. Flutter direction angle is computed by analyzing the centroid motion of the segmented regions in one candidate smoke video clip. Local Binary Motion Pattern (LBMP is used to define dynamic texture features of smoke videos. Finally, smoke video is recognized by Adaboost algorithm. The experimental results show that the proposed method can effectively detect smoke image recorded from different scenes.

  13. Using temporal IDF for efficient novelty detection in text streams

    OpenAIRE

    Karkali, Margarita; Rousseau, Francois; Ntoulas, Alexandros; Vazirgiannis, Michalis

    2014-01-01

    Novelty detection in text streams is a challenging task that emerges in quite a few different scenarios, ranging from email thread filtering to RSS news feed recommendation on a smartphone. An efficient novelty detection algorithm can save the user a great deal of time and resources when browsing through relevant yet usually previously-seen content. Most of the recent research on detection of novel documents in text streams has been building upon either geometric distances or distributional s...

  14. Video Anomaly Detection with Compact Feature Sets for Online Performance.

    Science.gov (United States)

    Leyva, Roberto; Sanchez, Victor; Li, Chang-Tsun

    2017-04-18

    Over the past decade, video anomaly detection has been explored with remarkable results. However, research on methodologies suitable for online performance is still very limited. In this paper, we present an online framework for video anomaly detection. The key aspect of our framework is a compact set of highly descriptive features, which is extracted from a novel cell structure that helps to define support regions in a coarse-to-fine fashion. Based on the scene's activity, only a limited number of support regions are processed, thus limiting the size of the feature set. Specifically, we use foreground occupancy and optical flow features. The framework uses an inference mechanism that evaluates the compact feature set via Gaussian Mixture Models, Markov Chains and Bag-of-Words in order to detect abnormal events. Our framework also considers the joint response of the models in the local spatio-temporal neighborhood to increase detection accuracy. We test our framework on popular existing datasets and on a new dataset comprising a wide variety of realistic videos captured by surveillance cameras. This particular dataset includes surveillance videos depicting criminal activities, car accidents and other dangerous situations. Evaluation results show that our framework outperforms other online methods and attains a very competitive detection performance compared to state-of-the-art non-online methods.

  15. Shot Boundary Detection in Soccer Video using Twin-comparison Algorithm and Dominant Color Region

    Directory of Open Access Journals (Sweden)

    Matko Šarić

    2008-06-01

    Full Text Available The first step in generic video processing is temporal segmentation, i.e. shot boundary detection. Camera shot transitions can be either abrupt (e.g. cuts or gradual (e.g. fades, dissolves, wipes. Sports video is one of the most challenging domains for robust shot boundary detection. We proposed a shot boundary detection algorithm for soccer video based on the twin-comparison method and the absolute difference between frames in their ratios of dominant colored pixels to total number of pixels. With this approach the detection of gradual transitions is improved by decreasing the number of false positives caused by some camera operations. We also compared performances of our algorithm and the standard twin-comparison method.

  16. Preserving color fidelity for display devices using scalable memory compression architecture for text, graphics, and video

    Science.gov (United States)

    Lebowsky, Fritz; Nicolas, Marina

    2014-01-01

    High-end monitors and TVs based on LCD technology continue to increase their native display resolution to 4k by 2k and beyond. Subsequently, uncompressed pixel amplitude processing becomes costly not only when transmitting over cable or wireless communication channels, but also when processing with array processor architectures. For motion video content, spatial preprocessing from YCbCr 444 to YCbCr 420 is widely accepted. However, due to spatial low pass filtering in horizontal and vertical direction, quality and readability of small text and graphics content is heavily compromised when color contrast is high in chrominance channels. On the other hand, straight forward YCbCr 444 compression based on mathematical error coding schemes quite often lacks optimal adaptation to visually significant image content. We present a block-based memory compression architecture for text, graphics, and video enabling multidimensional error minimization with context sensitive control of visually noticeable artifacts. As a result of analyzing image context locally, the number of operations per pixel can be significantly reduced, especially when implemented on array processor architectures. A comparative analysis based on some competitive solutions highlights the effectiveness of our approach, identifies its current limitations with regard to high quality color rendering, and illustrates remaining visual artifacts.

  17. An integrated framework for detecting suspicious behaviors in video surveillance

    Science.gov (United States)

    Zin, Thi Thi; Tin, Pyke; Hama, Hiromitsu; Toriu, Takashi

    2014-03-01

    In this paper, we propose an integrated framework for detecting suspicious behaviors in video surveillance systems which are established in public places such as railway stations, airports, shopping malls and etc. Especially, people loitering in suspicion, unattended objects left behind and exchanging suspicious objects between persons are common security concerns in airports and other transit scenarios. These involve understanding scene/event, analyzing human movements, recognizing controllable objects, and observing the effect of the human movement on those objects. In the proposed framework, multiple background modeling technique, high level motion feature extraction method and embedded Markov chain models are integrated for detecting suspicious behaviors in real time video surveillance systems. Specifically, the proposed framework employs probability based multiple backgrounds modeling technique to detect moving objects. Then the velocity and distance measures are computed as the high level motion features of the interests. By using an integration of the computed features and the first passage time probabilities of the embedded Markov chain, the suspicious behaviors in video surveillance are analyzed for detecting loitering persons, objects left behind and human interactions such as fighting. The proposed framework has been tested by using standard public datasets and our own video surveillance scenarios.

  18. Runway Detection From Map, Video and Aircraft Navigational Data

    Science.gov (United States)

    2016-03-01

    are corrected using image-processing techniques, such as the Hough transform for linear features. 14. SUBJECT TERMS runway, map, aircraft...video, detection, rotation matrix, Hough transform. 15. NUMBER OF PAGES 87 16. PRICE CODE 17. SECURITY CLASSIFICATION OF REPORT Unclassified 18...as the Hough transform for linear features. vi THIS PAGE INTENTIONALLY LEFT BLANK vii TABLE OF CONTENTS I. INTRODUCTION

  19. TNO at TRECVID 2008, Combining Audio and Video Fingerprinting for Robust Copy Detection

    NARCIS (Netherlands)

    Doets, P.J.; Eendebak, P.T.; Ranguelova, E.; Kraaij, W.

    2009-01-01

    TNO has evaluated a baseline audio and a video fingerprinting system based on robust hashing for the TRECVID 2008 copy detection task. We participated in the audio, the video and the combined audio-video copy detection task. The audio fingerprinting implementation clearly outperformed the video

  20. Small Moving Vehicle Detection in a Satellite Video of an Urban Area

    Directory of Open Access Journals (Sweden)

    Tao Yang

    2016-09-01

    Full Text Available Vehicle surveillance of a wide area allows us to learn much about the daily activities and traffic information. With the rapid development of remote sensing, satellite video has become an important data source for vehicle detection, which provides a broader field of surveillance. The achieved work generally focuses on aerial video with moderately-sized objects based on feature extraction. However, the moving vehicles in satellite video imagery range from just a few pixels to dozens of pixels and exhibit low contrast with respect to the background, which makes it hard to get available appearance or shape information. In this paper, we look into the problem of moving vehicle detection in satellite imagery. To the best of our knowledge, it is the first time to deal with moving vehicle detection from satellite videos. Our approach consists of two stages: first, through foreground motion segmentation and trajectory accumulation, the scene motion heat map is dynamically built. Following this, a novel saliency based background model which intensifies moving objects is presented to segment the vehicles in the hot regions. Qualitative and quantitative experiments on sequence from a recent Skybox satellite video dataset demonstrates that our approach achieves a high detection rate and low false alarm simultaneously.

  1. Violent Interaction Detection in Video Based on Deep Learning

    Science.gov (United States)

    Zhou, Peipei; Ding, Qinghai; Luo, Haibo; Hou, Xinglin

    2017-06-01

    Violent interaction detection is of vital importance in some video surveillance scenarios like railway stations, prisons or psychiatric centres. Existing vision-based methods are mainly based on hand-crafted features such as statistic features between motion regions, leading to a poor adaptability to another dataset. En lightened by the development of convolutional networks on common activity recognition, we construct a FightNet to represent the complicated visual violence interaction. In this paper, a new input modality, image acceleration field is proposed to better extract the motion attributes. Firstly, each video is framed as RGB images. Secondly, optical flow field is computed using the consecutive frames and acceleration field is obtained according to the optical flow field. Thirdly, the FightNet is trained with three kinds of input modalities, i.e., RGB images for spatial networks, optical flow images and acceleration images for temporal networks. By fusing results from different inputs, we conclude whether a video tells a violent event or not. To provide researchers a common ground for comparison, we have collected a violent interaction dataset (VID), containing 2314 videos with 1077 fight ones and 1237 no-fight ones. By comparison with other algorithms, experimental results demonstrate that the proposed model for violent interaction detection shows higher accuracy and better robustness.

  2. Deep Learning for Detection of Object-Based Forgery in Advanced Video

    Directory of Open Access Journals (Sweden)

    Ye Yao

    2017-12-01

    Full Text Available Passive video forensics has drawn much attention in recent years. However, research on detection of object-based forgery, especially for forged video encoded with advanced codec frameworks, is still a great challenge. In this paper, we propose a deep learning-based approach to detect object-based forgery in the advanced video. The presented deep learning approach utilizes a convolutional neural network (CNN to automatically extract high-dimension features from the input image patches. Different from the traditional CNN models used in computer vision domain, we let video frames go through three preprocessing layers before being fed into our CNN model. They include a frame absolute difference layer to cut down temporal redundancy between video frames, a max pooling layer to reduce computational complexity of image convolution, and a high-pass filter layer to enhance the residual signal left by video forgery. In addition, an asymmetric data augmentation strategy has been established to get a similar number of positive and negative image patches before the training. The experiments have demonstrated that the proposed CNN-based model with the preprocessing layers has achieved excellent results.

  3. Real-time logo detection and tracking in video

    Science.gov (United States)

    George, M.; Kehtarnavaz, N.; Rahman, M.; Carlsohn, M.

    2010-05-01

    This paper presents a real-time implementation of a logo detection and tracking algorithm in video. The motivation of this work stems from applications on smart phones that require the detection of logos in real-time. For example, one application involves detecting company logos so that customers can easily get special offers in real-time. This algorithm uses a hybrid approach by initially running the Scale Invariant Feature Transform (SIFT) algorithm on the first frame in order to obtain the logo location and then by using an online calibration of color within the SIFT detected area in order to detect and track the logo in subsequent frames in a time efficient manner. The results obtained indicate that this hybrid approach allows robust logo detection and tracking to be achieved in real-time.

  4. Using adversary text to detect adversary phase changes.

    Energy Technology Data Exchange (ETDEWEB)

    Speed, Ann Elizabeth; Doser, Adele Beatrice; Warrender, Christina E.

    2009-05-01

    The purpose of this work was to help develop a research roadmap and small proof ofconcept for addressing key problems and gaps from the perspective of using text analysis methods as a primary tool for detecting when a group is undergoing a phase change. Self- rganizing map (SOM) techniques were used to analyze text data obtained from the tworld-wide web. Statistical studies indicate that it may be possible to predict phase changes, as well as detect whether or not an example of writing can be attributed to a group of interest.

  5. Detecting plagiarism in program code and free text

    OpenAIRE

    Bandelj, Anej

    2014-01-01

    Lately we often hear allegations that a certain work is a plagiarism, hence I decided to describe this area in detail in my diploma thesis. First I define what precisely the term plagiarism means, in which areas it is present, how to limit it, and how to utilize software for its detection. I delve into the utility of software which detects details in source code and text documents. Such software does not determine plagiarism itself, but rather indicates the percentage of text or source code s...

  6. Fuzzy-Based Segmentation for Variable Font-Sized Text Extraction from Images/Videos

    Directory of Open Access Journals (Sweden)

    Samabia Tehsin

    2014-01-01

    Full Text Available Textual information embedded in multimedia can provide a vital tool for indexing and retrieval. A lot of work is done in the field of text localization and detection because of its very fundamental importance. One of the biggest challenges of text detection is to deal with variation in font sizes and image resolution. This problem gets elevated due to the undersegmentation or oversegmentation of the regions in an image. The paper addresses this problem by proposing a solution using novel fuzzy-based method. This paper advocates postprocessing segmentation method that can solve the problem of variation in text sizes and image resolution. The methodology is tested on ICDAR 2011 Robust Reading Challenge dataset which amply proves the strength of the recommended method.

  7. Texting

    Science.gov (United States)

    Tilley, Carol L.

    2009-01-01

    With the increasing ranks of cell phone ownership is an increase in text messaging, or texting. During 2008, more than 2.5 trillion text messages were sent worldwide--that's an average of more than 400 messages for every person on the planet. Although many of the messages teenagers text each day are perhaps nothing more than "how r u?" or "c u…

  8. Signal Detection Framework Using Semantic Text Mining Techniques

    Science.gov (United States)

    Sudarsan, Sithu D.

    2009-01-01

    Signal detection is a challenging task for regulatory and intelligence agencies. Subject matter experts in those agencies analyze documents, generally containing narrative text in a time bound manner for signals by identification, evaluation and confirmation, leading to follow-up action e.g., recalling a defective product or public advisory for…

  9. Towards a Video Passive Content Fingerprinting Method for Partial-Copy Detection Robust against Non-Simulated Attacks.

    Directory of Open Access Journals (Sweden)

    Zobeida Jezabel Guzman-Zavaleta

    Full Text Available Passive content fingerprinting is widely used for video content identification and monitoring. However, many challenges remain unsolved especially for partial-copies detection. The main challenge is to find the right balance between the computational cost of fingerprint extraction and fingerprint dimension, without compromising detection performance against various attacks (robustness. Fast video detection performance is desirable in several modern applications, for instance, in those where video detection involves the use of large video databases or in applications requiring real-time video detection of partial copies, a process whose difficulty increases when videos suffer severe transformations. In this context, conventional fingerprinting methods are not fully suitable to cope with the attacks and transformations mentioned before, either because the robustness of these methods is not enough or because their execution time is very high, where the time bottleneck is commonly found in the fingerprint extraction and matching operations. Motivated by these issues, in this work we propose a content fingerprinting method based on the extraction of a set of independent binary global and local fingerprints. Although these features are robust against common video transformations, their combination is more discriminant against severe video transformations such as signal processing attacks, geometric transformations and temporal and spatial desynchronization. Additionally, we use an efficient multilevel filtering system accelerating the processes of fingerprint extraction and matching. This multilevel filtering system helps to rapidly identify potential similar video copies upon which the fingerprint process is carried out only, thus saving computational time. We tested with datasets of real copied videos, and the results show how our method outperforms state-of-the-art methods regarding detection scores. Furthermore, the granularity of our method makes

  10. Detecting imperceptible movements in structures by means of video magnification

    Science.gov (United States)

    Ordóñez, Celestino; Cabo, Carlos; García-Cortés, Silverio; Menéndez, Agustín.

    2017-06-01

    The naked eye is not able to perceive very slow movements such as those occurring in certain structures under external forces. This might be the case of metallic or concrete bridges, tower cranes or steel beams. However, sometimes it is of interest to view such movements, since they can provide useful information regarding the mechanical state of those structures. In this work, we analyze the utility of video magnification to detect imperceptible movements in several types of structures. First, laboratory experiments were conducted to validate the method. Then, two different tests were carried out on real structures: one on a water slide and another on a tower crane. The results obtained allow us to conclude that image cross-correlation and video magnification is indeed a promising low-cost technique for structure health monitoring.

  11. Detection of upscale-crop and partial manipulation in surveillance video based on sensor pattern noise

    National Research Council Canada - National Science Library

    Hyun, Dai-Kyung; Ryu, Seung-Jin; Lee, Hae-Yeoun; Lee, Heung-Kyu

    2013-01-01

    .... Nevertheless, there is little research being done on forgery of surveillance videos. This paper proposes a forensic technique to detect forgeries of surveillance video based on sensor pattern noise (SPN...

  12. Advanced text authorship detection methods and their application to biblical texts

    Science.gov (United States)

    Putniņš, Tālis; Signoriello, Domenic J.; Jain, Samant; Berryman, Matthew J.; Abbott, Derek

    2005-12-01

    Authorship attribution has a range of applications in a growing number of fields such as forensic evidence, plagiarism detection, email filtering, and web information management. In this study, three attribution techniques are extended, tested on a corpus of English texts, and applied to a book in the New Testament of disputed authorship. The word recurrence interval based method compares standard deviations of the number of words between successive occurrences of a keyword both graphically and with chi-squared tests. The trigram Markov method compares the probabilities of the occurrence of words conditional on the preceding two words to determine the similarity between texts. The third method extracts stylometric measures such as the frequency of occurrence of function words and from these constructs text classification models using multiple discriminant analysis. The effectiveness of these techniques is compared. The accuracy of the results obtained by some of these extended methods is higher than many of the current state of the art approaches. Statistical evidence is presented about the authorship of the selected book from the New Testament.

  13. Do Instructional Videos on Sputum Submission Result in Increased Tuberculosis Case Detection? A Randomized Controlled Trial.

    Directory of Open Access Journals (Sweden)

    Grace Mhalu

    Full Text Available We examined the effect of an instructional video about the production of diagnostic sputum on case detection of tuberculosis (TB, and evaluated the acceptance of the video.Randomized controlled trial.We prepared a culturally adapted instructional video for sputum submission. We analyzed 200 presumptive TB cases coughing for more than two weeks who attended the outpatient department of the governmental Municipal Hospital in Mwananyamala (Dar es Salaam, Tanzania. They were randomly assigned to either receive instructions on sputum submission using the video before submission (intervention group, n = 100 or standard of care (control group, n = 100. Sputum samples were examined for volume, quality and presence of acid-fast bacilli by experienced laboratory technicians blinded to study groups.Median age was 39.1 years (interquartile range 37.0-50.0; 94 (47% were females, 106 (53% were males, and 49 (24.5% were HIV-infected. We found that the instructional video intervention was associated with detection of a higher proportion of microscopically confirmed cases (56%, 95% confidence interval [95% CI] 45.7-65.9%, sputum smear positive patients in the intervention group versus 23%, 95% CI 15.2-32.5%, in the control group, p <0.0001, an increase in volume of specimen defined as a volume ≥3ml (78%, 95% CI 68.6-85.7%, versus 45%, 95% CI 35.0-55.3%, p <0.0001, and specimens less likely to be salivary (14%, 95% CI 7.9-22.4%, versus 39%, 95% CI 29.4-49.3%, p = 0.0001. Older age, but not the HIV status or sex, modified the effectiveness of the intervention by improving it positively. When asked how well the video instructions were understood, the majority of patients in the intervention group reported to have understood the video instructions well (97%. Most of the patients thought the video would be useful in the cultural setting of Tanzania (92%.Sputum submission instructional videos increased the yield of tuberculosis cases through better quality of sputum

  14. Fall Detection for Elderly from Partially Observed Depth-Map Video Sequences Based on View-Invariant Human Activity Representation

    Directory of Open Access Journals (Sweden)

    Rami Alazrai

    2017-03-01

    Full Text Available This paper presents a new approach for fall detection from partially-observed depth-map video sequences. The proposed approach utilizes the 3D skeletal joint positions obtained from the Microsoft Kinect sensor to build a view-invariant descriptor for human activity representation, called the motion-pose geometric descriptor (MPGD. Furthermore, we have developed a histogram-based representation (HBR based on the MPGD to construct a length-independent representation of the observed video subsequences. Using the constructed HBR, we formulate the fall detection problem as a posterior-maximization problem in which the posteriori probability for each observed video subsequence is estimated using a multi-class SVM (support vector machine classifier. Then, we combine the computed posteriori probabilities from all of the observed subsequences to obtain an overall class posteriori probability of the entire partially-observed depth-map video sequence. To evaluate the performance of the proposed approach, we have utilized the Kinect sensor to record a dataset of depth-map video sequences that simulates four fall-related activities of elderly people, including: walking, sitting, falling form standing and falling from sitting. Then, using the collected dataset, we have developed three evaluation scenarios based on the number of unobserved video subsequences in the testing videos, including: fully-observed video sequence scenario, single unobserved video subsequence of random lengths scenarios and two unobserved video subsequences of random lengths scenarios. Experimental results show that the proposed approach achieved an average recognition accuracy of 93 . 6 % , 77 . 6 % and 65 . 1 % , in recognizing the activities during the first, second and third evaluation scenario, respectively. These results demonstrate the feasibility of the proposed approach to detect falls from partially-observed videos.

  15. MUTUAL COMPARATIVE FILTERING FOR CHANGE DETECTION IN VIDEOS WITH UNSTABLE ILLUMINATION CONDITIONS

    Directory of Open Access Journals (Sweden)

    S. V. Sidyakin

    2016-06-01

    Full Text Available In this paper we propose a new approach for change detection and moving objects detection in videos with unstable, abrupt illumination changes. This approach is based on mutual comparative filters and background normalization. We give the definitions of mutual comparative filters and outline their strong advantage for change detection purposes. Presented approach allows us to deal with changing illumination conditions in a simple and efficient way and does not have drawbacks, which exist in models that assume different color transformation laws. The proposed procedure can be used to improve a number of background modelling methods, which are not specifically designed to work under illumination changes.

  16. Shadow Detection Based on Regions of Light Sources for Object Extraction in Nighttime Video.

    Science.gov (United States)

    Lee, Gil-Beom; Lee, Myeong-Jin; Lee, Woo-Kyung; Park, Joo-Heon; Kim, Tae-Hwan

    2017-03-22

    Intelligent video surveillance systems detect pre-configured surveillance events through background modeling, foreground and object extraction, object tracking, and event detection. Shadow regions inside video frames sometimes appear as foreground objects, interfere with ensuing processes, and finally degrade the event detection performance of the systems. Conventional studies have mostly used intensity, color, texture, and geometric information to perform shadow detection in daytime video, but these methods lack the capability of removing shadows in nighttime video. In this paper, a novel shadow detection algorithm for nighttime video is proposed; this algorithm partitions each foreground object based on the object's vertical histogram and screens out shadow objects by validating their orientations heading toward regions of light sources. From the experimental results, it can be seen that the proposed algorithm shows more than 93.8% shadow removal and 89.9% object extraction rates for nighttime video sequences, and the algorithm outperforms conventional shadow removal algorithms designed for daytime videos.

  17. Physical models for moving shadow and object detection in video.

    Science.gov (United States)

    Nadimi, Sohail; Bhanu, Bir

    2004-08-01

    Current moving object detection systems typically detect shadows cast by the moving object as part of the moving object. In this paper, the problem of separating moving cast shadows from the moving objects in an outdoor environment is addressed. Unlike previous work, we present an approach that does not rely on any geometrical assumptions such as camera location and ground surface/object geometry. The approach is based on a new spatio-temporal albedo test and dichromatic reflection model and accounts for both the sun and the sky illuminations. Results are presented for several video sequences representing a variety of ground materials when the shadows are cast on different surface types. These results show that our approach is robust to widely different background and foreground materials, and illuminations.

  18. Effects of Explicit Instruction and Self-Directed Video Prompting on Text Comprehension of Students with Autism Spectrum Disorder

    Science.gov (United States)

    Sartini, Emily Claire

    2016-01-01

    The purpose of this study was to investigate the effects of explicit instruction combined with video prompting to teach text comprehension skills to students with autism spectrum disorder. Participants included 4 elementary school students with autism. A multiple probe across participants design was used to evaluate the intervention's…

  19. A Comparison of Video Modeling, Text-Based Instruction, and No Instruction for Creating Multiple Baseline Graphs in Microsoft Excel

    Science.gov (United States)

    Tyner, Bryan C.; Fienup, Daniel M.

    2015-01-01

    Graphing is socially significant for behavior analysts; however, graphing can be difficult to learn. Video modeling (VM) may be a useful instructional method but lacks evidence for effective teaching of computer skills. A between-groups design compared the effects of VM, text-based instruction, and no instruction on graphing performance.…

  20. Supporting Reporting: On the Positive Effects of Text- and Video-Based Awareness Material on Responsible Journalistic Suicide News Writing.

    Science.gov (United States)

    Scherr, Sebastian; Arendt, Florian; Schäfer, Markus

    2017-01-01

    Suicide is a global public health problem. Media impact on suicide is well confirmed and there are several recommendations on how media should and should not report on suicide to minimize the risk of copycat behavior. Those media guidelines have been developed to improve responsible reporting on suicide (RRS). Although such guidelines are used in several countries, we lack empirical evidence on their causal effect on actual journalistic news writing. We conducted an experiment with journalism students (N = 78) in Germany in which we tested whether exposure to awareness material promoting RRS influences news writing. As a supplement to the widely used text-based material, we tested the impact of a video in which a suicide expert presents the guidelines. A video was used as a supplement to text partly due to its potential benefit for prevention efforts over the Internet. We chose a low-budget production process allowing easy reproduction in different countries by local suicide experts. In the experiment, participants were either exposed to written, audio-visual, or no awareness material. Afterwards, participants read numerous facts of an ostensible suicide event and were asked to write a factual suicide news story based on these facts. Analyses indicate that awareness material exposure helped to improve RRS with the awareness video showing the strongest effects. We recommend that suicide prevention should use instructive awareness videos about RRS complementary to text-based awareness material.

  1. Design of an online video edge detection device for bottle caps based on FPGA

    OpenAIRE

    Donghui LIU; Lina TONG; Jiashuo WANG; Xiaoyun SUN; Xiaoying ZUO; Yakun DU; Zhenzhou WANG

    2015-01-01

    An online video edge detection device for bottle caps is designed and implemented using OV7670 video module and FPGA based control unit. By Verilog language programming, the device realizes the menu type parametric setting of the external VGA display, and completes the Roberts edge detection of real-time video image, which improves the speed of image processing. By improving the detection algorithm, the noise is effectively suppressed, and clear and coherent edge images are derived. The desig...

  2. Detection and objects tracking present in 2D digital video with Matlab

    Directory of Open Access Journals (Sweden)

    Melvin Ramírez Bogantes

    2013-09-01

    Full Text Available This paper presents the main results of research obtained in the design of an algorithm to detect and track an object in a video recording. The algorithm was designed in MatLab software and the videos used, which  presence of the mite Varroa Destructor in the cells of Africanized Honey Bees, were provided by the Centro de Investigación Apícola Tropical (CINAT-UNA.  The main result is the creation of a program capable of detecting and recording the movement of the mite, this is something innovative and useful for studies of the behavior of this species in the cells of honey bees performing the CINAT.

  3. Cross-domain active learning for video concept detection

    Science.gov (United States)

    Li, Huan; Li, Chao; Shi, Yuan; Xiong, Zhang; Hauptmann, Alexander G.

    2011-08-01

    As video data from a variety of different domains (e.g., news, documentaries, entertainment) have distinctive data distributions, cross-domain video concept detection becomes an important task, in which one can reuse the labeled data of one domain to benefit the learning task in another domain with insufficient labeled data. In this paper, we approach this problem by proposing a cross-domain active learning method which iteratively queries labels of the most informative samples in the target domain. Traditional active learning assumes that the training (source domain) and test data (target domain) are from the same distribution. However, it may fail when the two domains have different distributions because querying informative samples according to a base learner that initially learned from source domain may no longer be helpful for the target domain. In our paper, we use the Gaussian random field model as the base learner which has the advantage of exploring the distributions in both domains, and adopt uncertainty sampling as the query strategy. Additionally, we present an instance weighting trick to accelerate the adaptability of the base learner, and develop an efficient model updating method which can significantly speed up the active learning process. Experimental results on TRECVID collections highlight the effectiveness.

  4. Automatic detection of artifacts in converted S3D video

    Science.gov (United States)

    Bokov, Alexander; Vatolin, Dmitriy; Zachesov, Anton; Belous, Alexander; Erofeev, Mikhail

    2014-03-01

    In this paper we present algorithms for automatically detecting issues specific to converted S3D content. When a depth-image-based rendering approach produces a stereoscopic image, the quality of the result depends on both the depth maps and the warping algorithms. The most common problem with converted S3D video is edge-sharpness mismatch. This artifact may appear owing to depth-map blurriness at semitransparent edges: after warping, the object boundary becomes sharper in one view and blurrier in the other, yielding binocular rivalry. To detect this problem we estimate the disparity map, extract boundaries with noticeable differences, and analyze edge-sharpness correspondence between views. We pay additional attention to cases involving a complex background and large occlusions. Another problem is detection of scenes that lack depth volume: we present algorithms for detecting at scenes and scenes with at foreground objects. To identify these problems we analyze the features of the RGB image as well as uniform areas in the depth map. Testing of our algorithms involved examining 10 Blu-ray 3D releases with converted S3D content, including Clash of the Titans, The Avengers, and The Chronicles of Narnia: The Voyage of the Dawn Treader. The algorithms we present enable improved automatic quality assessment during the production stage.

  5. Detecting and extracting identifiable information from vehicles in videos

    Science.gov (United States)

    Roheda, Siddharth; Kalva, Hari; Naik, Mehul

    2015-03-01

    This paper presents a system to detect and extract identifiable information such as license plates, make, model, color, and bumper stickers present on vehicles. The goal of this work is to develop a system that automatically describes a vehicle just as a person would. This information can be used to improve traffic surveillance systems. The presented solution relies on efficient segmentation and structure of license plates to identify and extract information from vehicles. The system was evaluated on videos captures on Florida highways and is expected to work in other regions with little or no modifications. Results show that license plate was successfully segmented 92% of the cases, the make and the model of the car were segmented out and in 93% of the cases and bumper stickers were segmented in 92.5% of the cases. Over all recognition accuracy was 87%.

  6. Automatic Polyp Detection in Pillcam Colon 2 Capsule Images and Videos: Preliminary Feasibility Report

    Directory of Open Access Journals (Sweden)

    Pedro N. Figueiredo

    2011-01-01

    Full Text Available Background. The aim of this work is to present an automatic colorectal polyp detection scheme for capsule endoscopy. Methods. PillCam COLON2 capsule-based images and videos were used in our study. The database consists of full exam videos from five patients. The algorithm is based on the assumption that the polyps show up as a protrusion in the captured images and is expressed by means of a P-value, defined by geometrical features. Results. Seventeen PillCam COLON2 capsule videos are included, containing frames with polyps, flat lesions, diverticula, bubbles, and trash liquids. Polyps larger than 1 cm express a P-value higher than 2000, and 80% of the polyps show a P-value higher than 500. Diverticula, bubbles, trash liquids, and flat lesions were correctly interpreted by the algorithm as nonprotruding images. Conclusions. These preliminary results suggest that the proposed geometry-based polyp detection scheme works well, not only by allowing the detection of polyps but also by differentiating them from nonprotruding images found in the films.

  7. Detection and Localization of Anomalous Motion in Video Sequences from Local Histograms of Labeled Affine Flows

    Directory of Open Access Journals (Sweden)

    Juan-Manuel Pérez-Rúa

    2017-05-01

    Full Text Available We propose an original method for detecting and localizing anomalous motion patterns in videos from a camera view-based motion representation perspective. Anomalous motion should be taken in a broad sense, i.e., unexpected, abnormal, singular, irregular, or unusual motion. Identifying distinctive dynamic information at any time point and at any image location in a sequence of images is a key requirement in many situations and applications. The proposed method relies on so-called labeled affine flows (LAF involving both affine velocity vectors and affine motion classes. At every pixel, a motion class is inferred from the affine motion model selected in a set of candidate models estimated over a collection of windows. Then, the image is subdivided in blocks where motion class histograms weighted by the affine motion vector magnitudes are computed. They are compared blockwise to histograms of normal behaviors with a dedicated distance. More specifically, we introduce the local outlier factor (LOF to detect anomalous blocks. LOF is a local flexible measure of the relative density of data points in a feature space, here the space of LAF histograms. By thresholding the LOF value, we can detect an anomalous motion pattern in any block at any time instant of the video sequence. The threshold value is automatically set in each block by means of statistical arguments. We report comparative experiments on several real video datasets, demonstrating that our method is highly competitive for the intricate task of detecting different types of anomalous motion in videos. Specifically, we obtain very competitive results on all the tested datasets: 99.2% AUC for UMN, 82.8% AUC for UCSD, and 95.73% accuracy for PETS 2009, at the frame level.

  8. Review of passive-blind detection in digital video forgery based on sensing and imaging techniques

    Science.gov (United States)

    Tao, Junjie; Jia, Lili; You, Ying

    2016-01-01

    Advances in digital video compression and IP communication technologies raised new issues and challenges concerning the integrity and authenticity of surveillance videos. It is so important that the system should ensure that once recorded, the video cannot be altered; ensuring the audit trail is intact for evidential purposes. This paper gives an overview of passive techniques of Digital Video Forensics which are based on intrinsic fingerprints inherent in digital surveillance videos. In this paper, we performed a thorough research of literatures relevant to video manipulation detection methods which accomplish blind authentications without referring to any auxiliary information. We presents review of various existing methods in literature, and much more work is needed to be done in this field of video forensics based on video data analysis and observation of the surveillance systems.

  9. Impact of Interactive Video Communication Versus Text-Based Feedback on Teaching, Social, and Cognitive Presence in Online Learning Communities.

    Science.gov (United States)

    Seckman, Charlotte

    A key element to online learning is the ability to create a sense of presence to improve learning outcomes. This quasi-experimental study evaluated the impact of interactive video communication versus text-based feedback and found a significant difference between the 2 groups related to teaching, social, and cognitive presence. Recommendations to enhance presence should focus on providing timely feedback, interactive learning experiences, and opportunities for students to establish relationships with peers and faculty.

  10. Learning Latent Super-Events to Detect Multiple Activities in Videos

    OpenAIRE

    Piergiovanni, AJ; Ryoo, Michael S.

    2017-01-01

    In this paper, we introduce the concept of learning latent \\emph{super-events} from activity videos, and present how it benefits activity detection in continuous videos. We define a super-event as a set of multiple events occurring together in videos with a particular temporal organization; it is the opposite concept of sub-events. Real-world videos contain multiple activities and are rarely segmented (e.g., surveillance videos), and learning latent super-events allows the model to capture ho...

  11. Comparative study of motion detection methods for video surveillance systems

    Science.gov (United States)

    Sehairi, Kamal; Chouireb, Fatima; Meunier, Jean

    2017-03-01

    The objective of this study is to compare several change detection methods for a monostatic camera and identify the best method for different complex environments and backgrounds in indoor and outdoor scenes. To this end, we used the CDnet video dataset as a benchmark that consists of many challenging problems, ranging from basic simple scenes to complex scenes affected by bad weather and dynamic backgrounds. Twelve change detection methods, ranging from simple temporal differencing to more sophisticated methods, were tested and several performance metrics were used to precisely evaluate the results. Because most of the considered methods have not previously been evaluated on this recent large scale dataset, this work compares these methods to fill a lack in the literature, and thus this evaluation joins as complementary compared with the previous comparative evaluations. Our experimental results show that there is no perfect method for all challenging cases; each method performs well in certain cases and fails in others. However, this study enables the user to identify the most suitable method for his or her needs.

  12. A TBB-CUDA Implementation for Background Removal in a Video-Based Fire Detection System

    Directory of Open Access Journals (Sweden)

    Fan Wang

    2014-01-01

    Full Text Available This paper presents a parallel TBB-CUDA implementation for the acceleration of single-Gaussian distribution model, which is effective for background removal in the video-based fire detection system. In this framework, TBB mainly deals with initializing work of the estimated Gaussian model running on CPU, and CUDA performs background removal and adaption of the model running on GPU. This implementation can exploit the combined computation power of TBB-CUDA, which can be applied to the real-time environment. Over 220 video sequences are utilized in the experiments. The experimental results illustrate that TBB+CUDA can achieve a higher speedup than both TBB and CUDA. The proposed framework can effectively overcome the disadvantages of limited memory bandwidth and few execution units of CPU, and it reduces data transfer latency and memory latency between CPU and GPU.

  13. Automatic Detection of Cyberbullying in Social Media Text

    NARCIS (Netherlands)

    Van Hee, Cynthia; Jacobs, Gilles; Emmery, Chris; Desmet, Bart; Lefever, Els; Verhoeven, Ben; De Pauw, Guy; Daelemans, W.M.P.; Hoste, Veronique

    2018-01-01

    While social media offer great communication opportunities, they also increase the vulnerability of young people to threatening situations online. Recent studies report that cyberbullying constitutes a growing problem among youngsters. Successful prevention depends on the adequate detection of

  14. Students’ Learning Experiences from Didactic Teaching Sessions Including Patient Case Examples as Either Text or Video: A Qualitative Study

    DEFF Research Database (Denmark)

    Pedersen, Kamilla; Holdgaard, Martin Møller; Paltved, Charlotte

    2017-01-01

    on students' patient-centeredness. Video-based patient cases are probably more effective than text-based patient cases in fostering patient-centered perspectives in medical students. Teachers sharing stories from their own clinical experiences stimulates both engagement and excitement, but may also provoke......' perceptions of psychiatric patients and students' reflections on meeting and communicating with psychiatric patients. METHODS: The authors conducted group interviews with 30 medical students who volunteered to participate in interviews and applied inductive thematic content analysis to the transcribed....... Students taught with video-based patient cases, in contrast, often referred to the patient cases when highlighting new insights, including the importance of patient perspectives when communicating with patients. CONCLUSION: The format of patient cases included in teaching may have a substantial impact...

  15. A change detection approach to moving object detection in low frame-rate video

    Energy Technology Data Exchange (ETDEWEB)

    Porter, Reid B [Los Alamos National Laboratory; Harvey, Neal R [Los Alamos National Laboratory; Theiler, James P [Los Alamos National Laboratory

    2009-01-01

    Moving object detection is of significant interest in temporal image analysis since it is a first step in many object identification and tracking applications. A key component in almost all moving object detection algorithms is a pixel-level classifier, where each pixel is predicted to be either part of a moving object or part of the background. In this paper we investigate a change detection approach to the pixel-level classification problem and evaluate its impact on moving object detection. The change detection approach that we investigate was previously applied to multi-and hyper-spectral datasets, where images were typically taken several days, or months apart. In this paper, we apply the approach to low-frame rate (1-2 frames per second) video datasets.

  16. Simultaneous video stabilization and moving object detection in turbulence.

    Science.gov (United States)

    Oreifej, Omar; Li, Xin; Shah, Mubarak

    2013-02-01

    Turbulence mitigation refers to the stabilization of videos with nonuniform deformations due to the influence of optical turbulence. Typical approaches for turbulence mitigation follow averaging or dewarping techniques. Although these methods can reduce the turbulence, they distort the independently moving objects, which can often be of great interest. In this paper, we address the novel problem of simultaneous turbulence mitigation and moving object detection. We propose a novel three-term low-rank matrix decomposition approach in which we decompose the turbulence sequence into three components: the background, the turbulence, and the object. We simplify this extremely difficult problem into a minimization of nuclear norm, Frobenius norm, and l1 norm. Our method is based on two observations: First, the turbulence causes dense and Gaussian noise and therefore can be captured by Frobenius norm, while the moving objects are sparse and thus can be captured by l1 norm. Second, since the object's motion is linear and intrinsically different from the Gaussian-like turbulence, a Gaussian-based turbulence model can be employed to enforce an additional constraint on the search space of the minimization. We demonstrate the robustness of our approach on challenging sequences which are significantly distorted with atmospheric turbulence and include extremely tiny moving objects.

  17. Unsupervised Video Shot Detection Using Clustering Ensemble with a Color Global Scale-Invariant Feature Transform Descriptor

    Directory of Open Access Journals (Sweden)

    Hong Yi

    2008-01-01

    Full Text Available Abstract Scale-invariant feature transform (SIFT transforms a grayscale image into scale-invariant coordinates of local features that are invariant to image scale, rotation, and changing viewpoints. Because of its scale-invariant properties, SIFT has been successfully used for object recognition and content-based image retrieval. The biggest drawback of SIFT is that it uses only grayscale information and misses important visual information regarding color. In this paper, we present the development of a novel color feature extraction algorithm that addresses this problem, and we also propose a new clustering strategy using clustering ensembles for video shot detection. Based on Fibonacci lattice-quantization, we develop a novel color global scale-invariant feature transform (CGSIFT for better description of color contents in video frames for video shot detection. CGSIFT first quantizes a color image, representing it with a small number of color indices, and then uses SIFT to extract features from the quantized color index image. We also develop a new space description method using small image regions to represent global color features as the second step of CGSIFT. Clustering ensembles focusing on knowledge reuse are then applied to obtain better clustering results than using single clustering methods for video shot detection. Evaluation of the proposed feature extraction algorithm and the new clustering strategy using clustering ensembles reveals very promising results for video shot detection.

  18. Unsupervised Video Shot Detection Using Clustering Ensemble with a Color Global Scale-Invariant Feature Transform Descriptor

    Directory of Open Access Journals (Sweden)

    Yuchou Chang

    2008-02-01

    Full Text Available Scale-invariant feature transform (SIFT transforms a grayscale image into scale-invariant coordinates of local features that are invariant to image scale, rotation, and changing viewpoints. Because of its scale-invariant properties, SIFT has been successfully used for object recognition and content-based image retrieval. The biggest drawback of SIFT is that it uses only grayscale information and misses important visual information regarding color. In this paper, we present the development of a novel color feature extraction algorithm that addresses this problem, and we also propose a new clustering strategy using clustering ensembles for video shot detection. Based on Fibonacci lattice-quantization, we develop a novel color global scale-invariant feature transform (CGSIFT for better description of color contents in video frames for video shot detection. CGSIFT first quantizes a color image, representing it with a small number of color indices, and then uses SIFT to extract features from the quantized color index image. We also develop a new space description method using small image regions to represent global color features as the second step of CGSIFT. Clustering ensembles focusing on knowledge reuse are then applied to obtain better clustering results than using single clustering methods for video shot detection. Evaluation of the proposed feature extraction algorithm and the new clustering strategy using clustering ensembles reveals very promising results for video shot detection.

  19. Comparison Of Processing Time Of Different Size Of Images And Video Resolutions For Object Detection Using Fuzzy Inference System

    Directory of Open Access Journals (Sweden)

    Yogesh Yadav

    2017-01-01

    Full Text Available Object Detection with small computation cost and processing time is a necessity in diverse domains such as traffic analysis security cameras video surveillance etc .With current advances in technology and decrease in prices of image sensors and video cameras the resolution of captured images is more than 1MP and has higher frame rates. This implies a considerable data size that needs to be processed in a very short period of time when real-time operations and data processing is needed. Real time video processing with high performance can be achieved with GPU technology. The aim of this study is to evaluate the influence of different image and video resolutions on the processing time number of objects detections and accuracy of the detected object. MOG2 algorithm is used for processing video input data with GPU module. Fuzzy interference system is used to evaluate the accuracy of number of detected object and to show the difference between CPU and GPU computing methods.

  20. Video to Text (V2T) in Wide Area Motion Imagery

    Science.gov (United States)

    2015-09-01

    microtext) or a document (e.g., using Sphinx or Apache NLP ) as an automated approach [102]. Previous work in natural language full-text searching...language processing ( NLP ) based module. The heart of the structured text processing module includes the following seven key word banks...Features Tracker MHT Multiple Hypothesis Tracking MIL Multiple Instance Learning NLP Natural Language Processing OAB Online AdaBoost OF Optic Flow

  1. Small Moving Vehicle Detection in a Satellite Video of an Urban Area

    OpenAIRE

    Tao Yang; Xiwen Wang,; Bowei Yao; Jing Li; Yanning Zhang; Zhannan He; Wencheng Duan

    2016-01-01

    Vehicle surveillance of a wide area allows us to learn much about the daily activities and traffic information. With the rapid development of remote sensing, satellite video has become an important data source for vehicle detection, which provides a broader field of surveillance. The achieved work generally focuses on aerial video with moderately-sized objects based on feature extraction. However, the moving vehicles in satellite video imagery range from just a few pixels to dozens of pixels ...

  2. Tracking of Vehicle Movement on a Parking Lot Based on Video Detection

    Directory of Open Access Journals (Sweden)

    Ján HALGAŠ

    2014-06-01

    Full Text Available This article deals with topic of transport vehicles identification for dynamic and static transport based on video detection. It explains some of the technologies and approaches necessary for processing of specific image information (transport situation. The paper also describes a design of algorithm for vehicle detection on parking lot and consecutive record of trajectory into virtual environment. It shows a new approach to moving object detection (vehicles, people, and handlers on an enclosed area with emphasis on secure parking. The created application enables automatic identification of trajectory of specific objects moving within the parking area. The application was created in program language C++ with using an open source library OpenCV.

  3. Object Occlusion Detection Using Automatic Camera Calibration for a Wide-Area Video Surveillance System

    Directory of Open Access Journals (Sweden)

    Jaehoon Jung

    2016-06-01

    Full Text Available This paper presents an object occlusion detection algorithm using object depth information that is estimated by automatic camera calibration. The object occlusion problem is a major factor to degrade the performance of object tracking and recognition. To detect an object occlusion, the proposed algorithm consists of three steps: (i automatic camera calibration using both moving objects and a background structure; (ii object depth estimation; and (iii detection of occluded regions. The proposed algorithm estimates the depth of the object without extra sensors but with a generic red, green and blue (RGB camera. As a result, the proposed algorithm can be applied to improve the performance of object tracking and object recognition algorithms for video surveillance systems.

  4. Adversary phase change detection using SOMs and text data.

    Energy Technology Data Exchange (ETDEWEB)

    Speed, Ann Elizabeth; Doser, Adele Beatrice; Warrender, Christina E.

    2010-05-01

    In this work, we developed a self-organizing map (SOM) technique for using web-based text analysis to forecast when a group is undergoing a phase change. By 'phase change', we mean that an organization has fundamentally shifted attitudes or behaviors. For instance, when ice melts into water, the characteristics of the substance change. A formerly peaceful group may suddenly adopt violence, or a violent organization may unexpectedly agree to a ceasefire. SOM techniques were used to analyze text obtained from organization postings on the world-wide web. Results suggest it may be possible to forecast phase changes, and determine if an example of writing can be attributed to a group of interest.

  5. Acquisition Program Problem Detection Using Text Mining Methods

    Science.gov (United States)

    2012-03-01

    this method into their practices (Berry & Kogan, 2010). Latent Semantic Analysis (LSA), Probabilistic Latent Semantic Analysis (PLSA), Latent...also known as Latent Semantic Indexing, uses a series of three matrices (document eigenvector, eigenvalue, and term eigenvector) to approximate the...Estimate at Complete • EVM: Earned Value Management • HTML: Hyper Text Markup Language • LDA: Latent Dirichlet Allocation • LSA: Latent Semantic Analysis

  6. Improving text recognition by distinguishing scene and overlay text

    Science.gov (United States)

    Quehl, Bernhard; Yang, Haojin; Sack, Harald

    2015-02-01

    Video texts are closely related to the content of a video. They provide a valuable source for indexing and interpretation of video data. Text detection and recognition task in images or videos typically distinguished between overlay and scene text. Overlay text is artificially superimposed on the image at the time of editing and scene text is text captured by the recording system. Typically, OCR systems are specialized on one kind of text type. However, in video images both types of text can be found. In this paper, we propose a method to automatically distinguish between overlay and scene text to dynamically control and optimize post processing steps following text detection. Based on a feature combination a Support Vector Machine (SVM) is trained to classify scene and overlay text. We show how this distinction in overlay and scene text improves the word recognition rate. Accuracy of the proposed methods has been evaluated by using publicly available test data sets.

  7. Slow motion replay detection of tennis video based on color auto-correlogram

    Science.gov (United States)

    Zhang, Xiaoli; Zhi, Min

    2012-04-01

    In this paper, an effective slow motion replay detection method for tennis videos which contains logo transition is proposed. This method is based on the theory of color auto-correlogram and achieved by fowllowing steps: First,detect the candidate logo transition areas from the video frame sequence. Second, generate logo template. Then use color auto-correlogram for similarity matching between video frames and logo template in the candidate logo transition areas. Finally, select logo frames according to the matching results and locate the borders of slow motion accurately by using the brightness change during logo transition process. Experiment shows that, unlike previous approaches, this method has a great improvement in border locating accuracy rate, and can be used for other sports videos which have logo transition, too. In addition, as the algorithm only calculate the contents in the central area of the video frames, speed of the algorithm has been improved greatly.

  8. Fast Temporal Activity Proposals for Efficient Detection of Human Actions in Untrimmed Videos

    KAUST Repository

    Heilbron, Fabian Caba

    2016-12-13

    In many large-scale video analysis scenarios, one is interested in localizing and recognizing human activities that occur in short temporal intervals within long untrimmed videos. Current approaches for activity detection still struggle to handle large-scale video collections and the task remains relatively unexplored. This is in part due to the computational complexity of current action recognition approaches and the lack of a method that proposes fewer intervals in the video, where activity processing can be focused. In this paper, we introduce a proposal method that aims to recover temporal segments containing actions in untrimmed videos. Building on techniques for learning sparse dictionaries, we introduce a learning framework to represent and retrieve activity proposals. We demonstrate the capabilities of our method in not only producing high quality proposals but also in its efficiency. Finally, we show the positive impact our method has on recognition performance when it is used for action detection, while running at 10FPS.

  9. An Automatic Gastrointestinal Polyp Detection System in Video Endoscopy Using Fusion of Color Wavelet and Convolutional Neural Network Features

    Directory of Open Access Journals (Sweden)

    Mustain Billah

    2017-01-01

    Full Text Available Gastrointestinal polyps are considered to be the precursors of cancer development in most of the cases. Therefore, early detection and removal of polyps can reduce the possibility of cancer. Video endoscopy is the most used diagnostic modality for gastrointestinal polyps. But, because it is an operator dependent procedure, several human factors can lead to misdetection of polyps. Computer aided polyp detection can reduce polyp miss detection rate and assists doctors in finding the most important regions to pay attention to. In this paper, an automatic system has been proposed as a support to gastrointestinal polyp detection. This system captures the video streams from endoscopic video and, in the output, it shows the identified polyps. Color wavelet (CW features and convolutional neural network (CNN features of video frames are extracted and combined together which are used to train a linear support vector machine (SVM. Evaluations on standard public databases show that the proposed system outperforms the state-of-the-art methods, gaining accuracy of 98.65%, sensitivity of 98.79%, and specificity of 98.52%.

  10. Multiple Moving Object Detection for Fast Video Content Description in Compressed Domain

    Directory of Open Access Journals (Sweden)

    Boris Mansencal

    2007-11-01

    Full Text Available Indexing deals with the automatic extraction of information with the objective of automatically describing and organizing the content. Thinking of a video stream, different types of information can be considered semantically important. Since we can assume that the most relevant one is linked to the presence of moving foreground objects, their number, their shape, and their appearance can constitute a good mean for content description. For this reason, we propose to combine both motion information and region-based color segmentation to extract moving objects from an MPEG2 compressed video stream starting only considering low-resolution data. This approach, which we refer to as “rough indexing,” consists in processing P-frame motion information first, and then in performing I-frame color segmentation. Next, since many details can be lost due to the low-resolution data, to improve the object detection results, a novel spatiotemporal filtering has been developed which is constituted by a quadric surface modeling the object trace along time. This method enables to effectively correct possible former detection errors without heavily increasing the computational effort.

  11. Efficiently detecting outlying behavior in video-game players

    Directory of Open Access Journals (Sweden)

    Young Bin Kim

    2015-12-01

    Full Text Available In this paper, we propose a method for automatically detecting the times during which game players exhibit specific behavior, such as when players commonly show excitement, concentration, immersion, and surprise. The proposed method detects such outlying behavior based on the game players’ characteristics. These characteristics are captured non-invasively in a general game environment. In this paper, cameras were used to analyze observed data such as facial expressions and player movements. Moreover, multimodal data from the game players (i.e., data regarding adjustments to the volume and the use of the keyboard and mouse was used to analyze high-dimensional game-player data. A support vector machine was used to efficiently detect outlying behaviors. We verified the effectiveness of the proposed method using games from several genres. The recall rate of the outlying behavior pre-identified by industry experts was approximately 70%. The proposed method can also be used for feedback analysis of various interactive content provided in PC environments.

  12. Road user behaviour analyses based on video detections

    DEFF Research Database (Denmark)

    Agerholm, Niels; Tønning, Charlotte; Madsen, Tanja Kidholm Osmann

    2017-01-01

    has been developed. It works as a watchdog – if a passing road user affects defined part(s) of the video frame, RUBA records the time of the activity. It operates with three type of detectors (defined parts of the video frame): 1) if a road user passes the detector independent of the direction, 2......) if a road user passes the area in one pre-adjusted specific direction and 3) if a road user is standing still in the detector area. Also, RUBA can be adjusted so it registers massive entities (e.g. cars) while less massive ones (e.g. cyclists) are not registered. The software has been used for various...... analyses of traffic behaviour: traffic counts with and without removal of different modes of transportation, traffic conflicts, traffic behaviour for specific traffic flows and modes and comparisons of speeds in rebuilt road areas. While there is still space for improvement regarding data treatment speed...

  13. Video-based real-time on-street parking occupancy detection system

    Science.gov (United States)

    Bulan, Orhan; Loce, Robert P.; Wu, Wencheng; Wang, YaoRong; Bernal, Edgar A.; Fan, Zhigang

    2013-10-01

    Urban parking management is receiving significant attention due to its potential to reduce traffic congestion, fuel consumption, and emissions. Real-time parking occupancy detection is a critical component of on-street parking management systems, where occupancy information is relayed to drivers via smart phone apps, radio, Internet, on-road signs, or global positioning system auxiliary signals. Video-based parking occupancy detection systems can provide a cost-effective solution to the sensing task while providing additional functionality for traffic law enforcement and surveillance. We present a video-based on-street parking occupancy detection system that can operate in real time. Our system accounts for the inherent challenges that exist in on-street parking settings, including illumination changes, rain, shadows, occlusions, and camera motion. Our method utilizes several components from video processing and computer vision for motion detection, background subtraction, and vehicle detection. We also present three traffic law enforcement applications: parking angle violation detection, parking boundary violation detection, and exclusion zone violation detection, which can be integrated into the parking occupancy cameras as a value-added option. Our experimental results show that the proposed parking occupancy detection method performs in real-time at 5 frames/s and achieves better than 90% detection accuracy across several days of videos captured in a busy street block under various weather conditions such as sunny, cloudy, and rainy, among others.

  14. A Benchmark Dataset and Saliency-guided Stacked Autoencoders for Video-based Salient Object Detection.

    Science.gov (United States)

    Li, Jia; Xia, Changqun; Chen, Xiaowu

    2017-10-12

    Image-based salient object detection (SOD) has been extensively studied in past decades. However, video-based SOD is much less explored due to the lack of large-scale video datasets within which salient objects are unambiguously defined and annotated. Toward this end, this paper proposes a video-based SOD dataset that consists of 200 videos. In constructing the dataset, we manually annotate all objects and regions over 7,650 uniformly sampled keyframes and collect the eye-tracking data of 23 subjects who free-view all videos. From the user data, we find that salient objects in a video can be defined as objects that consistently pop-out throughout the video, and objects with such attributes can be unambiguously annotated by combining manually annotated object/region masks with eye-tracking data of multiple subjects. To the best of our knowledge, it is currently the largest dataset for videobased salient object detection. Based on this dataset, this paper proposes an unsupervised baseline approach for video-based SOD by using saliencyguided stacked autoencoders. In the proposed approach, multiple spatiotemporal saliency cues are first extracted at the pixel, superpixel and object levels. With these saliency cues, stacked autoencoders are constructed in an unsupervised manner that automatically infers a saliency score for each pixel by progressively encoding the high-dimensional saliency cues gathered from the pixel and its spatiotemporal neighbors. In experiments, the proposed unsupervised approach is compared with 31 state-of-the-art models on the proposed dataset and outperforms 30 of them, including 19 imagebased classic (unsupervised or non-deep learning) models, six image-based deep learning models, and five video-based unsupervised models. Moreover, benchmarking results show that the proposed dataset is very challenging and has the potential to boost the development of video-based SOD.

  15. Facial Video-Based Photoplethysmography to Detect HRV at Rest.

    Science.gov (United States)

    Moreno, J; Ramos-Castro, J; Movellan, J; Parrado, E; Rodas, G; Capdevila, L

    2015-06-01

    Our aim is to demonstrate the usefulness of photoplethysmography (PPG) for analyzing heart rate variability (HRV) using a standard 5-min test at rest with paced breathing, comparing the results with real RR intervals and testing supine and sitting positions. Simultaneous recordings of R-R intervals were conducted with a Polar system and a non-contact PPG, based on facial video recording on 20 individuals. Data analysis and editing were performed with individually designated software for each instrument. Agreement on HRV parameters was assessed with concordance correlations, effect size from ANOVA and Bland and Altman plots. For supine position, differences between video and Polar systems showed a small effect size in most HRV parameters. For sitting position, these differences showed a moderate effect size in most HRV parameters. A new procedure, based on the pixels that contained more heart beat information, is proposed for improving the signal-to-noise ratio in the PPG video signal. Results were acceptable in both positions but better in the supine position. Our approach could be relevant for applications that require monitoring of stress or cardio-respiratory health, such as effort/recuperation states in sports. © Georg Thieme Verlag KG Stuttgart · New York.

  16. Amplitude Integrated Electroencephalography Compared With Conventional Video EEG for Neonatal Seizure Detection: A Diagnostic Accuracy Study.

    Science.gov (United States)

    Rakshasbhuvankar, Abhijeet; Rao, Shripada; Palumbo, Linda; Ghosh, Soumya; Nagarajan, Lakshmi

    2017-08-01

    This diagnostic accuracy study compared the accuracy of seizure detection by amplitude-integrated electroencephalography with the criterion standard conventional video EEG in term and near-term infants at risk of seizures. Simultaneous recording of amplitude-integrated EEG (2-channel amplitude-integrated EEG with raw trace) and video EEG was done for 24 hours for each infant. Amplitude-integrated EEG was interpreted by a neonatologist; video EEG was interpreted by a neurologist independently. Thirty-five infants were included in the analysis. In the 7 infants with seizures on video EEG, there were 169 seizure episodes on video EEG, of which only 57 were identified by amplitude-integrated EEG. Amplitude-integrated EEG had a sensitivity of 33.7% for individual seizure detection. Amplitude-integrated EEG had an 86% sensitivity for detection of babies with seizures; however, it was nonspecific, in that 50% of infants with seizures detected by amplitude-integrated EEG did not have true seizures by video EEG. In conclusion, our study suggests that amplitude-integrated EEG is a poor screening tool for neonatal seizures.

  17. Holistic quaternion vector convolution filter for RGB-depth video contour detection

    Science.gov (United States)

    Ti, Chunli; Xu, Guodong; Guan, Yudong; Teng, Yidan; Zhang, Ye

    2017-05-01

    A quaternion vector gradient filter is proposed for RGB-depth (RGB-D) video contour detection. First, a holistic quaternion vector system is introduced to synthetically express the color and depth information, by adding the depth to its scalar part. Then, a convolution differential operator for quaternion vector is proposed to highlight edges with both depth and chromatic variations but restrain the gradient of intensity term. In addition, the quaternion vector gradients are adaptively weighted utilizing depth confidence measure and the quadtree decomposition of the coding tree units in the video streaming. Results on the 3-D high-efficiency video coding test sequences and quantitative simulated experiments on Berkeley segmentation datasets both indicate the availability of the proposed gradient-based method on detecting the semantic contour of the RGB-D videos.

  18. Medical Student and Tutor Perceptions of Video Versus Text in an Interactive Online Virtual Patient for Problem-Based Learning: A Pilot Study.

    Science.gov (United States)

    Woodham, Luke A; Ellaway, Rachel H; Round, Jonathan; Vaughan, Sophie; Poulton, Terry; Zary, Nabil

    2015-06-18

    The impact of the use of video resources in primarily paper-based problem-based learning (PBL) settings has been widely explored. Although it can provide many benefits, the use of video can also hamper the critical thinking of learners in contexts where learners are developing clinical reasoning. However, the use of video has not been explored in the context of interactive virtual patients for PBL. A pilot study was conducted to explore how undergraduate medical students interpreted and evaluated information from video- and text-based materials presented in the context of a branched interactive online virtual patient designed for PBL. The goal was to inform the development and use of virtual patients for PBL and to inform future research in this area. An existing virtual patient for PBL was adapted for use in video and provided as an intervention to students in the transition year of the undergraduate medicine course at St George's, University of London. Survey instruments were used to capture student and PBL tutor experiences and perceptions of the intervention, and a formative review meeting was run with PBL tutors. Descriptive statistics were generated for the structured responses and a thematic analysis was used to identify emergent themes in the unstructured responses. Analysis of student responses (n=119) and tutor comments (n=18) yielded 8 distinct themes relating to the perceived educational efficacy of information presented in video and text formats in a PBL context. Although some students found some characteristics of the videos beneficial, when asked to express a preference for video or text the majority of those that responded to the question (65%, 65/100) expressed a preference for text. Student responses indicated that the use of video slowed the pace of PBL and impeded students' ability to review and critically appraise the presented information. Our findings suggest that text was perceived to be a better source of information than video in virtual

  19. Medical Student and Tutor Perceptions of Video Versus Text in an Interactive Online Virtual Patient for Problem-Based Learning: A Pilot Study

    Science.gov (United States)

    Ellaway, Rachel H; Round, Jonathan; Vaughan, Sophie; Poulton, Terry; Zary, Nabil

    2015-01-01

    Background The impact of the use of video resources in primarily paper-based problem-based learning (PBL) settings has been widely explored. Although it can provide many benefits, the use of video can also hamper the critical thinking of learners in contexts where learners are developing clinical reasoning. However, the use of video has not been explored in the context of interactive virtual patients for PBL. Objective A pilot study was conducted to explore how undergraduate medical students interpreted and evaluated information from video- and text-based materials presented in the context of a branched interactive online virtual patient designed for PBL. The goal was to inform the development and use of virtual patients for PBL and to inform future research in this area. Methods An existing virtual patient for PBL was adapted for use in video and provided as an intervention to students in the transition year of the undergraduate medicine course at St George’s, University of London. Survey instruments were used to capture student and PBL tutor experiences and perceptions of the intervention, and a formative review meeting was run with PBL tutors. Descriptive statistics were generated for the structured responses and a thematic analysis was used to identify emergent themes in the unstructured responses. Results Analysis of student responses (n=119) and tutor comments (n=18) yielded 8 distinct themes relating to the perceived educational efficacy of information presented in video and text formats in a PBL context. Although some students found some characteristics of the videos beneficial, when asked to express a preference for video or text the majority of those that responded to the question (65%, 65/100) expressed a preference for text. Student responses indicated that the use of video slowed the pace of PBL and impeded students’ ability to review and critically appraise the presented information. Conclusions Our findings suggest that text was perceived to be a

  20. COMPOSITIONAL AND CONTENT-RELATED PARTICULARITIES OF POLITICAL MEDIA TEXTS (THROUGH THE EXAMPLE OF THE TEXTS OF POLITICAL VIDEO CLIPS ISSUED BY THE CANDIDATES FOR PRESIDENCY IN FRANCE IN 2017

    Directory of Open Access Journals (Sweden)

    Dmitrieva, A.V.

    2017-09-01

    Full Text Available The article examines the texts of political advertising video clips issued by the candidates for presidency in France during the campaign before the first round of elections in 2017. The mentioned examples of media texts are analysed from the compositional point of view as well as from that of the content particularities which are directly connected to the text structure. In general, the majority of the studied clips have a similar structure and consist of three parts: introduction, main part and conclusion. However, as a result of the research, a range of advantages marking well-structured videos was revealed. These include: addressing the voters and stating the speech topic clearly at the beginning of the clip, a relevant attention-grabbing opening phrase, consistency and clarity of the information presentation, appropriate use of additional video plots, conclusion at the end of the clip.

  1. Verbal methods of realisation of addresser-addressee relations in French political media texts (through the example of the texts of political videos issued by the candidates for the French 2017 presidential election

    Directory of Open Access Journals (Sweden)

    Dmitrieva Anastasia Valerievna

    2017-10-01

    Full Text Available The article deals with the addresser-addressee relations in the texts of French political advertising video clips from the verbal, textual point of view. The texts of video clips issued by the candidates for the French 2017 presidential election during the first round of the campaign serve as the material for this article. The aim of the article is to determine how the candidates (i.e. the addressers effectuate their relations with the voters (i.e. the addressees in the texts of their videos. As a result, a range of rhetorical methods were used by the candidates allowing them to attract maximum attention of the target audience. It makes the addressees trust the addresser and provide the desired perlocutionary effect.

  2. Affective video retrieval: violence detection in Hollywood movies by large-scale segmental feature extraction.

    Directory of Open Access Journals (Sweden)

    Florian Eyben

    Full Text Available Without doubt general video and sound, as found in large multimedia archives, carry emotional information. Thus, audio and video retrieval by certain emotional categories or dimensions could play a central role for tomorrow's intelligent systems, enabling search for movies with a particular mood, computer aided scene and sound design in order to elicit certain emotions in the audience, etc. Yet, the lion's share of research in affective computing is exclusively focusing on signals conveyed by humans, such as affective speech. Uniting the fields of multimedia retrieval and affective computing is believed to lend to a multiplicity of interesting retrieval applications, and at the same time to benefit affective computing research, by moving its methodology "out of the lab" to real-world, diverse data. In this contribution, we address the problem of finding "disturbing" scenes in movies, a scenario that is highly relevant for computer-aided parental guidance. We apply large-scale segmental feature extraction combined with audio-visual classification to the particular task of detecting violence. Our system performs fully data-driven analysis including automatic segmentation. We evaluate the system in terms of mean average precision (MAP on the official data set of the MediaEval 2012 evaluation campaign's Affect Task, which consists of 18 original Hollywood movies, achieving up to .398 MAP on unseen test data in full realism. An in-depth analysis of the worth of individual features with respect to the target class and the system errors is carried out and reveals the importance of peak-related audio feature extraction and low-level histogram-based video analysis.

  3. Joint Wavelet Video Denoising and Motion Activity Detection in Multimodal Human Activity Analysis: Application to Video-Assisted Bioacoustic/Psychophysiological Monitoring

    Science.gov (United States)

    Dimoulas, C. A.; Avdelidis, K. A.; Kalliris, G. M.; Papanikolaou, G. V.

    2007-12-01

    The current work focuses on the design and implementation of an indoor surveillance application for long-term automated analysis of human activity, in a video-assisted biomedical monitoring system. Video processing is necessary to overcome noise-related problems, caused by suboptimal video capturing conditions, due to poor lighting or even complete darkness during overnight recordings. Modified wavelet-domain spatiotemporal Wiener filtering and motion-detection algorithms are employed to facilitate video enhancement, motion-activity-based indexing and summarization. Structural aspects for validation of the motion detection results are also used. The proposed system has been already deployed in monitoring of long-term abdominal sounds, for surveillance automation, motion-artefacts detection and connection with other psychophysiological parameters. However, it can be used to any video-assisted biomedical monitoring or other surveillance application with similar demands.

  4. Effectiveness of a Video-Versus Text-Based Computer-Tailored Intervention for Obesity Prevention after One Year: A Randomized Controlled Trial

    Directory of Open Access Journals (Sweden)

    Kei Long Cheung

    2017-10-01

    Full Text Available Computer-tailored programs may help to prevent overweight and obesity, which are worldwide public health problems. This study investigated (1 the 12-month effectiveness of a video- and text-based computer-tailored intervention on energy intake, physical activity, and body mass index (BMI, and (2 the role of educational level in intervention effects. A randomized controlled trial in The Netherlands was conducted, in which adults were allocated to a video-based condition, text-based condition, or control condition, with baseline, 6 months, and 12 months follow-up. Outcome variables were self-reported BMI, physical activity, and energy intake. Mixed-effects modelling was used to investigate intervention effects and potential interaction effects. Compared to the control group, the video intervention group was effective regarding energy intake after 6 months (least squares means (LSM difference = −205.40, p = 0.00 and 12 months (LSM difference = −128.14, p = 0.03. Only video intervention resulted in lower average daily energy intake after one year (d = 0.12. Educational role and BMI did not seem to interact with this effect. No intervention effects on BMI and physical activity were found. The video computer-tailored intervention was effective on energy intake after one year. This effect was not dependent on educational levels or BMI categories, suggesting that video tailoring can be effective for a broad range of risk groups and may be preferred over text tailoring.

  5. Real-Time Human Detection for Aerial Captured Video Sequences via Deep Models

    Directory of Open Access Journals (Sweden)

    Nouar AlDahoul

    2018-01-01

    Full Text Available Human detection in videos plays an important role in various real life applications. Most of traditional approaches depend on utilizing handcrafted features which are problem-dependent and optimal for specific tasks. Moreover, they are highly susceptible to dynamical events such as illumination changes, camera jitter, and variations in object sizes. On the other hand, the proposed feature learning approaches are cheaper and easier because highly abstract and discriminative features can be produced automatically without the need of expert knowledge. In this paper, we utilize automatic feature learning methods which combine optical flow and three different deep models (i.e., supervised convolutional neural network (S-CNN, pretrained CNN feature extractor, and hierarchical extreme learning machine for human detection in videos captured using a nonstatic camera on an aerial platform with varying altitudes. The models are trained and tested on the publicly available and highly challenging UCF-ARG aerial dataset. The comparison between these models in terms of training, testing accuracy, and learning speed is analyzed. The performance evaluation considers five human actions (digging, waving, throwing, walking, and running. Experimental results demonstrated that the proposed methods are successful for human detection task. Pretrained CNN produces an average accuracy of 98.09%. S-CNN produces an average accuracy of 95.6% with soft-max and 91.7% with Support Vector Machines (SVM. H-ELM has an average accuracy of 95.9%. Using a normal Central Processing Unit (CPU, H-ELM’s training time takes 445 seconds. Learning in S-CNN takes 770 seconds with a high performance Graphical Processing Unit (GPU.

  6. Do Instructional Videos on Sputum Submission Result in Increased Tuberculosis Case Detection? A Randomized Controlled Trial.

    Science.gov (United States)

    Mhalu, Grace; Hella, Jerry; Doulla, Basra; Mhimbira, Francis; Mtutu, Hawa; Hiza, Helen; Sasamalo, Mohamed; Rutaihwa, Liliana; Rieder, Hans L; Seimon, Tamsyn; Mutayoba, Beatrice; Weiss, Mitchell G; Fenner, Lukas

    2015-01-01

    We examined the effect of an instructional video about the production of diagnostic sputum on case detection of tuberculosis (TB), and evaluated the acceptance of the video. Randomized controlled trial. We prepared a culturally adapted instructional video for sputum submission. We analyzed 200 presumptive TB cases coughing for more than two weeks who attended the outpatient department of the governmental Municipal Hospital in Mwananyamala (Dar es Salaam, Tanzania). They were randomly assigned to either receive instructions on sputum submission using the video before submission (intervention group, n = 100) or standard of care (control group, n = 100). Sputum samples were examined for volume, quality and presence of acid-fast bacilli by experienced laboratory technicians blinded to study groups. Median age was 39.1 years (interquartile range 37.0-50.0); 94 (47%) were females, 106 (53%) were males, and 49 (24.5%) were HIV-infected. We found that the instructional video intervention was associated with detection of a higher proportion of microscopically confirmed cases (56%, 95% confidence interval [95% CI] 45.7-65.9%, sputum smear positive patients in the intervention group versus 23%, 95% CI 15.2-32.5%, in the control group, p instructions were understood, the majority of patients in the intervention group reported to have understood the video instructions well (97%). Most of the patients thought the video would be useful in the cultural setting of Tanzania (92%). Sputum submission instructional videos increased the yield of tuberculosis cases through better quality of sputum samples. If confirmed in larger studies, instructional videos may have a substantial effect on the case yield using sputum microscopy and also molecular tests. This low-cost strategy should be considered as part of the efforts to control TB in resource-limited settings. Pan African Clinical Trials Registry PACTR201504001098231.

  7. Detection of Double-Compressed H.264/AVC Video Incorporating the Features of the String of Data Bits and Skip Macroblocks

    Directory of Open Access Journals (Sweden)

    Heng Yao

    2017-12-01

    Full Text Available Today’s H.264/AVC coded videos have a high quality, high data-compression ratio. They also have a strong fault tolerance, better network adaptability, and have been widely applied on the Internet. With the popularity of powerful and easy-to-use video editing software, digital videos can be tampered with in various ways. Therefore, the double compression in the H.264/AVC video can be used as a first step in the study of video-tampering forensics. This paper proposes a simple, but effective, double-compression detection method that analyzes the periodic features of the string of data bits (SODBs and the skip macroblocks (S-MBs for all I-frames and P-frames in a double-compressed H.264/AVC video. For a given suspicious video, the SODBs and S-MBs are extracted for each frame. Both features are then incorporated to generate one enhanced feature to represent the periodic artifact of the double-compressed video. Finally, a time-domain analysis is conducted to detect the periodicity of the features. The primary Group of Pictures (GOP size is estimated based on an exhaustive strategy. The experimental results demonstrate the efficacy of the proposed method.

  8. Feature Extraction in IR Images Via Synchronous Video Detection

    Science.gov (United States)

    Shepard, Steven M.; Sass, David T.

    1989-03-01

    IR video images acquired by scanning imaging radiometers are subject to several problems which make measurement of small temperature differences difficult. Among these problems are 1) aliasing, which occurs When events at frequencies higher than the video frame rate are observed, 2) limited temperature resolution imposed by the 3-bit digitization available in existing commercial systems, and 3) susceptibility to noise and background clutter. Bandwidth narrowing devices (e.g. lock-in amplifiers or boxcar averagers) are routinely used to achieve a high degree of signal to noise improvement for time-varying 1-dimensional signals. We will describe techniques which allow similar S/N improvement for 2-dimensional imagery acquired with an off the shelf scanning imaging radiometer system. These techniques are iplemented in near-real-time, utilizing a microcomputer and specially developed hardware and software . We will also discuss the application of the system to feature extraction in cluttered images, and to acquisition of events which vary faster than the frame rate.

  9. Study on the Detection of Moving Target in the Mining Method Based on Hybrid Algorithm for Sports Video Analysis

    Directory of Open Access Journals (Sweden)

    Huang Tian

    2014-10-01

    Full Text Available Moving object detection and tracking is the computer vision and image processing is a hot research direction, based on the analysis of the moving target detection and tracking algorithm in common use, focus on the sports video target tracking non rigid body. In sports video, non rigid athletes often have physical deformation in the process of movement, and may be associated with the occurrence of moving target under cover. Media data is surging to fast search and query causes more difficulties in data. However, the majority of users want to be able to quickly from the multimedia data to extract the interested content and implicit knowledge (concepts, rules, rules, models and correlation, retrieval and query quickly to take advantage of them, but also can provide the decision support problem solving hierarchy. Based on the motion in sport video object as the object of study, conducts the system research from the theoretical level and technical framework and so on, from the layer by layer mining between low level motion features to high-level semantic motion video, not only provides support for users to find information quickly, but also can provide decision support for the user to solve the problem.

  10. Detection and localization of copy-paste forgeries in digital videos.

    Science.gov (United States)

    Singh, Raahat Devender; Aggarwal, Naveen

    2017-12-01

    Amidst the continual march of technology, we find ourselves relying on digital videos to proffer visual evidence in several highly sensitive areas such as journalism, politics, civil and criminal litigation, and military and intelligence operations. However, despite being an indispensable source of information with high evidentiary value, digital videos are also extremely vulnerable to conscious manipulations. Therefore, in a situation where dependence on video evidence is unavoidable, it becomes crucial to authenticate the contents of this evidence before accepting them as an accurate depiction of reality. Digital videos can suffer from several kinds of manipulations, but perhaps, one of the most consequential forgeries is copy-paste forgery, which involves insertion/removal of objects into/from video frames. Copy-paste forgeries alter the information presented by the video scene, which has a direct effect on our basic understanding of what that scene represents, and so, from a forensic standpoint, the challenge of detecting such forgeries is especially significant. In this paper, we propose a sensor pattern noise based copy-paste detection scheme, which is an improved and forensically stronger version of an existing noise-residue based technique. We also study a demosaicing artifact based image forensic scheme to estimate the extent of its viability in the domain of video forensics. Furthermore, we suggest a simplistic clustering technique for the detection of copy-paste forgeries, and determine if it possess the capabilities desired of a viable and efficacious video forensic scheme. Finally, we validate these schemes on a set of realistically tampered MJPEG, MPEG-2, MPEG-4, and H.264/AVC encoded videos in a diverse experimental set-up by varying the strength of post-production re-compressions and transcodings, bitrates, and sizes of the tampered regions. Such an experimental set-up is representative of a neutral testing platform and simulates a real

  11. Video monitoring of visible atmospheric emissions: from a manual device to a new fully automatic detection and classification device; Video surveillance des rejets atmospheriques d'un site siderurgique: d'un systeme manuel a la detection automatique

    Energy Technology Data Exchange (ETDEWEB)

    Bardet, I.; Ryckelynck, F.; Desmonts, T. [Sollac, 59 - Dunkerque (France)

    1999-11-01

    Complete text of publication follows: the context of strong local sensitivity to dust emissions from an integrated steel plant justifies the monitoring of the emissions of abnormally coloured smokes from this plant. In a first step, the watch is done 'visually' by screening and counting the puff emissions through a set of seven cameras and video recorders. The development of a new device making automatic picture analysis allowed to render the inspection automatic. The new system detects and counts the incidents and sends an alarm to the process operator. This way for automatic detection can be extended, after some tests, to other uses in the environmental field. (authors)

  12. People detection in nuclear plants by video processing for safety purpose

    Energy Technology Data Exchange (ETDEWEB)

    Jorge, Carlos Alexandre F.; Mol, Antonio Carlos A., E-mail: calexandre@ien.gov.b, E-mail: mol@ien.gov.b [Instituto de Engenharia Nuclear (IEN/CNEN), Rio de Janeiro, RJ (Brazil); Seixas, Jose M.; Silva, Eduardo Antonio B., E-mail: seixas@lps.ufrj.b, E-mail: eduardo@lps.ufrj.b [Coordenacao dos Programas de Pos-Graduacao de Engenharia (COPPE/UFRJ), Rio de Janeiro, RJ (Brazil). Programa de Engenharia Eletrica; Cota, Raphael E.; Ramos, Bruno L., E-mail: brunolange@poli.ufrj.b [Universidade Federal do Rio de Janeiro (EP/UFRJ), RJ (Brazil). Dept. de Engenharia Eletronica e de Computacao

    2011-07-01

    This work describes the development of a surveillance system for safety purposes in nuclear plants. The final objective is to track people online in videos, in order to estimate the dose received by personnel, during the execution of working tasks in nuclear plants. The estimation will be based on their tracked positions and on dose rate mapping in a real nuclear plant at Instituto de Engenharia Nuclear, Argonauta nuclear research reactor. Cameras have been installed within Argonauta's room, supplying the data needed. Both video processing and statistical signal processing techniques may be used for detection, segmentation and tracking people in video. This first paper reports people segmentation in video using background subtraction, by two different approaches, namely frame differences, and blind signal separation based on the independent component analysis method. Results are commented, along with perspectives for further work. (author)

  13. The Boy Who Learned To Read Through Sustained Video Game Play: Considering Systemic Resistance To The Use Of New Texts In The Classroom

    Directory of Open Access Journals (Sweden)

    Rochelle SKOGEN

    2012-12-01

    Full Text Available Various studies have discussed the pedagogical potential of video game play in the classroom but resistance to such texts remains high. The study presented here discusses the case study of one young boy who, having failed to learn to read in the public school system was able to learn in a private Sudbury model school where video games were not only allowed but considered important learning tools. Findings suggest that the incorporation of such new texts in today’s public schools have the potential to motivate and enhance the learning of children.

  14. A video-based eye pupil detection system for diagnosing bipolar disorder

    OpenAIRE

    AKINCI, Gökay; Polat, Ediz; Koçak, Orhan Murat

    2012-01-01

    Eye pupil detection systems have become increasingly popular in image processing and computer vision applications in medical systems. In this study, a video-based eye pupil detection system is developed for diagnosing bipolar disorder. Bipolar disorder is a condition in which people experience changes in cognitive processes and abilities, including reduced attentional and executive capabilities and impaired memory. In order to detect these abnormal behaviors, a number of neuropsychologi...

  15. An On-Line Method for Thermal Diffusivity Detection of Thin Films Using Infrared Video

    Directory of Open Access Journals (Sweden)

    Dong Huilong

    2016-03-01

    Full Text Available A novel method for thermal diffusivity evolution of thin-film materials with pulsed Gaussian beam and infrared video is reported. Compared with common pulse methods performed in specialized labs, the proposed method implements a rapid on-line measurement without producing the off-centre detection error. Through mathematical deduction of the original heat conduction model, it is discovered that the area s, which is encircled by the maximum temperature curve rTMAX(θ, increases linearly over elapsed time. The thermal diffusivity is acquired from the growth rate of the area s. In this study, the off-centre detection error is avoided by performing the distance regularized level set evolution formulation. The area s was extracted from the binary images of temperature variation rate, without inducing errors from determination of the heat source centre. Thermal diffusivities of three materials, 304 stainless steel, titanium, and zirconium have been measured with the established on-line detection system, and the measurement errors are: −2.26%, −1.07%, and 1.61% respectively.

  16. Efficiently detecting outlying behavior in video-game players

    National Research Council Canada - National Science Library

    Kim, Young Bin; Kang, Shin Jin; Lee, Sang Hyeok; Jung, Jang Young; Kam, Hyeong Ryeol; Lee, Jung; Kim, Young Sun; Lee, Joonsoo; Kim, Chang Hun

    2015-01-01

    In this paper, we propose a method for automatically detecting the times during which game players exhibit specific behavior, such as when players commonly show excitement, concentration, immersion, and surprise...

  17. Efficiently detecting outlying behavior in video-game players

    OpenAIRE

    Kim, Young Bin; Kang, Shin Jin; Lee, Sang Hyeok; Jung, Jang Young; Kam, Hyeong Ryeol; Lee, Jung; Kim, Young Sun; Lee, Joonsoo; Kim, Chang Hun

    2015-01-01

    In this paper, we propose a method for automatically detecting the times during which game players exhibit specific behavior, such as when players commonly show excitement, concentration, immersion, and surprise. The proposed method detects such outlying behavior based on the game players’ characteristics. These characteristics are captured non-invasively in a general game environment. In this paper, cameras were used to analyze observed data such as facial expressions and player movements. M...

  18. Bayesian foreground and shadow detection in uncertain frame rate surveillance videos.

    Science.gov (United States)

    Benedek, C; Sziranyi, T

    2008-04-01

    In in this paper, we propose a new model regarding foreground and shadow detection in video sequences. The model works without detailed a priori object-shape information, and it is also appropriate for low and unstable frame rate video sources. Contribution is presented in three key issues: 1) we propose a novel adaptive shadow model, and show the improvements versus previous approaches in scenes with difficult lighting and coloring effects; 2) we give a novel description for the foreground based on spatial statistics of the neighboring pixel values, which enhances the detection of background or shadow-colored object parts; 3) we show how microstructure analysis can be used in the proposed framework as additional feature components improving the results. Finally, a Markov random field model is used to enhance the accuracy of the separation. We validate our method on outdoor and indoor sequences including real surveillance videos and well-known benchmark test sets.

  19. Real-time billboard trademark detection and recognition in sports video

    Science.gov (United States)

    Bu, Jiang; Lao, Song-Yan; Bai, Liang

    2013-03-01

    Nowadays, different applications like automatic video indexing, keyword based video search and TV commercials can be developed by detecting and recognizing the billboard trademark. We propose a hierarchical solution for real-time billboard trademark recognition in various sports video, billboard frames are detected in the first level, fuzzy decision tree with easily-computing features are employed to accelerate the process, while in the second level, color and regional SIFT features are combined for the first time to describe the appearance of trademarks, and the shared nearest neighbor (SNN) clustering with x2 distance is utilized instead of traditional K-means clustering to construct the SIFT vocabulary, at last, Latent Semantic Analysis (LSA) based SIFT vocabulary matching is performed on the template trademark and the candidate regions in billboard frame. The preliminary experiments demonstrate the effectiveness of the hierarchical solution, and real time constraints are also met by our solution.

  20. Fusion of acoustic measurements with video surveillance for estuarine threat detection

    Science.gov (United States)

    Bunin, Barry; Sutin, Alexander; Kamberov, George; Roh, Heui-Seol; Luczynski, Bart; Burlick, Matt

    2008-04-01

    Stevens Institute of Technology has established a research laboratory environment in support of the U.S. Navy in the area of Anti-Terrorism and Force Protection. Called the Maritime Security Laboratory, or MSL, it provides the capabilities of experimental research to enable development of novel methods of threat detection in the realistic environment of the Hudson River Estuary. In MSL, this is done through a multi-modal interdisciplinary approach. In this paper, underwater acoustic measurements and video surveillance are combined. Stevens' researchers have developed a specialized prototype video system to identify, video-capture, and map surface ships in a sector of the estuary. The combination of acoustic noise with video data for different kinds of ships in Hudson River enabled estimation of sound attenuation in a wide frequency band. Also, it enabled the collection of a noise library of various ships that can be used for ship classification by passive acoustic methods. Acoustics and video can be used to determine a ship's position. This knowledge can be used for ship noise suppression in hydrophone arrays in underwater threat detection. Preliminary experimental results of position determination are presented in the paper.

  1. Facial Video based Detection of Physical Fatigue for Maximal Muscle Activity

    DEFF Research Database (Denmark)

    Haque, Mohammad Ahsanul; Irani, Ramin; Nasrollahi, Kamal

    2016-01-01

    Physical fatigue reveals the health condition of a person at for example health checkup, fitness assessment or rehabilitation training. This paper presents an efficient noncontact system for detecting non-localized physi-cal fatigue from maximal muscle activity using facial videos acquired...

  2. The ImageNet Shuffle: Reorganized Pre-training for Video Event Detection

    NARCIS (Netherlands)

    Mettes, P.; Koelma, D.C.; Snoek, C.G.M.

    2016-01-01

    This paper strives for video event detection using a representation learned from deep convolutional neural networks. Different from the leading approaches, who all learn from the 1,000 classes defined in the ImageNet Large Scale Visual Recognition Challenge, we investigate how to leverage the

  3. Detecting Road Users at Intersections Through Changing Weather Using RGB-Thermal Videos

    DEFF Research Database (Denmark)

    Bahnsen, Chris; Moeslund, Thomas B.

    2015-01-01

    This paper compares the performance of a watch-dog sys- tem that detects road user actions in urban intersections to a KLT- based tracking system used in traffic surveillance. The two approaches are evaluated on 16 hours of video data captured by RGB and ther- mal cameras under challenging light...

  4. Performance optimization for pedestrian detection on degraded video using natural scene statistics

    Science.gov (United States)

    Winterlich, Anthony; Denny, Patrick; Kilmartin, Liam; Glavin, Martin; Jones, Edward

    2014-11-01

    We evaluate the effects of transmission artifacts such as JPEG compression and additive white Gaussian noise on the performance of a state-of-the-art pedestrian detection algorithm, which is based on integral channel features. Integral channel features combine the diversity of information obtained from multiple image channels with the computational efficiency of the Viola and Jones detection framework. We utilize "quality aware" spatial image statistics to blindly categorize distorted video frames by distortion type and level without the use of an explicit reference. We combine quality statistics with a multiclassifier detection framework for optimal pedestrian detection performance across varying image quality. Our detection method provides statistically significant improvements over current approaches based on single classifiers, on two large pedestrian databases containing a wide variety of artificially added distortion. The improvement in detection performance is further demonstrated on real video data captured from multiple cameras containing varying levels of sensor noise and compression. The results of our research have the potential to be used in real-time in-vehicle networks to improve pedestrian detection performance across a wide range of image and video quality.

  5. Chemical Detection Architecture for a Subway System [video

    OpenAIRE

    Ignacio, Joselito; Center for Homeland Defense and Security Naval Postgraduate School

    2014-01-01

    This proposed system process aims to improve subway safety through better enabling the rapid detection and response to a chemical release in a subway system. The process is designed to be location-independent and generalized to most subway systems despite each system's unique characteristics.

  6. Supported eText in Captioned Videos: A Comparison of Expanded versus Standard Captions on Student Comprehension of Educational Content

    Science.gov (United States)

    Anderson-Inman, Lynne; Terrazas-Arellanes, Fatima E.

    2009-01-01

    Expanded captions are designed to enhance the educational value by linking unfamiliar words to one of three types of information: vocabulary definitions, labeled illustrations, or concept maps. This study investigated the effects of expanded captions versus standard captions on the comprehension of educational video materials on DVD by secondary…

  7. SMART VIDEO SURVEILLANCE SYSTEM FOR VEHICLE DETECTION AND TRAFFIC FLOW CONTROL

    Directory of Open Access Journals (Sweden)

    A. A. SHAFIE

    2011-08-01

    Full Text Available Traffic signal light can be optimized using vehicle flow statistics obtained by Smart Video Surveillance Software (SVSS. This research focuses on efficient traffic control system by detecting and counting the vehicle numbers at various times and locations. At present, one of the biggest problems in the main city in any country is the traffic jam during office hour and office break hour. Sometimes it can be seen that the traffic signal green light is still ON even though there is no vehicle coming. Similarly, it is also observed that long queues of vehicles are waiting even though the road is empty due to traffic signal light selection without proper investigation on vehicle flow. This can be handled by adjusting the vehicle passing time implementing by our developed SVSS. A number of experiment results of vehicle flows are discussed in this research graphically in order to test the feasibility of the developed system. Finally, adoptive background model is proposed in SVSS in order to successfully detect target objects such as motor bike, car, bus, etc.

  8. A Macro-Observation Scheme for Abnormal Event Detection in Daily-Life Video Sequences

    Directory of Open Access Journals (Sweden)

    Chiu Wei-Yao

    2010-01-01

    Full Text Available Abstract We propose a macro-observation scheme for abnormal event detection in daily life. The proposed macro-observation representation records the time-space energy of motions of all moving objects in a scene without segmenting individual object parts. The energy history of each pixel in the scene is instantly updated with exponential weights without explicitly specifying the duration of each activity. Since possible activities in daily life are numerous and distinct from each other and not all abnormal events can be foreseen, images from a video sequence that spans sufficient repetition of normal day-to-day activities are first randomly sampled. A constrained clustering model is proposed to partition the sampled images into groups. The new observed event that has distinct distance from any of the cluster centroids is then classified as an anomaly. The proposed method has been evaluated in daily work of a laboratory and BEHAVE benchmark dataset. The experimental results reveal that it can well detect abnormal events such as burglary and fighting as long as they last for a sufficient duration of time. The proposed method can be used as a support system for the scene that requires full time monitoring personnel.

  9. A 3-Step Algorithm Using Region-Based Active Contours for Video Objects Detection

    Directory of Open Access Journals (Sweden)

    Stéphanie Jehan-Besson

    2002-06-01

    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.

  10. Learners' Use of Communication Strategies in Text-Based and Video-Based Synchronous Computer-Mediated Communication Environments: Opportunities for Language Learning

    Science.gov (United States)

    Hung, Yu-Wan; Higgins, Steve

    2016-01-01

    This study investigates the different learning opportunities enabled by text-based and video-based synchronous computer-mediated communication (SCMC) from an interactionist perspective. Six Chinese-speaking learners of English and six English-speaking learners of Chinese were paired up as tandem (reciprocal) learning dyads. Each dyad participated…

  11. Pregnancy Prevention at Her Fingertips: A Text- and Mobile Video-Based Pilot Intervention to Promote Contraceptive Methods among College Women

    Science.gov (United States)

    Walsh-Buhi, Eric R.; Helmy, Hannah; Harsch, Kristin; Rella, Natalie; Godcharles, Cheryl; Ogunrunde, Adejoke; Lopez Castillo, Humberto

    2016-01-01

    Objective: This paper reports on a pilot study evaluating the feasibility and acceptability of a text- and mobile video-based intervention to educate women and men attending college about non-daily contraception, with a particular focus on long-acting reversible contraception (LARC). A secondary objective is to describe the process of intervention…

  12. The research of moving objects behavior detection and tracking algorithm in aerial video

    Science.gov (United States)

    Yang, Le-le; Li, Xin; Yang, Xiao-ping; Li, Dong-hui

    2015-12-01

    The article focuses on the research of moving target detection and tracking algorithm in Aerial monitoring. Study includes moving target detection, moving target behavioral analysis and Target Auto tracking. In moving target detection, the paper considering the characteristics of background subtraction and frame difference method, using background reconstruction method to accurately locate moving targets; in the analysis of the behavior of the moving object, using matlab technique shown in the binary image detection area, analyzing whether the moving objects invasion and invasion direction; In Auto Tracking moving target, A video tracking algorithm that used the prediction of object centroids based on Kalman filtering was proposed.

  13. Extracting foreground ensemble features to detect abnormal crowd behavior in intelligent video-surveillance systems

    Science.gov (United States)

    Chan, Yi-Tung; Wang, Shuenn-Jyi; Tsai, Chung-Hsien

    2017-09-01

    Public safety is a matter of national security and people's livelihoods. In recent years, intelligent video-surveillance systems have become important active-protection systems. A surveillance system that provides early detection and threat assessment could protect people from crowd-related disasters and ensure public safety. Image processing is commonly used to extract features, e.g., people, from a surveillance video. However, little research has been conducted on the relationship between foreground detection and feature extraction. Most current video-surveillance research has been developed for restricted environments, in which the extracted features are limited by having information from a single foreground; they do not effectively represent the diversity of crowd behavior. This paper presents a general framework based on extracting ensemble features from the foreground of a surveillance video to analyze a crowd. The proposed method can flexibly integrate different foreground-detection technologies to adapt to various monitored environments. Furthermore, the extractable representative features depend on the heterogeneous foreground data. Finally, a classification algorithm is applied to these features to automatically model crowd behavior and distinguish an abnormal event from normal patterns. The experimental results demonstrate that the proposed method's performance is both comparable to that of state-of-the-art methods and satisfies the requirements of real-time applications.

  14. CHARACTER RECOGNITION OF VIDEO SUBTITLES\\

    Directory of Open Access Journals (Sweden)

    Satish S Hiremath

    2016-11-01

    Full Text Available An important task in content based video indexing is to extract text information from videos. The challenges involved in text extraction and recognition are variation of illumination on each video frame with text, the text present on the complex background and different font size of the text. Using various image processing algorithms like morphological operations, blob detection and histogram of oriented gradients the character recognition of video subtitles is implemented. Segmentation, feature extraction and classification are the major steps of character recognition. Several experimental results are shown to demonstrate the performance of the proposed algorithm

  15. Ethical use of covert videoing techniques in detecting Munchausen syndrome by proxy.

    Science.gov (United States)

    Foreman, D M; Farsides, C

    1993-01-01

    Munchausen syndrome by proxy is an especially malignant form of child abuse in which the carer (usually the mother) fabricates or exacerbates illness in the child to obtain medical attention. It can result in serious illness and even death of the child and it is difficult to detect. Some investigators have used video to monitor the carer's interaction with the child without obtaining consent--covert videoing. The technique presents several ethical problems, including exposure of the child to further abuse and a breach of trust between carer, child, and the professionals. Although covert videoing can be justified in restricted circumstances, new abuse procedures under the Children Act now seem to make its use unethical in most cases. Sufficient evidence should mostly be obtained from separation of the child and carer or videoing with consent to enable action to be taken to protect the child under an assessment order. If the new statutory instruments prove ineffective in Munchausen syndrome by proxy covert videoing may need to be re-evaluated. PMID:8401021

  16. Improving Students� Ability in Writing Hortatory Exposition Texts by Using Process-Genre Based Approach with YouTube Videos as the Media

    Directory of Open Access Journals (Sweden)

    fifin naili rizkiyah

    2017-06-01

    Full Text Available Abstract: This research is aimed at finding out how Process-Genre Based Approach strategy with YouTube Videos as the media are employed to improve the students� ability in writing hortatory exposition texts. This study uses collaborative classroom action research design following the procedures namely planning, implementing, observing, and reflecting. The procedures of carrying out the strategy are: (1 relating several issues/ cases to the students� background knowledge and introducing the generic structures and linguistic features of hortatory exposition text as the BKoF stage, (2 analyzing the generic structure and the language features used in the text and getting model on how to write a hortatory exposition text by using the YouTube Video as the MoT stage, (3 writing a hortatory exposition text collaboratively in a small group and in pairs through process writing as the JCoT stage, and (4 writing a hortatory exposition text individually as the ICoT stage. The result shows that the use of Process-Genre Based Approach and YouTube Videos can improve the students� ability in writing hortatory exposition texts. The percentage of the students achieving the score above the minimum passing grade (70 had improved from only 15.8% (3 out of 19 students in the preliminary study to 100% (22 students in the Cycle 1. Besides, the score of each aspect; content, organization, vocabulary, grammar, and mechanics also improved. � Key Words: writing ability, hortatory exposition text, process-genre based approach, youtube video

  17. Integrating Online and Offline Three-Dimensional Deep Learning for Automated Polyp Detection in Colonoscopy Videos.

    Science.gov (United States)

    Lequan Yu; Hao Chen; Qi Dou; Jing Qin; Pheng Ann Heng

    2017-01-01

    Automated polyp detection in colonoscopy videos has been demonstrated to be a promising way for colorectal cancer prevention and diagnosis. Traditional manual screening is time consuming, operator dependent, and error prone; hence, automated detection approach is highly demanded in clinical practice. However, automated polyp detection is very challenging due to high intraclass variations in polyp size, color, shape, and texture, and low interclass variations between polyps and hard mimics. In this paper, we propose a novel offline and online three-dimensional (3-D) deep learning integration framework by leveraging the 3-D fully convolutional network (3D-FCN) to tackle this challenging problem. Compared with the previous methods employing hand-crafted features or 2-D convolutional neural network, the 3D-FCN is capable of learning more representative spatio-temporal features from colonoscopy videos, and hence has more powerful discrimination capability. More importantly, we propose a novel online learning scheme to deal with the problem of limited training data by harnessing the specific information of an input video in the learning process. We integrate offline and online learning to effectively reduce the number of false positives generated by the offline network and further improve the detection performance. Extensive experiments on the dataset of MICCAI 2015 Challenge on Polyp Detection demonstrated the better performance of our method when compared with other competitors.

  18. An automatic system to detect and extract texts in medical images for de-identification

    Science.gov (United States)

    Zhu, Yingxuan; Singh, P. D.; Siddiqui, Khan; Gillam, Michael

    2010-03-01

    Recently, there is an increasing need to share medical images for research purpose. In order to respect and preserve patient privacy, most of the medical images are de-identified with protected health information (PHI) before research sharing. Since manual de-identification is time-consuming and tedious, so an automatic de-identification system is necessary and helpful for the doctors to remove text from medical images. A lot of papers have been written about algorithms of text detection and extraction, however, little has been applied to de-identification of medical images. Since the de-identification system is designed for end-users, it should be effective, accurate and fast. This paper proposes an automatic system to detect and extract text from medical images for de-identification purposes, while keeping the anatomic structures intact. First, considering the text have a remarkable contrast with the background, a region variance based algorithm is used to detect the text regions. In post processing, geometric constraints are applied to the detected text regions to eliminate over-segmentation, e.g., lines and anatomic structures. After that, a region based level set method is used to extract text from the detected text regions. A GUI for the prototype application of the text detection and extraction system is implemented, which shows that our method can detect most of the text in the images. Experimental results validate that our method can detect and extract text in medical images with a 99% recall rate. Future research of this system includes algorithm improvement, performance evaluation, and computation optimization.

  19. Pedestrian detection in video surveillance using fully convolutional YOLO neural network

    Science.gov (United States)

    Molchanov, V. V.; Vishnyakov, B. V.; Vizilter, Y. V.; Vishnyakova, O. V.; Knyaz, V. A.

    2017-06-01

    More than 80% of video surveillance systems are used for monitoring people. Old human detection algorithms, based on background and foreground modelling, could not even deal with a group of people, to say nothing of a crowd. Recent robust and highly effective pedestrian detection algorithms are a new milestone of video surveillance systems. Based on modern approaches in deep learning, these algorithms produce very discriminative features that can be used for getting robust inference in real visual scenes. They deal with such tasks as distinguishing different persons in a group, overcome problem with sufficient enclosures of human bodies by the foreground, detect various poses of people. In our work we use a new approach which enables to combine detection and classification tasks into one challenge using convolution neural networks. As a start point we choose YOLO CNN, whose authors propose a very efficient way of combining mentioned above tasks by learning a single neural network. This approach showed competitive results with state-of-the-art models such as FAST R-CNN, significantly overcoming them in speed, which allows us to apply it in real time video surveillance and other video monitoring systems. Despite all advantages it suffers from some known drawbacks, related to the fully-connected layers that obstruct applying the CNN to images with different resolution. Also it limits the ability to distinguish small close human figures in groups which is crucial for our tasks since we work with rather low quality images which often include dense small groups of people. In this work we gradually change network architecture to overcome mentioned above problems, train it on a complex pedestrian dataset and finally get the CNN detecting small pedestrians in real scenes.

  20. A clinically viable capsule endoscopy video analysis platform for automatic bleeding detection

    Science.gov (United States)

    Yi, Steven; Jiao, Heng; Xie, Jean; Mui, Peter; Leighton, Jonathan A.; Pasha, Shabana; Rentz, Lauri; Abedi, Mahmood

    2013-02-01

    In this paper, we present a novel and clinically valuable software platform for automatic bleeding detection on gastrointestinal (GI) tract from Capsule Endoscopy (CE) videos. Typical CE videos for GI tract run about 8 hours and are manually reviewed by physicians to locate diseases such as bleedings and polyps. As a result, the process is time consuming and is prone to disease miss-finding. While researchers have made efforts to automate this process, however, no clinically acceptable software is available on the marketplace today. Working with our collaborators, we have developed a clinically viable software platform called GISentinel for fully automated GI tract bleeding detection and classification. Major functional modules of the SW include: the innovative graph based NCut segmentation algorithm, the unique feature selection and validation method (e.g. illumination invariant features, color independent features, and symmetrical texture features), and the cascade SVM classification for handling various GI tract scenes (e.g. normal tissue, food particles, bubbles, fluid, and specular reflection). Initial evaluation results on the SW have shown zero bleeding instance miss-finding rate and 4.03% false alarm rate. This work is part of our innovative 2D/3D based GI tract disease detection software platform. While the overall SW framework is designed for intelligent finding and classification of major GI tract diseases such as bleeding, ulcer, and polyp from the CE videos, this paper will focus on the automatic bleeding detection functional module.

  1. The use of telehealth (text messaging and video communications) in patients with cystic fibrosis: A pilot study.

    Science.gov (United States)

    Gur, Michal; Nir, Vered; Teleshov, Anna; Bar-Yoseph, Ronen; Manor, Eynav; Diab, Gizelle; Bentur, Lea

    2017-05-01

    Background Poor communications between cystic fibrosis (CF) patients and health-care providers may result in gaps in knowledge and misconceptions about medication usage, and can lead to poor adherence. We aimed to assess the feasibility of using WhatsApp and Skype to improve communications. Methods This single-centre pilot study included CF patients who were older than eight years of age assigned to two groups: one without intervention (control group), and one with intervention. Each patient from the intervention group received Skype-based online video chats and WhatsApp messages from members of the multidisciplinary CF team. CF questionnaires, revised (CFQ-R) scores, knowledge and adherence based on CF My Way and patients satisfaction were evaluated before and after three months. Feasibility was assessed by session attendance, acceptability and satisfaction survey. Descriptive analysis and paired and non-paired t-tests were used as applicable. Results Eighteen patients were recruited to this feasibility study (nine in each group). Each intervention group participant had between four and six Skype video chats and received 22-45 WhatsApp messages. In this small study, CFQ-R scores, knowledge, adherence and patient satisfaction were similar in both groups before and after the three-month intervention. Conclusions A telehealth-based approach, using Skype video chats and WhatsApp messages, was feasible and acceptable in this pilot study. A larger and longer multi-centre study is warranted to examine the efficacy of these interventions to improve knowledge, adherence and communication.

  2. Scene Text Detection and Segmentation based on Cascaded Convolution Neural Networks.

    Science.gov (United States)

    Tang, Youbao; Wu, Xiangqian

    2017-01-20

    Scene text detection and segmentation are two important and challenging research problems in the field of computer vision. This paper proposes a novel method for scene text detection and segmentation based on cascaded convolution neural networks (CNNs). In this method, a CNN based text-aware candidate text region (CTR) extraction model (named detection network, DNet) is designed and trained using both the edges and the whole regions of text, with which coarse CTRs are detected. A CNN based CTR refinement model (named segmentation network, SNet) is then constructed to precisely segment the coarse CTRs into text to get the refined CTRs. With DNet and SNet, much fewer CTRs are extracted than with traditional approaches while more true text regions are kept. The refined CTRs are finally classified using a CNN based CTR classification model (named classification network, CNet) to get the final text regions. All of these CNN based models are modified from VGGNet-16. Extensive experiments on three benchmark datasets demonstrate that the proposed method achieves state-of-the-art performance and greatly outperforms other scene text detection and segmentation approaches.

  3. Detection and Separation of Speech Event Using Audio and Video Information Fusion and Its Application to Robust Speech Interface

    Directory of Open Access Journals (Sweden)

    Futoshi Asano

    2004-09-01

    Full Text Available A method of detecting speech events in a multiple-sound-source condition using audio and video information is proposed. For detecting speech events, sound localization using a microphone array and human tracking by stereo vision is combined by a Bayesian network. From the inference results of the Bayesian network, information on the time and location of speech events can be known. The information on the detected speech events is then utilized in the robust speech interface. A maximum likelihood adaptive beamformer is employed as a preprocessor of the speech recognizer to separate the speech signal from environmental noise. The coefficients of the beamformer are kept updated based on the information of the speech events. The information on the speech events is also used by the speech recognizer for extracting the speech segment.

  4. Lexicon-based detection of emotion in different types of texts: Preliminary remarks


    Directory of Open Access Journals (Sweden)

    Hille Pajupuu

    2012-05-01

    Full Text Available Paragraphs of four genres are analysed to detect their emotional colouring, while a lexicon-based approach of linguistic analysis is weighed against reader opinion. The aim is to find out the prospects of automatic detection of emotion in any text by using a very small lexicon of about 600 frequent emotion words.DOI: http://dx.doi.org/10.5128/ERYa8.11

  5. Optimizing a neural network for detection of moving vehicles in video

    Science.gov (United States)

    Fischer, Noëlle M.; Kruithof, Maarten C.; Bouma, Henri

    2017-10-01

    In the field of security and defense, it is extremely important to reliably detect moving objects, such as cars, ships, drones and missiles. Detection and analysis of moving objects in cameras near borders could be helpful to reduce illicit trading, drug trafficking, irregular border crossing, trafficking in human beings and smuggling. Many recent benchmarks have shown that convolutional neural networks are performing well in the detection of objects in images. Most deep-learning research effort focuses on classification or detection on single images. However, the detection of dynamic changes (e.g., moving objects, actions and events) in streaming video is extremely relevant for surveillance and forensic applications. In this paper, we combine an end-to-end feedforward neural network for static detection with a recurrent Long Short-Term Memory (LSTM) network for multi-frame analysis. We present a practical guide with special attention to the selection of the optimizer and batch size. The end-to-end network is able to localize and recognize the vehicles in video from traffic cameras. We show an efficient way to collect relevant in-domain data for training with minimal manual labor. Our results show that the combination with LSTM improves performance for the detection of moving vehicles.

  6. THE COMPARISON OF DESCRIPTIVE TEXT WRITING ABILITY USING YOU TUBE DOWNLOADED VIDEO AND SERIAL PICTURES AT THE STUDENTS’OF SMPN 2 METROACADEMIC YEAR 2012/2013

    Directory of Open Access Journals (Sweden)

    Eka Bayu Pramanca

    2013-10-01

    Full Text Available This research discusses about how two different techniques affect the students’ ability in descriptive text at SMP N 2 Metro. The objectives of this research are (1 to know the difference result of using YouTube Downloaded Video and Serial Pictures media toward students’ writing ability in descriptive text and (2 to know which one is more effective of students’ writing ability in descriptive text instruction between learning by using YouTube Downloaded Video and Serial Pictures media. The implemented method is quantitative research design in that both researchers use true experimental research design. In this research , experimental and control class pre-test and post test are conducted. It is carried out at the first grade of SMP N 2 Metro in academic year 2012/2013. The population in this research is 7 different classes with total number of 224 students. 2 classes of the total population are taken as the samples; VII.1 students in experimental class and VII.2 students  in control class by using cluster random sampling technique.  The instruments of the research are tests, treatment and post-test. The data analyzing procedure uses t-test  and results the following output. The result of ttest is 3,96 and ttable  is 2,06. It means that tcount > ttable with the criterion of ttest is Ha is accepted if tcount  > ttable. So, there is any difference result of students’ writing ability using YouTube Downloaded Video and Serial Pictures Media. However; Youtube Downloaded Video media is more effective media than Serial Pictures media toward students’ writing ability. This research is consistent with the previous result of the studies and thus this technique is  recommended to use in writing instruction especially in descriptive text in order that students may feel fun and enjoy during the  learning process.

  7. Research on the video detection device in the invisible part of stay cable anchorage system

    Science.gov (United States)

    Cai, Lin; Deng, Nianchun; Xiao, Zexin

    2012-11-01

    The cables in anchorage zone of cable-stayed bridge are hidden within the embedded pipe, which leads to the difficulty for detecting the damage of the cables with visual inspection. We have built a detection device based on high-resolution video capture, realized the distance observing of invisible segment of stay cable and damage detection of outer surface of cable in the small volume. The system mainly consists of optical stents and precision mechanical support device, optical imaging system, lighting source, drived motor control and IP camera video capture system. The principal innovations of the device are ⑴A set of telescope objectives with three different focal lengths are designed and used in different distances of the monitors by means of converter. ⑵Lens system is far separated with lighting system, so that the imaging optical path could effectively avoid the harsh environment which would be in the invisible part of cables. The practice shows that the device not only can collect the clear surveillance video images of outer surface of cable effectively, but also has a broad application prospect in security warning of prestressed structures.

  8. Authentication of Surveillance Videos: Detecting Frame Duplication Based on Residual Frame.

    Science.gov (United States)

    Fadl, Sondos M; Han, Qi; Li, Qiong

    2017-10-16

    Nowadays, surveillance systems are used to control crimes. Therefore, the authenticity of digital video increases the accuracy of deciding to admit the digital video as legal evidence or not. Inter-frame duplication forgery is the most common type of video forgery methods. However, many existing methods have been proposed for detecting this type of forgery and these methods require high computational time and impractical. In this study, we propose an efficient inter-frame duplication detection algorithm based on standard deviation of residual frames. Standard deviation of residual frame is applied to select some frames and ignore others, which represent a static scene. Then, the entropy of discrete cosine transform coefficients is calculated for each selected residual frame to represent its discriminating feature. Duplicated frames are then detected exactly using subsequence feature analysis. The experimental results demonstrated that the proposed method is effective to identify inter-frame duplication forgery with localization and acceptable running time. © 2017 American Academy of Forensic Sciences.

  9. Detective text of post-modernism: precedential phenomena as linguacultural markers of intertexuality

    Directory of Open Access Journals (Sweden)

    Tuova Ruzana Hamedovna

    2015-12-01

    Full Text Available Detective postmodern text is characterized by active functioning of gaming modality, which is partly responsible for its inclusion in the semantic space of precedent phenomena. Precedential phenomena mark elements of linguaculture and intertextuality as one of the important features of the postmodern age. Dual transformation, which is subjected to precedent phenomena determines ultimately receptive-interpretive activity of the reader: producing text simulates certain perception through the signs of the text of the addresser and the recipient creates a new text according to their own ideas about the text-addresser and the author’s vision. Polyfunctionality of postmodern text is determined by the presence in it of precedent phenomena and polyvariety of interpretations of detective texts due to the interaction with the case genre that is reflected in the author’s game with meanings. Game modality focused on comic effect by binding the text and contrasting concepts, which helps the recipient to render artistic images, characters and plot situations, strengthening the vitality of precedent phenomena. In detective novels by Boris Akunin texts and precedent names are widely used, provoking the reader to intellectual activity and, thus, involving in the interpretation of the text of its common cultural postmodern experience.

  10. Automated Video Detection of Epileptic Convulsion Slowing as a Precursor for Post-Seizure Neuronal Collapse.

    Science.gov (United States)

    Kalitzin, Stiliyan N; Bauer, Prisca R; Lamberts, Robert J; Velis, Demetrios N; Thijs, Roland D; Lopes Da Silva, Fernando H

    2016-12-01

    Automated monitoring and alerting for adverse events in people with epilepsy can provide higher security and quality of life for those who suffer from this debilitating condition. Recently, we found a relation between clonic slowing at the end of a convulsive seizure (CS) and the occurrence and duration of a subsequent period of postictal generalized EEG suppression (PGES). Prolonged periods of PGES can be predicted by the amount of progressive increase of interclonic intervals (ICIs) during the seizure. The purpose of the present study is to develop an automated, remote video sensing-based algorithm for real-time detection of significant clonic slowing that can be used to alert for PGES. This may help preventing sudden unexpected death in epilepsy (SUDEP). The technique is based on our previously published optical flow video sequence processing paradigm that was applied for automated detection of major motor seizures. Here, we introduce an integral Radon-like transformation on the time-frequency wavelet spectrum to detect log-linear frequency changes during the seizure. We validate the automated detection and quantification of the ICI increase by comparison to the results from manually processed electroencephalography (EEG) traces as "gold standard". We studied 48 cases of convulsive seizures for which synchronized EEG-video recordings were available. In most cases, the spectral ridges obtained from Gabor-wavelet transformations of the optical flow group velocities were in close proximity to the ICI traces detected manually from EEG data during the seizure. The quantification of the slowing-down effect measured by the dominant angle in the Radon transformed spectrum was significantly correlated with the exponential ICI increase factors obtained from manual detection. If this effect is validated as a reliable precursor of PGES periods that lead to or increase the probability of SUDEP, the proposed method would provide an efficient alerting device.

  11. Automatic detection of adverse events to predict drug label changes using text and data mining techniques.

    Science.gov (United States)

    Gurulingappa, Harsha; Toldo, Luca; Rajput, Abdul Mateen; Kors, Jan A; Taweel, Adel; Tayrouz, Yorki

    2013-11-01

    The aim of this study was to assess the impact of automatically detected adverse event signals from text and open-source data on the prediction of drug label changes. Open-source adverse effect data were collected from FAERS, Yellow Cards and SIDER databases. A shallow linguistic relation extraction system (JSRE) was applied for extraction of adverse effects from MEDLINE case reports. Statistical approach was applied on the extracted datasets for signal detection and subsequent prediction of label changes issued for 29 drugs by the UK Regulatory Authority in 2009. 76% of drug label changes were automatically predicted. Out of these, 6% of drug label changes were detected only by text mining. JSRE enabled precise identification of four adverse drug events from MEDLINE that were undetectable otherwise. Changes in drug labels can be predicted automatically using data and text mining techniques. Text mining technology is mature and well-placed to support the pharmacovigilance tasks. Copyright © 2013 John Wiley & Sons, Ltd.

  12. Unsupervised Video Shot Detection Using Clustering Ensemble with a Color Global Scale-Invariant Feature Transform Descriptor

    National Research Council Canada - National Science Library

    Chang, Yuchou; Lee, DJ; Hong, Yi; Archibald, James

    .... In this paper, we present the development of a novel color feature extraction algorithm that addresses this problem, and we also propose a new clustering strategy using clustering ensembles for video shot detection...

  13. Moving object detection in top-view aerial videos improved by image stacking

    Science.gov (United States)

    Teutsch, Michael; Krüger, Wolfgang; Beyerer, Jürgen

    2017-08-01

    Image stacking is a well-known method that is used to improve the quality of images in video data. A set of consecutive images is aligned by applying image registration and warping. In the resulting image stack, each pixel has redundant information about its intensity value. This redundant information can be used to suppress image noise, resharpen blurry images, or even enhance the spatial image resolution as done in super-resolution. Small moving objects in the videos usually get blurred or distorted by image stacking and thus need to be handled explicitly. We use image stacking in an innovative way: image registration is applied to small moving objects only, and image warping blurs the stationary background that surrounds the moving objects. Our video data are coming from a small fixed-wing unmanned aerial vehicle (UAV) that acquires top-view gray-value images of urban scenes. Moving objects are mainly cars but also other vehicles such as motorcycles. The resulting images, after applying our proposed image stacking approach, are used to improve baseline algorithms for vehicle detection and segmentation. We improve precision and recall by up to 0.011, which corresponds to a reduction of the number of false positive and false negative detections by more than 3 per second. Furthermore, we show how our proposed image stacking approach can be implemented efficiently.

  14. A hybrid method of natural scene text detection using MSERs masks in HSV space color

    Science.gov (United States)

    Turki, Houssem; Ben Halima, Mohamed; Alimi, Adel M.

    2017-03-01

    Text detection in natural scenes holds great importance in the field of research and still remains a challenge and an important task because of size, various fonts, line orientation, different illumination conditions, weak characters and complex backgrounds in image. The contribution of our proposed method is to filtering out complex backgrounds by combining three strategies. These are enhancing the edge candidate detection in HSV space color, then using MSER candidate detection to get different masks applied in HSV space color as well as gray color. After that, we opt for the Stroke Width Transform (SWT) and heuristic filtering. Such strategies are followed so as to maximize the capacity of zones text pixels candidates and distinguish between text boxes and the rest of the image. The non-text components are filtered by classifying the characters candidates based on Support Vector Machines (SVM) using Histogram of Oriented Gradients (HOG) features. Finally we apply boundary box localization after a stage of word grouping where false positives are eliminated by geometrical properties of text blocks. The proposed method has been evaluated on ICDAR 2013 scene text detection competition dataset and the encouraging experiments results demonstrate the robustness of our method.

  15. Harnessing the Power of Text Mining for the Detection of Abusive Content in Social Media

    OpenAIRE

    Chen, Hao; McKeever, Susan; Delany, Sarah Jane

    2016-01-01

    Abstract The issues of cyberbullying and online harassment have gained considerable coverage in the last number of years. Social media providers need to be able to detect abusive content both accurately and efficiently in order to protect their users. Our aim is to investigate the application of core text mining techniques for the automatic detection of abusive content across a range of social media sources include blogs, forums, media-sharing, Q&A and chat - using datasets from Twitter, YouT...

  16. Lexicon-based detection of emotion in different types of texts: Preliminary remarks


    OpenAIRE

    Hille Pajupuu; Krista Kerge; Rene Altrov

    2012-01-01

    Paragraphs of four genres are analysed to detect their emotional colouring, while a lexicon-based approach of linguistic analysis is weighed against reader opinion. The aim is to find out the prospects of automatic detection of emotion in any text by using a very small lexicon of about 600 frequent emotion words.DOI: http://dx.doi.org/10.5128/ERYa8.11

  17. Research of Pedestrian Crossing Safety Facilities Based on the Video Detection

    Science.gov (United States)

    Li, Sheng-Zhen; Xie, Quan-Long; Zang, Xiao-Dong; Tang, Guo-Jun

    Since that the pedestrian crossing facilities at present is not perfect, pedestrian crossing is in chaos and pedestrians from opposite direction conflict and congest with each other, which severely affects the pedestrian traffic efficiency, obstructs the vehicle and bringing about some potential security problems. To solve these problems, based on video identification, a pedestrian crossing guidance system was researched and designed. It uses the camera to monitor the pedestrians in real time and sums up the number of pedestrians through video detection program, and a group of pedestrian's induction lamp array is installed at the interval of crosswalk, which adjusts color display according to the proportion of pedestrians from both sides to guide pedestrians from both opposite directions processing separately. The emulation analysis result from cellular automaton shows that the system reduces the pedestrian crossing conflict, shortens the time of pedestrian crossing and improves the safety of pedestrians crossing.

  18. Shuttlecock detection system for fully-autonomous badminton robot with two high-speed video cameras

    Science.gov (United States)

    Masunari, T.; Yamagami, K.; Mizuno, M.; Une, S.; Uotani, M.; Kanematsu, T.; Demachi, K.; Sano, S.; Nakamura, Y.; Suzuki, S.

    2017-02-01

    Two high-speed video cameras are successfully used to detect the motion of a flying shuttlecock of badminton. The shuttlecock detection system is applied to badminton robots that play badminton fully autonomously. The detection system measures the three dimensional position and velocity of a flying shuttlecock, and predicts the position where the shuttlecock falls to the ground. The badminton robot moves quickly to the position where the shuttle-cock falls to, and hits the shuttlecock back into the opponent's side of the court. In the game of badminton, there is a large audience, and some of them move behind a flying shuttlecock, which are a kind of background noise and makes it difficult to detect the motion of the shuttlecock. The present study demonstrates that such noises can be eliminated by the method of stereo imaging with two high-speed cameras.

  19. Recognition and defect detection of dot-matrix text via variation-model based learning

    Science.gov (United States)

    Ohyama, Wataru; Suzuki, Koushi; Wakabayashi, Tetsushi

    2017-03-01

    An algorithm for recognition and defect detection of dot-matrix text printed on products is proposed. Extraction and recognition of dot-matrix text contains several difficulties, which are not involved in standard camera-based OCR, that the appearance of dot-matrix characters is corrupted and broken by illumination, complex texture in the background and other standard characters printed on product packages. We propose a dot-matrix text extraction and recognition method which does not require any user interaction. The method employs detected location of corner points and classification score. The result of evaluation experiment using 250 images shows that recall and precision of extraction are 78.60% and 76.03%, respectively. Recognition accuracy of correctly extracted characters is 94.43%. Detecting printing defect of dot-matrix text is also important in the production scene to avoid illegal productions. We also propose a detection method for printing defect of dot-matrix characters. The method constructs a feature vector of which elements are classification scores of each character class and employs support vector machine to classify four types of printing defect. The detection accuracy of the proposed method is 96.68 %.

  20. Automatic parametrization of Support Vector Machines for short texts polarity detection

    Directory of Open Access Journals (Sweden)

    Aurelio Sanabria Rodríguez

    2017-04-01

    Full Text Available The information from social media is emerging as a valuable source in decision-making, unfortunately the tools to turn these data into useful information still need some work. Using Support Vector Machines for polarity detection in short texts are popular among researchers for their good results, but parameter optimization to train classification models is a complex and costly process. This article compares two algorithms for automated parameter optimization in the process of creating classification models for polarity detection: the recently created Grey Wolf Optimizer and the Grid Search, using accuracy and f-score metrics.

  1. Detection and Localization of Robotic Tools in Robot-Assisted Surgery Videos Using Deep Neural Networks for Region Proposal and Detection.

    Science.gov (United States)

    Sarikaya, Duygu; Corso, Jason J; Guru, Khurshid A

    2017-07-01

    Video understanding of robot-assisted surgery (RAS) videos is an active research area. Modeling the gestures and skill level of surgeons presents an interesting problem. The insights drawn may be applied in effective skill acquisition, objective skill assessment, real-time feedback, and human-robot collaborative surgeries. We propose a solution to the tool detection and localization open problem in RAS video understanding, using a strictly computer vision approach and the recent advances of deep learning. We propose an architecture using multimodal convolutional neural networks for fast detection and localization of tools in RAS videos. To the best of our knowledge, this approach will be the first to incorporate deep neural networks for tool detection and localization in RAS videos. Our architecture applies a region proposal network (RPN) and a multimodal two stream convolutional network for object detection to jointly predict objectness and localization on a fusion of image and temporal motion cues. Our results with an average precision of 91% and a mean computation time of 0.1 s per test frame detection indicate that our study is superior to conventionally used methods for medical imaging while also emphasizing the benefits of using RPN for precision and efficiency. We also introduce a new data set, ATLAS Dione, for RAS video understanding. Our data set provides video data of ten surgeons from Roswell Park Cancer Institute, Buffalo, NY, USA, performing six different surgical tasks on the daVinci Surgical System (dVSS) with annotations of robotic tools per frame.

  2. Automated Polyp Detection in Colonoscopy Videos Using Shape and Context Information.

    Science.gov (United States)

    Tajbakhsh, Nima; Gurudu, Suryakanth R; Liang, Jianming

    2016-02-01

    This paper presents the culmination of our research in designing a system for computer-aided detection (CAD) of polyps in colonoscopy videos. Our system is based on a hybrid context-shape approach, which utilizes context information to remove non-polyp structures and shape information to reliably localize polyps. Specifically, given a colonoscopy image, we first obtain a crude edge map. Second, we remove non-polyp edges from the edge map using our unique feature extraction and edge classification scheme. Third, we localize polyp candidates with probabilistic confidence scores in the refined edge maps using our novel voting scheme. The suggested CAD system has been tested using two public polyp databases, CVC-ColonDB, containing 300 colonoscopy images with a total of 300 polyp instances from 15 unique polyps, and ASU-Mayo database, which is our collection of colonoscopy videos containing 19,400 frames and a total of 5,200 polyp instances from 10 unique polyps. We have evaluated our system using free-response receiver operating characteristic (FROC) analysis. At 0.1 false positives per frame, our system achieves a sensitivity of 88.0% for CVC-ColonDB and a sensitivity of 48% for the ASU-Mayo database. In addition, we have evaluated our system using a new detection latency analysis where latency is defined as the time from the first appearance of a polyp in the colonoscopy video to the time of its first detection by our system. At 0.05 false positives per frame, our system yields a polyp detection latency of 0.3 seconds.

  3. Scene text detection via extremal region based double threshold convolutional network classification.

    Directory of Open Access Journals (Sweden)

    Wei Zhu

    Full Text Available In this paper, we present a robust text detection approach in natural images which is based on region proposal mechanism. A powerful low-level detector named saliency enhanced-MSER extended from the widely-used MSER is proposed by incorporating saliency detection methods, which ensures a high recall rate. Given a natural image, character candidates are extracted from three channels in a perception-based illumination invariant color space by saliency-enhanced MSER algorithm. A discriminative convolutional neural network (CNN is jointly trained with multi-level information including pixel-level and character-level information as character candidate classifier. Each image patch is classified as strong text, weak text and non-text by double threshold filtering instead of conventional one-step classification, leveraging confident scores obtained via CNN. To further prune non-text regions, we develop a recursive neighborhood search algorithm to track credible texts from weak text set. Finally, characters are grouped into text lines using heuristic features such as spatial location, size, color, and stroke width. We compare our approach with several state-of-the-art methods, and experiments show that our method achieves competitive performance on public datasets ICDAR 2011 and ICDAR 2013.

  4. Using an Electronic Text-Matching Tool (Turnitin) to Detect Plagiarism in a New Zealand University

    Science.gov (United States)

    Goddard, Robert; Rudzki, Romuald

    2005-01-01

    This paper is concerned with reporting the experience and findings of staff using a commercially-available text-matching tool (Turnitin) to detect plagiarism in a university setting in New Zealand. The use of actual instances of plagiarism revealed through Turnitin in a teaching department is a departure from the more usual self-reporting…

  5. Text mining to detect indications of fraud in annual reports worldwide

    NARCIS (Netherlands)

    Fissette, Marcia Valentine Maria

    2017-01-01

    The research described in this thesis examined the contribution of text analysis to detecting indications of fraud in the annual reports of companies worldwide. A total of 1,727 annual reports have been collected, of which 402 are of the years and companies in which fraudulent activities took place,

  6. Scene text detection via extremal region based double threshold convolutional network classification.

    Science.gov (United States)

    Zhu, Wei; Lou, Jing; Chen, Longtao; Xia, Qingyuan; Ren, Mingwu

    2017-01-01

    In this paper, we present a robust text detection approach in natural images which is based on region proposal mechanism. A powerful low-level detector named saliency enhanced-MSER extended from the widely-used MSER is proposed by incorporating saliency detection methods, which ensures a high recall rate. Given a natural image, character candidates are extracted from three channels in a perception-based illumination invariant color space by saliency-enhanced MSER algorithm. A discriminative convolutional neural network (CNN) is jointly trained with multi-level information including pixel-level and character-level information as character candidate classifier. Each image patch is classified as strong text, weak text and non-text by double threshold filtering instead of conventional one-step classification, leveraging confident scores obtained via CNN. To further prune non-text regions, we develop a recursive neighborhood search algorithm to track credible texts from weak text set. Finally, characters are grouped into text lines using heuristic features such as spatial location, size, color, and stroke width. We compare our approach with several state-of-the-art methods, and experiments show that our method achieves competitive performance on public datasets ICDAR 2011 and ICDAR 2013.

  7. A comparison study on algorithms of detecting long forms for short forms in biomedical text

    Directory of Open Access Journals (Sweden)

    Wu Cathy H

    2007-11-01

    Full Text Available Abstract Motivation With more and more research dedicated to literature mining in the biomedical domain, more and more systems are available for people to choose from when building literature mining applications. In this study, we focus on one specific kind of literature mining task, i.e., detecting definitions of acronyms, abbreviations, and symbols in biomedical text. We denote acronyms, abbreviations, and symbols as short forms (SFs and their corresponding definitions as long forms (LFs. The study was designed to answer the following questions; i how well a system performs in detecting LFs from novel text, ii what the coverage is for various terminological knowledge bases in including SFs as synonyms of their LFs, and iii how to combine results from various SF knowledge bases. Method We evaluated the following three publicly available detection systems in detecting LFs for SFs: i a handcrafted pattern/rule based system by Ao and Takagi, ALICE, ii a machine learning system by Chang et al., and iii a simple alignment-based program by Schwartz and Hearst. In addition, we investigated the conceptual coverage of two terminological knowledge bases: i the UMLS (the Unified Medical Language System, and ii the BioThesaurus (a thesaurus of names for all UniProt protein records. We also implemented a web interface that provides a virtual integration of various SF knowledge bases. Results We found that detection systems agree with each other on most cases, and the existing terminological knowledge bases have a good coverage of synonymous relationship for frequently defined LFs. The web interface allows people to detect SF definitions from text and to search several SF knowledge bases. Availability The web site is http://gauss.dbb.georgetown.edu/liblab/SFThesaurus.

  8. Automated detection of follow-up appointments using text mining of discharge records.

    Science.gov (United States)

    Ruud, Kari L; Johnson, Matthew G; Liesinger, Juliette T; Grafft, Carrie A; Naessens, James M

    2010-06-01

    To determine whether text mining can accurately detect specific follow-up appointment criteria in free-text hospital discharge records. Cross-sectional study. Mayo Clinic Rochester hospitals. Inpatients discharged from general medicine services in 2006 (n = 6481). Textual hospital dismissal summaries were manually reviewed to determine whether the records contained specific follow-up appointment arrangement elements: date, time and either physician or location for an appointment. The data set was evaluated for the same criteria using SAS Text Miner software. The two assessments were compared to determine the accuracy of text mining for detecting records containing follow-up appointment arrangements. Agreement of text-mined appointment findings with gold standard (manual abstraction) including sensitivity, specificity, positive predictive and negative predictive values (PPV and NPV). About 55.2% (3576) of discharge records contained all criteria for follow-up appointment arrangements according to the manual review, 3.2% (113) of which were missed through text mining. Text mining incorrectly identified 3.7% (107) follow-up appointments that were not considered valid through manual review. Therefore, the text mining analysis concurred with the manual review in 96.6% of the appointment findings. Overall sensitivity and specificity were 96.8 and 96.3%, respectively; and PPV and NPV were 97.0 and 96.1%, respectively. of individual appointment criteria resulted in accuracy rates of 93.5% for date, 97.4% for time, 97.5% for physician and 82.9% for location. Text mining of unstructured hospital dismissal summaries can accurately detect documentation of follow-up appointment arrangement elements, thus saving considerable resources for performance assessment and quality-related research.

  9. Affect Detection from Text-Based Virtual Improvisation and Emotional Gesture Recognition

    Directory of Open Access Journals (Sweden)

    Li Zhang

    2012-01-01

    Full Text Available We have developed an intelligent agent to engage with users in virtual drama improvisation previously. The intelligent agent was able to perform sentence-level affect detection from user inputs with strong emotional indicators. However, we noticed that many inputs with weak or no affect indicators also contain emotional implication but were regarded as neutral expressions by the previous interpretation. In this paper, we employ latent semantic analysis to perform topic theme detection and identify target audiences for such inputs. We also discuss how such semantic interpretation of the dialog contexts is used to interpret affect more appropriately during virtual improvisation. Also, in order to build a reliable affect analyser, it is important to detect and combine weak affect indicators from other channels such as body language. Such emotional body language detection also provides a nonintrusive channel to detect users’ experience without interfering with the primary task. Thus, we also make initial exploration on affect detection from several universally accepted emotional gestures.

  10. Automatically Detecting Failures in Natural Language Processing Tools for Online Community Text.

    Science.gov (United States)

    Park, Albert; Hartzler, Andrea L; Huh, Jina; McDonald, David W; Pratt, Wanda

    2015-08-31

    The prevalence and value of patient-generated health text are increasing, but processing such text remains problematic. Although existing biomedical natural language processing (NLP) tools are appealing, most were developed to process clinician- or researcher-generated text, such as clinical notes or journal articles. In addition to being constructed for different types of text, other challenges of using existing NLP include constantly changing technologies, source vocabularies, and characteristics of text. These continuously evolving challenges warrant the need for applying low-cost systematic assessment. However, the primarily accepted evaluation method in NLP, manual annotation, requires tremendous effort and time. The primary objective of this study is to explore an alternative approach-using low-cost, automated methods to detect failures (eg, incorrect boundaries, missed terms, mismapped concepts) when processing patient-generated text with existing biomedical NLP tools. We first characterize common failures that NLP tools can make in processing online community text. We then demonstrate the feasibility of our automated approach in detecting these common failures using one of the most popular biomedical NLP tools, MetaMap. Using 9657 posts from an online cancer community, we explored our automated failure detection approach in two steps: (1) to characterize the failure types, we first manually reviewed MetaMap's commonly occurring failures, grouped the inaccurate mappings into failure types, and then identified causes of the failures through iterative rounds of manual review using open coding, and (2) to automatically detect these failure types, we then explored combinations of existing NLP techniques and dictionary-based matching for each failure cause. Finally, we manually evaluated the automatically detected failures. From our manual review, we characterized three types of failure: (1) boundary failures, (2) missed term failures, and (3) word ambiguity

  11. Rheumatoid Arthritis Educational Video Series

    Medline Plus

    Full Text Available ... Rheumatoid Arthritis Educational Video Series Rheumatoid Arthritis Educational Video Series This series of five videos was designed ... Activity Role of Body Weight in Osteoarthritis Educational Videos for Patients Rheumatoid Arthritis Educational Video Series Psoriatic ...

  12. Exploring supervised and unsupervised methods to detect topics in biomedical text

    Directory of Open Access Journals (Sweden)

    Yu Hong

    2006-03-01

    Full Text Available Abstract Background Topic detection is a task that automatically identifies topics (e.g., "biochemistry" and "protein structure" in scientific articles based on information content. Topic detection will benefit many other natural language processing tasks including information retrieval, text summarization and question answering; and is a necessary step towards the building of an information system that provides an efficient way for biologists to seek information from an ocean of literature. Results We have explored the methods of Topic Spotting, a task of text categorization that applies the supervised machine-learning technique naïve Bayes to assign automatically a document into one or more predefined topics; and Topic Clustering, which apply unsupervised hierarchical clustering algorithms to aggregate documents into clusters such that each cluster represents a topic. We have applied our methods to detect topics of more than fifteen thousand of articles that represent over sixteen thousand entries in the Online Mendelian Inheritance in Man (OMIM database. We have explored bag of words as the features. Additionally, we have explored semantic features; namely, the Medical Subject Headings (MeSH that are assigned to the MEDLINE records, and the Unified Medical Language System (UMLS semantic types that correspond to the MeSH terms, in addition to bag of words, to facilitate the tasks of topic detection. Our results indicate that incorporating the MeSH terms and the UMLS semantic types as additional features enhances the performance of topic detection and the naïve Bayes has the highest accuracy, 66.4%, for predicting the topic of an OMIM article as one of the total twenty-five topics. Conclusion Our results indicate that the supervised topic spotting methods outperformed the unsupervised topic clustering; on the other hand, the unsupervised topic clustering methods have the advantages of being robust and applicable in real world settings.

  13. Who wrote the "Letter to the Hebrews"?: data mining for detection of text authorship

    Science.gov (United States)

    Sabordo, Madeleine; Chai, Shong Y.; Berryman, Matthew J.; Abbott, Derek

    2005-02-01

    This paper explores the authorship of the Letter to the Hebrews using a number of different measures of relationship between different texts of the New Testament. The methods used in the study include file zipping and compression techniques, prediction by the partial matching technique and the word recurrence interval technique. The long term motivation is that the techniques employed in this study may find applicability in future generation web search engines, email authorship identification, detection of plagiarism and terrorist email traffic filtration.

  14. Detecting causality from online psychiatric texts using inter-sentential language patterns

    Directory of Open Access Journals (Sweden)

    Wu Jheng-Long

    2012-07-01

    Full Text Available Abstract Background Online psychiatric texts are natural language texts expressing depressive problems, published by Internet users via community-based web services such as web forums, message boards and blogs. Understanding the cause-effect relations embedded in these psychiatric texts can provide insight into the authors’ problems, thus increasing the effectiveness of online psychiatric services. Methods Previous studies have proposed the use of word pairs extracted from a set of sentence pairs to identify cause-effect relations between sentences. A word pair is made up of two words, with one coming from the cause text span and the other from the effect text span. Analysis of the relationship between these words can be used to capture individual word associations between cause and effect sentences. For instance, (broke up, life and (boyfriend, meaningless are two word pairs extracted from the sentence pair: “I broke up with my boyfriend. Life is now meaningless to me”. The major limitation of word pairs is that individual words in sentences usually cannot reflect the exact meaning of the cause and effect events, and thus may produce semantically incomplete word pairs, as the previous examples show. Therefore, this study proposes the use of inter-sentential language patterns such as ≪broke up, boyfriend>, Results Performance was evaluated on a corpus of texts collected from PsychPark (http://www.psychpark.org, a virtual psychiatric clinic maintained by a group of volunteer professionals from the Taiwan Association of Mental Health Informatics. Experimental results show that the use of inter-sentential language patterns outperformed the use of word pairs proposed in previous studies. Conclusions This study demonstrates the acquisition of inter-sentential language patterns for causality detection from online psychiatric texts. Such semantically more complete and precise features can improve causality detection performance.

  15. NEI You Tube Videos: Amblyopia

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    Full Text Available ... NEI YouTube Videos > NEI YouTube Videos: Amblyopia NEI YouTube Videos YouTube Videos Home Age-Related Macular Degeneration ... Retinopathy of Prematurity Science Spanish Videos Webinars NEI YouTube Videos: Amblyopia Embedded video for NEI YouTube Videos: ...

  16. NEI You Tube Videos: Amblyopia

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    Full Text Available ... YouTube Videos > NEI YouTube Videos: Amblyopia NEI YouTube Videos YouTube Videos Home Age-Related Macular Degeneration Amblyopia ... of Prematurity Science Spanish Videos Webinars NEI YouTube Videos: Amblyopia Embedded video for NEI YouTube Videos: Amblyopia ...

  17. ΤND: a thyroid nodule detection system for analysis of ultrasound images and videos.

    Science.gov (United States)

    Keramidas, Eystratios G; Maroulis, Dimitris; Iakovidis, Dimitris K

    2012-06-01

    In this paper, we present a computer-aided-diagnosis (CAD) system prototype, named TND (Thyroid Nodule Detector), for the detection of nodular tissue in ultrasound (US) thyroid images and videos acquired during thyroid US examinations. The proposed system incorporates an original methodology that involves a novel algorithm for automatic definition of the boundaries of the thyroid gland, and a novel approach for the extraction of noise resilient image features effectively representing the textural and the echogenic properties of the thyroid tissue. Through extensive experimental evaluation on real thyroid US data, its accuracy in thyroid nodule detection has been estimated to exceed 95%. These results attest to the feasibility of the clinical application of TND, for the provision of a second more objective opinion to the radiologists by exploiting image evidences.

  18. Exploring supervised and unsupervised methods to detect topics in biomedical text.

    Science.gov (United States)

    Lee, Minsuk; Wang, Weiqing; Yu, Hong

    2006-03-16

    Topic detection is a task that automatically identifies topics (e.g., "biochemistry" and "protein structure") in scientific articles based on information content. Topic detection will benefit many other natural language processing tasks including information retrieval, text summarization and question answering; and is a necessary step towards the building of an information system that provides an efficient way for biologists to seek information from an ocean of literature. We have explored the methods of Topic Spotting, a task of text categorization that applies the supervised machine-learning technique naïve Bayes to assign automatically a document into one or more predefined topics; and Topic Clustering, which apply unsupervised hierarchical clustering algorithms to aggregate documents into clusters such that each cluster represents a topic. We have applied our methods to detect topics of more than fifteen thousand of articles that represent over sixteen thousand entries in the Online Mendelian Inheritance in Man (OMIM) database. We have explored bag of words as the features. Additionally, we have explored semantic features; namely, the Medical Subject Headings (MeSH) that are assigned to the MEDLINE records, and the Unified Medical Language System (UMLS) semantic types that correspond to the MeSH terms, in addition to bag of words, to facilitate the tasks of topic detection. Our results indicate that incorporating the MeSH terms and the UMLS semantic types as additional features enhances the performance of topic detection and the naïve Bayes has the highest accuracy, 66.4%, for predicting the topic of an OMIM article as one of the total twenty-five topics. Our results indicate that the supervised topic spotting methods outperformed the unsupervised topic clustering; on the other hand, the unsupervised topic clustering methods have the advantages of being robust and applicable in real world settings.

  19. Exploring supervised and unsupervised methods to detect topics in biomedical text

    Science.gov (United States)

    Lee, Minsuk; Wang, Weiqing; Yu, Hong

    2006-01-01

    Background Topic detection is a task that automatically identifies topics (e.g., "biochemistry" and "protein structure") in scientific articles based on information content. Topic detection will benefit many other natural language processing tasks including information retrieval, text summarization and question answering; and is a necessary step towards the building of an information system that provides an efficient way for biologists to seek information from an ocean of literature. Results We have explored the methods of Topic Spotting, a task of text categorization that applies the supervised machine-learning technique naïve Bayes to assign automatically a document into one or more predefined topics; and Topic Clustering, which apply unsupervised hierarchical clustering algorithms to aggregate documents into clusters such that each cluster represents a topic. We have applied our methods to detect topics of more than fifteen thousand of articles that represent over sixteen thousand entries in the Online Mendelian Inheritance in Man (OMIM) database. We have explored bag of words as the features. Additionally, we have explored semantic features; namely, the Medical Subject Headings (MeSH) that are assigned to the MEDLINE records, and the Unified Medical Language System (UMLS) semantic types that correspond to the MeSH terms, in addition to bag of words, to facilitate the tasks of topic detection. Our results indicate that incorporating the MeSH terms and the UMLS semantic types as additional features enhances the performance of topic detection and the naïve Bayes has the highest accuracy, 66.4%, for predicting the topic of an OMIM article as one of the total twenty-five topics. Conclusion Our results indicate that the supervised topic spotting methods outperformed the unsupervised topic clustering; on the other hand, the unsupervised topic clustering methods have the advantages of being robust and applicable in real world settings. PMID:16539745

  20. Gaussian Process Regression-Based Video Anomaly Detection and Localization With Hierarchical Feature Representation.

    Science.gov (United States)

    Cheng, Kai-Wen; Chen, Yie-Tarng; Fang, Wen-Hsien

    2015-12-01

    This paper presents a hierarchical framework for detecting local and global anomalies via hierarchical feature representation and Gaussian process regression (GPR) which is fully non-parametric and robust to the noisy training data, and supports sparse features. While most research on anomaly detection has focused more on detecting local anomalies, we are more interested in global anomalies that involve multiple normal events interacting in an unusual manner, such as car accidents. To simultaneously detect local and global anomalies, we cast the extraction of normal interactions from the training videos as a problem of finding the frequent geometric relations of the nearby sparse spatio-temporal interest points (STIPs). A codebook of interaction templates is then constructed and modeled using the GPR, based on which a novel inference method for computing the likelihood of an observed interaction is also developed. Thereafter, these local likelihood scores are integrated into globally consistent anomaly masks, from which anomalies can be succinctly identified. To the best of our knowledge, it is the first time GPR is employed to model the relationship of the nearby STIPs for anomaly detection. Simulations based on four widespread datasets show that the new method outperforms the main state-of-the-art methods with lower computational burden.

  1. Detecting causality from online psychiatric texts using inter-sentential language patterns

    Science.gov (United States)

    2012-01-01

    Background Online psychiatric texts are natural language texts expressing depressive problems, published by Internet users via community-based web services such as web forums, message boards and blogs. Understanding the cause-effect relations embedded in these psychiatric texts can provide insight into the authors’ problems, thus increasing the effectiveness of online psychiatric services. Methods Previous studies have proposed the use of word pairs extracted from a set of sentence pairs to identify cause-effect relations between sentences. A word pair is made up of two words, with one coming from the cause text span and the other from the effect text span. Analysis of the relationship between these words can be used to capture individual word associations between cause and effect sentences. For instance, (broke up, life) and (boyfriend, meaningless) are two word pairs extracted from the sentence pair: “I broke up with my boyfriend. Life is now meaningless to me”. The major limitation of word pairs is that individual words in sentences usually cannot reflect the exact meaning of the cause and effect events, and thus may produce semantically incomplete word pairs, as the previous examples show. Therefore, this study proposes the use of inter-sentential language patterns such as ≪broke up, boyfriend>, Mental Health Informatics. Experimental results show that the use of inter-sentential language patterns outperformed the use of word pairs proposed in previous studies. Conclusions This study demonstrates the acquisition of inter-sentential language patterns for causality detection from online psychiatric texts. Such semantically more complete and precise features can improve causality detection performance. PMID:22809317

  2. Advances in automated deception detection in text-based computer-mediated communication

    Science.gov (United States)

    Adkins, Mark; Twitchell, Douglas P.; Burgoon, Judee K.; Nunamaker, Jay F., Jr.

    2004-08-01

    The Internet has provided criminals, terrorists, spies, and other threats to national security a means of communication. At the same time it also provides for the possibility of detecting and tracking their deceptive communication. Recent advances in natural language processing, machine learning and deception research have created an environment where automated and semi-automated deception detection of text-based computer-mediated communication (CMC, e.g. email, chat, instant messaging) is a reachable goal. This paper reviews two methods for discriminating between deceptive and non-deceptive messages in CMC. First, Document Feature Mining uses document features or cues in CMC messages combined with machine learning techniques to classify messages according to their deceptive potential. The method, which is most useful in asynchronous applications, also allows for the visualization of potential deception cues in CMC messages. Second, Speech Act Profiling, a method for quantifying and visualizing synchronous CMC, has shown promise in aiding deception detection. The methods may be combined and are intended to be a part of a suite of tools for automating deception detection.

  3. Affective video retrieval: violence detection in Hollywood movies by large-scale segmental feature extraction.

    Science.gov (United States)

    Eyben, Florian; Weninger, Felix; Lehment, Nicolas; Schuller, Björn; Rigoll, Gerhard

    2013-01-01

    Without doubt general video and sound, as found in large multimedia archives, carry emotional information. Thus, audio and video retrieval by certain emotional categories or dimensions could play a central role for tomorrow's intelligent systems, enabling search for movies with a particular mood, computer aided scene and sound design in order to elicit certain emotions in the audience, etc. Yet, the lion's share of research in affective computing is exclusively focusing on signals conveyed by humans, such as affective speech. Uniting the fields of multimedia retrieval and affective computing is believed to lend to a multiplicity of interesting retrieval applications, and at the same time to benefit affective computing research, by moving its methodology "out of the lab" to real-world, diverse data. In this contribution, we address the problem of finding "disturbing" scenes in movies, a scenario that is highly relevant for computer-aided parental guidance. We apply large-scale segmental feature extraction combined with audio-visual classification to the particular task of detecting violence. Our system performs fully data-driven analysis including automatic segmentation. We evaluate the system in terms of mean average precision (MAP) on the official data set of the MediaEval 2012 evaluation campaign's Affect Task, which consists of 18 original Hollywood movies, achieving up to .398 MAP on unseen test data in full realism. An in-depth analysis of the worth of individual features with respect to the target class and the system errors is carried out and reveals the importance of peak-related audio feature extraction and low-level histogram-based video analysis.

  4. Endoscopic trimodal imaging detects colonic neoplasia as well as standard video endoscopy.

    Science.gov (United States)

    Kuiper, Teaco; van den Broek, Frank J C; Naber, Anton H; van Soest, Ellert J; Scholten, Pieter; Mallant-Hent, Rosalie Ch; van den Brande, Jan; Jansen, Jeroen M; van Oijen, Arnoud H A M; Marsman, Willem A; Bergman, Jacques J G H M; Fockens, Paul; Dekker, Evelien

    2011-06-01

    Endoscopic trimodal imaging (ETMI) is a novel endoscopic technique that combines high-resolution endoscopy (HRE), autofluorescence imaging (AFI), and narrow-band imaging (NBI) that has only been studied in academic settings. We performed a randomized, controlled trial in a nonacademic setting to compare ETMI with standard video endoscopy (SVE) in the detection and differentiation of colorectal lesions. The study included 234 patients scheduled to receive colonoscopy who were randomly assigned to undergo a colonoscopy in tandem with either ETMI or SVE. In the ETMI group (n=118), first examination was performed using HRE, followed by AFI. In the other group, both examinations were performed using SVE (n=116). In the ETMI group, detected lesions were differentiated using AFI and NBI. In the ETMI group, 87 adenomas were detected in the first examination (with HRE), and then 34 adenomas were detected during second inspection (with AFI). In the SVE group, 79 adenomas were detected during the first inspection, and then 33 adenomas were detected during the second inspection. Adenoma detection rates did not differ significantly between the 2 groups (ETMI: 1.03 vs SVE: 0.97, P=.360). The adenoma miss-rate was 29% for HRE and 28% for SVE. The sensitivity, specificity, and accuracy of NBI in differentiating adenomas from nonadenomatous lesions were 87%, 63%, and 75%, respectively; corresponding values for AFI were 90%, 37%, and 62%, respectively. In a nonacademic setting, ETMI did not improve the detection rate for adenomas compared with SVE. NBI and AFI each differentiated colonic lesions with high levels of sensitivity but low levels of specificity. Copyright © 2011 AGA Institute. Published by Elsevier Inc. All rights reserved.

  5. Multiorientation/multiscript scene text detection based on projection profile analysis and graph segmentation

    Science.gov (United States)

    Koo, Hyung Il

    2016-11-01

    Textline detection in natural images has been an important problem and researchers have attempted to address this problem by grouping connected components (CCs) into clusters corresponding to textlines. However, developing bottom-up rules that work for multiorientation and/or multiscript textlines is not a simple task. In order to address this problem, we propose a framework that incorporates projection profile analysis (PPA) into the CC-based approach. Specifically, we build a graph of CCs and recursively partition the graph into subgraphs, until textline structures are detected by PPA. Although PPA has been a common technique in document image processing, it was developed for scanned documents, and we also propose a method to compute projection profiles for CCs. Experimental results show that our method is efficient and achieves better or comparable performance on conventional datasets (ICDAR 2011/2013 and MSRA-TD500), and shows promising results on a challenging dataset (ICDAR 2015 incidental text localization dataset).

  6. PLAGIARISM DETECTION IN TEXT DOCUMENTS USING SENTENCE BOUNDED STOP WORD N-GRAMS

    Directory of Open Access Journals (Sweden)

    DEEPA GUPTA

    2016-10-01

    Full Text Available With the evolution of technologies like internet search engines and improved text editors, plagiarism has become a critical issue. Many works are already available in verbatim plagiarism detection which is a type of simple copy and paste plagiarism but when it comes to intelligent plagiarism the scenario becomes more complex. Intelligent plagiarism includes plagiarism through idea adoption, translation and text manipulations which is more challenging to deal with. The paper makes an attempt to detect intelligent plagiarism using the structural information within the document. This is done by the extraction of stop words, in contrast to the other methods that usually rely upon content words. The proposed method enhances this existing idea by including the rough sentence boundaries along with stop word profiles. Further this method is extended using the part of speech tags and finally the system is evaluated using sample documents from PAN- 2010 data set. The results are compared with the baseline approach and performance is evaluated based on standard PAN measures.

  7. A Multi-view Approach for Detecting Non-Cooperative Users in Online Video Sharing Systems

    OpenAIRE

    Langbehn, Hendrickson Reiter; Ricci, Saulo M. R.; Gonçalves, Marcos A.; Almeida, Jussara Marques; Pappa, Gisele Lobo; Benevenuto, Fabrício

    2010-01-01

    Most online video sharing systems (OVSSs), such as YouTube and Yahoo! Video, have several mechanisms for supporting interactions among users. One such mechanism is the  video response feature in YouTube, which allows a user to post a video in response to another video. While increasingly popular, the video response feature opens the opportunity for non-cooperative users to  introduce  ``content pollution'' into the system, thus causing loss of service effectiveness and credibility as w...

  8. Video Pulses: User-Based Modeling of Interesting Video Segments

    Directory of Open Access Journals (Sweden)

    Markos Avlonitis

    2014-01-01

    Full Text Available We present a user-based method that detects regions of interest within a video in order to provide video skims and video summaries. Previous research in video retrieval has focused on content-based techniques, such as pattern recognition algorithms that attempt to understand the low-level features of a video. We are proposing a pulse modeling method, which makes sense of a web video by analyzing users' Replay interactions with the video player. In particular, we have modeled the user information seeking behavior as a time series and the semantic regions as a discrete pulse of fixed width. Then, we have calculated the correlation coefficient between the dynamically detected pulses at the local maximums of the user activity signal and the pulse of reference. We have found that users' Replay activity significantly matches the important segments in information-rich and visually complex videos, such as lecture, how-to, and documentary. The proposed signal processing of user activity is complementary to previous work in content-based video retrieval and provides an additional user-based dimension for modeling the semantics of a social video on the web.

  9. Investigation on effectiveness of mid-level feature representation for semantic boundary detection in news video

    Science.gov (United States)

    Radhakrishan, Regunathan; Xiong, Ziyou; Divakaran, Ajay; Raj, Bhiksha

    2003-11-01

    In our past work, we have attempted to use a mid-level feature namely the state population histogram obtained from the Hidden Markov Model (HMM) of a general sound class, for speaker change detection so as to extract semantic boundaries in broadcast news. In this paper, we compare the performance of our previous approach with another approach based on video shot detection and speaker change detection using the Bayesian Information Criterion (BIC). Our experiments show that the latter approach performs significantly better than the former. This motivated us to examine the mid-level feature closely. We found that the component population histogram enabled discovery of broad phonetic categories such as vowels, nasals, fricatives etc, regardless of the number of distinct speakers in the test utterance. In order for it to be useful for speaker change detection, the individual components should model the phonetic sounds of each speaker separately. From our experiments, we conclude that state/component population histograms can only be useful for further clustering or semantic class discovery if the features are chosen carefully so that the individual states represent the semantic categories of interest.

  10. Portable automatic text classification for adverse drug reaction detection via multi-corpus training.

    Science.gov (United States)

    Sarker, Abeed; Gonzalez, Graciela

    2015-02-01

    Automatic detection of adverse drug reaction (ADR) mentions from text has recently received significant interest in pharmacovigilance research. Current research focuses on various sources of text-based information, including social media-where enormous amounts of user posted data is available, which have the potential for use in pharmacovigilance if collected and filtered accurately. The aims of this study are: (i) to explore natural language processing (NLP) approaches for generating useful features from text, and utilizing them in optimized machine learning algorithms for automatic classification of ADR assertive text segments; (ii) to present two data sets that we prepared for the task of ADR detection from user posted internet data; and (iii) to investigate if combining training data from distinct corpora can improve automatic classification accuracies. One of our three data sets contains annotated sentences from clinical reports, and the two other data sets, built in-house, consist of annotated posts from social media. Our text classification approach relies on generating a large set of features, representing semantic properties (e.g., sentiment, polarity, and topic), from short text nuggets. Importantly, using our expanded feature sets, we combine training data from different corpora in attempts to boost classification accuracies. Our feature-rich classification approach performs significantly better than previously published approaches with ADR class F-scores of 0.812 (previously reported best: 0.770), 0.538 and 0.678 for the three data sets. Combining training data from multiple compatible corpora further improves the ADR F-scores for the in-house data sets to 0.597 (improvement of 5.9 units) and 0.704 (improvement of 2.6 units) respectively. Our research results indicate that using advanced NLP techniques for generating information rich features from text can significantly improve classification accuracies over existing benchmarks. Our experiments

  11. Unsupervised video-based lane detection using location-enhanced topic models

    Science.gov (United States)

    Sun, Hao; Wang, Cheng; Wang, Boliang; El-Sheimy, Naser

    2010-10-01

    An unsupervised learning algorithm based on topic models is presented for lane detection in video sequences observed by uncalibrated moving cameras. Our contributions are twofold. First, we introduce the maximally stable extremal region (MSER) detector for lane-marking feature extraction and derive a novel shape descriptor in an affine invariant manner to describe region shapes and a modified scale-invariant feature transform descriptor to capture feature appearance characteristics. MSER features are more stable compared to edge points or line pairs and hence provide robustness to lane-marking variations in scale, lighting, viewpoint, and shadows. Second, we proposed a novel location-enhanced probabilistic latent semantic analysis (pLSA) topic model for simultaneous lane recognition and localization. The proposed model overcomes the limitation of a pLSA model for effective topic localization. Experimental results on traffic sequences in various scenarios demonstrate the effectiveness and robustness of the proposed method.

  12. Detecting New Words from Chinese Text Using Latent Semi-CRF Models

    Science.gov (United States)

    Sun, Xiao; Huang, Degen; Ren, Fuji

    Chinese new words and their part-of-speech (POS) are particularly problematic in Chinese natural language processing. With the fast development of internet and information technology, it is impossible to get a complete system dictionary for Chinese natural language processing, as new words out of the basic system dictionary are always being created. A latent semi-CRF model, which combines the strengths of LDCRF (Latent-Dynamic Conditional Random Field) and semi-CRF, is proposed to detect the new words together with their POS synchronously regardless of the types of the new words from the Chinese text without being pre-segmented. Unlike the original semi-CRF, the LDCRF is applied to generate the candidate entities for training and testing the latent semi-CRF, which accelerates the training speed and decreases the computation cost. The complexity of the latent semi-CRF could be further adjusted by tuning the number of hidden variables in LDCRF and the number of the candidate entities from the Nbest outputs of the LDCRF. A new-words-generating framework is proposed for model training and testing, under which the definitions and distributions of the new words conform to the ones existing in real text. Specific features called “Global Fragment Information” for new word detection and POS tagging are adopted in the model training and testing. The experimental results show that the proposed method is capable of detecting even low frequency new words together with their POS tags. The proposed model is found to be performing competitively with the state-of-the-art models presented.

  13. From university research to innovation: Detecting knowledge transfer via text mining

    Energy Technology Data Exchange (ETDEWEB)

    Woltmann, S.; Clemmensen, L.; Alkærsig, L

    2016-07-01

    Knowledge transfer by universities is a top priority in innovation policy and a primary purpose for public research funding, due to being an important driver of technical change and innovation. Current empirical research on the impact of university research relies mainly on formal databases and indicators such as patents, collaborative publications and license agreements, to assess the contribution to the socioeconomic surrounding of universities. In this study, we present an extension of the current empirical framework by applying new computational methods, namely text mining and pattern recognition. Text samples for this purpose can include files containing social media contents, company websites and annual reports. The empirical focus in the present study is on the technical sciences and in particular on the case of the Technical University of Denmark (DTU). We generated two independent text collections (corpora) to identify correlations of university publications and company webpages. One corpus representing the company sites, serving as sample of the private economy and a second corpus, providing the reference to the university research, containing relevant publications. We associated the former with the latter to obtain insights into possible text and semantic relatedness. The text mining methods are extrapolating the correlations, semantic patterns and content comparison of the two corpora to define the document relatedness. We expect the development of a novel tool using contemporary techniques for the measurement of public research impact. The approach aims to be applicable across universities and thus enable a more holistic comparable assessment. This rely less on formal databases, which is certainly beneficial in terms of the data reliability. We seek to provide a supplementary perspective for the detection of the dissemination of university research and hereby enable policy makers to gain additional insights of (informal) contributions of knowledge

  14. A Review on Video/Image Authentication and Tamper Detection Techniques

    Science.gov (United States)

    Parmar, Zarna; Upadhyay, Saurabh

    2013-02-01

    With the innovations and development in sophisticated video editing technology and a wide spread of video information and services in our society, it is becoming increasingly significant to assure the trustworthiness of video information. Therefore in surveillance, medical and various other fields, video contents must be protected against attempt to manipulate them. Such malicious alterations could affect the decisions based on these videos. A lot of techniques are proposed by various researchers in the literature that assure the authenticity of video information in their own way. In this paper we present a brief survey on video authentication techniques with their classification. These authentication techniques are generally classified into following categories: digital signature based techniques, watermark based techniques, and other authentication techniques.

  15. An Automatic Gastrointestinal Polyp Detection System in Video Endoscopy Using Fusion of Color Wavelet and Convolutional Neural Network Features.

    Science.gov (United States)

    Billah, Mustain; Waheed, Sajjad; Rahman, Mohammad Motiur

    2017-01-01

    Gastrointestinal polyps are considered to be the precursors of cancer development in most of the cases. Therefore, early detection and removal of polyps can reduce the possibility of cancer. Video endoscopy is the most used diagnostic modality for gastrointestinal polyps. But, because it is an operator dependent procedure, several human factors can lead to misdetection of polyps. Computer aided polyp detection can reduce polyp miss detection rate and assists doctors in finding the most important regions to pay attention to. In this paper, an automatic system has been proposed as a support to gastrointestinal polyp detection. This system captures the video streams from endoscopic video and, in the output, it shows the identified polyps. Color wavelet (CW) features and convolutional neural network (CNN) features of video frames are extracted and combined together which are used to train a linear support vector machine (SVM). Evaluations on standard public databases show that the proposed system outperforms the state-of-the-art methods, gaining accuracy of 98.65%, sensitivity of 98.79%, and specificity of 98.52%.

  16. An Automatic Gastrointestinal Polyp Detection System in Video Endoscopy Using Fusion of Color Wavelet and Convolutional Neural Network Features

    Science.gov (United States)

    Waheed, Sajjad; Rahman, Mohammad Motiur

    2017-01-01

    Gastrointestinal polyps are considered to be the precursors of cancer development in most of the cases. Therefore, early detection and removal of polyps can reduce the possibility of cancer. Video endoscopy is the most used diagnostic modality for gastrointestinal polyps. But, because it is an operator dependent procedure, several human factors can lead to misdetection of polyps. Computer aided polyp detection can reduce polyp miss detection rate and assists doctors in finding the most important regions to pay attention to. In this paper, an automatic system has been proposed as a support to gastrointestinal polyp detection. This system captures the video streams from endoscopic video and, in the output, it shows the identified polyps. Color wavelet (CW) features and convolutional neural network (CNN) features of video frames are extracted and combined together which are used to train a linear support vector machine (SVM). Evaluations on standard public databases show that the proposed system outperforms the state-of-the-art methods, gaining accuracy of 98.65%, sensitivity of 98.79%, and specificity of 98.52%. PMID:28894460

  17. Semi-automated detection of fractional shortening in zebrafish embryo heart videos

    Directory of Open Access Journals (Sweden)

    Nasrat Sara

    2016-09-01

    Full Text Available Quantifying cardiac functions in model organisms like embryonic zebrafish is of high importance in small molecule screens for new therapeutic compounds. One relevant cardiac parameter is the fractional shortening (FS. A method for semi-automatic quantification of FS in video recordings of zebrafish embryo hearts is presented. The software provides automated visual information about the end-systolic and end-diastolic stages of the heart by displaying corresponding colored lines into a Motion-mode display. After manually marking the ventricle diameters in frames of end-systolic and end-diastolic stages, the FS is calculated. The software was evaluated by comparing the results of the determination of FS with results obtained from another established method. Correlations of 0.96 < r < 0.99 between the two methods were found indicating that the new software provides comparable results for the determination of the FS.

  18. DEEPEN: A negation detection system for clinical text incorporating dependency relation into NegEx.

    Science.gov (United States)

    Mehrabi, Saeed; Krishnan, Anand; Sohn, Sunghwan; Roch, Alexandra M; Schmidt, Heidi; Kesterson, Joe; Beesley, Chris; Dexter, Paul; Max Schmidt, C; Liu, Hongfang; Palakal, Mathew

    2015-04-01

    In Electronic Health Records (EHRs), much of valuable information regarding patients' conditions is embedded in free text format. Natural language processing (NLP) techniques have been developed to extract clinical information from free text. One challenge faced in clinical NLP is that the meaning of clinical entities is heavily affected by modifiers such as negation. A negation detection algorithm, NegEx, applies a simplistic approach that has been shown to be powerful in clinical NLP. However, due to the failure to consider the contextual relationship between words within a sentence, NegEx fails to correctly capture the negation status of concepts in complex sentences. Incorrect negation assignment could cause inaccurate diagnosis of patients' condition or contaminated study cohorts. We developed a negation algorithm called DEEPEN to decrease NegEx's false positives by taking into account the dependency relationship between negation words and concepts within a sentence using Stanford dependency parser. The system was developed and tested using EHR data from Indiana University (IU) and it was further evaluated on Mayo Clinic dataset to assess its generalizability. The evaluation results demonstrate DEEPEN, which incorporates dependency parsing into NegEx, can reduce the number of incorrect negation assignment for patients with positive findings, and therefore improve the identification of patients with the target clinical findings in EHRs. Copyright © 2015 Elsevier Inc. All rights reserved.

  19. Real-Time Straight-Line Detection for XGA-Size Videos by Hough Transform with Parallelized Voting Procedures

    Directory of Open Access Journals (Sweden)

    Jungang Guan

    2017-01-01

    Full Text Available The Hough Transform (HT is a method for extracting straight lines from an edge image. The main limitations of the HT for usage in actual applications are computation time and storage requirements. This paper reports a hardware architecture for HT implementation on a Field Programmable Gate Array (FPGA with parallelized voting procedure. The 2-dimensional accumulator array, namely the Hough space in parametric form (ρ, θ, for computing the strength of each line by a voting mechanism is mapped on a 1-dimensional array with regular increments of θ. Then, this Hough space is divided into a number of parallel parts. The computation of (ρ, θ for the edge pixels and the voting procedure for straight-line determination are therefore executable in parallel. In addition, a synchronized initialization for the Hough space further increases the speed of straight-line detection, so that XGA video processing becomes possible. The designed prototype system has been synthesized on a DE4 platform with a Stratix-IV FPGA device. In the application of road-lane detection, the average processing speed of this HT implementation is 5.4ms per XGA-frame at 200 MHz working frequency.

  20. Face Recognition and Tracking in Videos

    Directory of Open Access Journals (Sweden)

    Swapnil Vitthal Tathe

    2017-07-01

    Full Text Available Advancement in computer vision technology and availability of video capturing devices such as surveillance cameras has evoked new video processing applications. The research in video face recognition is mostly biased towards law enforcement applications. Applications involves human recognition based on face and iris, human computer interaction, behavior analysis, video surveillance etc. This paper presents face tracking framework that is capable of face detection using Haar features, recognition using Gabor feature extraction, matching using correlation score and tracking using Kalman filter. The method has good recognition rate for real-life videos and robust performance to changes due to illumination, environmental factors, scale, pose and orientations.

  1. Filtering Video Noise as Audio with Motion Detection to Form a Musical Instrument

    OpenAIRE

    Thomé, Carl

    2016-01-01

    Even though they differ in the physical domain, digital video and audio share many characteristics. Both are temporal data streams often stored in buffers with 8-bit values. This paper investigates a method for creating harmonic sounds with a video signal as input. A musical instrument is proposed, that utilizes video in both a sound synthesis method, and in a controller interface for selecting musical notes at specific velocities. The resulting instrument was informally determined by the aut...

  2. Pilot study on real-time motion detection in UAS video data by human observer and image exploitation algorithm

    Science.gov (United States)

    Hild, Jutta; Krüger, Wolfgang; Brüstle, Stefan; Trantelle, Patrick; Unmüßig, Gabriel; Voit, Michael; Heinze, Norbert; Peinsipp-Byma, Elisabeth; Beyerer, Jürgen

    2017-05-01

    Real-time motion video analysis is a challenging and exhausting task for the human observer, particularly in safety and security critical domains. Hence, customized video analysis systems providing functions for the analysis of subtasks like motion detection or target tracking are welcome. While such automated algorithms relieve the human operators from performing basic subtasks, they impose additional interaction duties on them. Prior work shows that, e.g., for interaction with target tracking algorithms, a gaze-enhanced user interface is beneficial. In this contribution, we present an investigation on interaction with an independent motion detection (IDM) algorithm. Besides identifying an appropriate interaction technique for the user interface - again, we compare gaze-based and traditional mouse-based interaction - we focus on the benefit an IDM algorithm might provide for an UAS video analyst. In a pilot study, we exposed ten subjects to the task of moving target detection in UAS video data twice, once performing with automatic support, once performing without it. We compare the two conditions considering performance in terms of effectiveness (correct target selections). Additionally, we report perceived workload (measured using the NASA-TLX questionnaire) and user satisfaction (measured using the ISO 9241-411 questionnaire). The results show that a combination of gaze input and automated IDM algorithm provides valuable support for the human observer, increasing the number of correct target selections up to 62% and reducing workload at the same time.

  3. Motion-based video monitoring for early detection of livestock diseases: The case of African swine fever.

    Science.gov (United States)

    Fernández-Carrión, Eduardo; Martínez-Avilés, Marta; Ivorra, Benjamin; Martínez-López, Beatriz; Ramos, Ángel Manuel; Sánchez-Vizcaíno, José Manuel

    2017-01-01

    Early detection of infectious diseases can substantially reduce the health and economic impacts on livestock production. Here we describe a system for monitoring animal activity based on video and data processing techniques, in order to detect slowdown and weakening due to infection with African swine fever (ASF), one of the most significant threats to the pig industry. The system classifies and quantifies motion-based animal behaviour and daily activity in video sequences, allowing automated and non-intrusive surveillance in real-time. The aim of this system is to evaluate significant changes in animals' motion after being experimentally infected with ASF virus. Indeed, pig mobility declined progressively and fell significantly below pre-infection levels starting at four days after infection at a confidence level of 95%. Furthermore, daily motion decreased in infected animals by approximately 10% before the detection of the disease by clinical signs. These results show the promise of video processing techniques for real-time early detection of livestock infectious diseases.

  4. Occam's approach to video critical behavior detection: a practical real time video in-vehicle alertness monitor.

    Science.gov (United States)

    Steffin, Morris; Wahl, Keith

    2004-01-01

    Driver and pilot fatigue and incapacitation are major causes of injuries and equipment loss. A method is proposed for constant in-vehicle monitoring of alertness, including detection of drowsiness and incapacitation. Novel features of this method include increases in efficiency and specificity that allow real time monitoring in the functional environment by practicable and affordable hardware. The described approach should result in a generally deployable system with acceptable sensitivity and specificity and with capability for operator alarms and automated vehicle intervention to prevent injuries caused by reduced levels of operator performance.

  5. Do participants' preferences for mode of delivery (text, video, or both) influence the effectiveness of a Web-based physical activity intervention?

    Science.gov (United States)

    Vandelanotte, Corneel; Duncan, Mitch J; Plotnikoff, Ronald C; Mummery, W Kerry

    2012-02-29

    In randomized controlled trials, participants cannot choose their preferred intervention delivery mode and thus might refuse to participate or not engage fully if assigned to a nonpreferred group. This might underestimate the true effectiveness of behavior-change interventions. To examine whether receiving interventions either matched or mismatched with participants' preferred delivery mode would influence effectiveness of a Web-based physical activity intervention. Adults (n = 863), recruited via email, were randomly assigned to one of three intervention delivery modes (text based, video based, or combined) and received fully automated, Internet-delivered personal advice about physical activity. Personalized intervention content, based on the theory of planned behavior and stages of change concept, was identical across groups. Online, self-assessed questionnaires measuring physical activity were completed at baseline, 1 week, and 1 month. Physical activity advice acceptability and website usability were assessed at 1 week. Before randomization, participants were asked which delivery mode they preferred, to categorize them as matched or mismatched. Time spent on the website was measured throughout the intervention. We applied intention-to-treat, repeated-measures analyses of covariance to assess group differences. Attrition was high (575/863, 66.6%), though equal between groups (t(86) (3) =1.31, P =.19). At 1-month follow-up, 93 participants were categorized as matched and 195 as mismatched. They preferred text mode (493/803, 61.4%) over combined (216/803, 26.9%) and video modes (94/803, 11.7%). After the intervention, 20% (26/132) of matched-group participants and 34% (96/282) in the mismatched group changed their delivery mode preference. Time effects were significant for all physical activity outcomes (total physical activity: F(2,801) = 5.07, P = .009; number of activity sessions: F(2,801) = 7.52, P < .001; walking: F(2,801) = 8.32, P < .001; moderate physical

  6. Detecting and Analyzing Cybercrime in Text-Based Communication of Cybercriminal Networks through Computational Linguistic and Psycholinguistic Feature Modeling

    Science.gov (United States)

    Mbaziira, Alex Vincent

    2017-01-01

    Cybercriminals are increasingly using Internet-based text messaging applications to exploit their victims. Incidents of deceptive cybercrime in text-based communication are increasing and include fraud, scams, as well as favorable and unfavorable fake reviews. In this work, we use a text-based deception detection approach to train models for…

  7. Affective video retrieval: violence detection in Hollywood movies by large-scale segmental feature extraction

    National Research Council Canada - National Science Library

    Eyben, Florian; Weninger, Felix; Lehment, Nicolas; Schuller, Björn; Rigoll, Gerhard

    2013-01-01

    .... Thus, audio and video retrieval by certain emotional categories or dimensions could play a central role for tomorrow's intelligent systems, enabling search for movies with a particular mood, computer...

  8. Detection of isolated covert saccades with the video head impulse test in peripheral vestibular disorders.

    Science.gov (United States)

    Blödow, Alexander; Pannasch, Sebastian; Walther, Leif Erik

    2013-08-01

    The function of the semicircular canal receptors and the pathway of the vestibulo-ocular-reflex (VOR) can be diagnosed with the clinical head impulse test (cHIT). Recently, the video head impulse test (vHIT) has been introduced but so far there is little clinical experience with the vHIT in patients with peripheral vestibular disorders. The aim of the study was to investigate the horizontal VOR (hVOR) by means of vHIT in peripheral vestibular disorders. Using the vHIT, we examined the hVOR in a group of 117 patients and a control group of 20 healthy subjects. The group of patients included vestibular neuritis (VN) (n=52), vestibular schwannoma (VS) (n=31), Ménière's disease (MD) (n=22) and bilateral vestibulopathy (BV) (n=12). Normal hVOR gain was at 0.96 ± 0.08, while abnormal hVOR gain was at 0.44 ± 0.20 (79.1% of all cases). An abnormal vHIT was found in VN (94.2%), VS (61.3%), MD (54.5%) and BV (91.7%). Three conditions of refixation saccades occurred frequently in cases with abnormal hVOR: isolated covert saccades (13.7%), isolated overt saccades (34.3%) and the combination of overt and covert saccades (52.0%). The vHIT detects abnormal hVOR changes in the combination of gain assessment and refixation saccades. Since isolated covert saccades in hVOR changes can only be seen with vHIT, peripheral vestibular disorders are likely to be diagnosed incorrectly with the cHIT to a certain amount. Copyright © 2012 Elsevier Ireland Ltd. All rights reserved.

  9. Financial Investigations. A Financial Approach to Detecting and Resolving Crimes. [Text], Instructor's Guide, and Student Workbook.

    Science.gov (United States)

    Internal Revenue Service (Dept. of Treasury), Washington, DC.

    This packet contains a textbook, an instructor's guide, and a student workbook for a course on conducting financial investigations to detect and solve crimes. The topics covered in the 11 chapters of the textbook and the ancillaries are the following: (1) why financial investigation?; (2) laws related to financial crimes; (3) evidence; (4) sources…

  10. PICO element detection in medical text without metadata: are first sentences enough?

    Science.gov (United States)

    Huang, Ke-Chun; Chiang, I-Jen; Xiao, Furen; Liao, Chun-Chih; Liu, Charles Chih-Ho; Wong, Jau-Min

    2013-10-01

    Efficient identification of patient, intervention, comparison, and outcome (PICO) components in medical articles is helpful in evidence-based medicine. The purpose of this study is to clarify whether first sentences of these components are good enough to train naive Bayes classifiers for sentence-level PICO element detection. We extracted 19,854 structured abstracts of randomized controlled trials with any P/I/O label from PubMed for naive Bayes classifiers training. Performances of classifiers trained by first sentences of each section (CF) and those trained by all sentences (CA) were compared using all sentences by ten-fold cross-validation. The results measured by recall, precision, and F-measures show that there are no significant differences in performance between CF and CA for detection of O-element (F-measure=0.731±0.009 vs. 0.738±0.010, p=0.123). However, CA perform better for I-elements, in terms of recall (0.752±0.012 vs. 0.620±0.007, pPICO element detection. Their performance varies in detecting different elements. Copyright © 2013 Elsevier Inc. All rights reserved.

  11. Text Detection from Natural Scene Images : Towards a System for Visually Impaired Persons

    NARCIS (Netherlands)

    Ezaki, Nobuo; Bulacu, Marius; Schomaker, Lambert

    2004-01-01

    We propose a system that reads the text encountered in natural scenes with the aim to provide assistance to the visually impaired persons. This paper describes the sys- tem design and evaluates several character extraction meth- ods. Automatic text recognition from natural images re- ceives a

  12. Detection of Explanation Obstacles in Scientific Texts: The Effect of an Understanding Task vs. an Experiment Task

    Science.gov (United States)

    Morgado, Júlia; Otero, José; Vaz-Rebelo, Piedade; Sanjosé, Vicente; Caldeira, Helena

    2014-01-01

    The aim of this study is to analyse the effect of tasks on the detection of explanation obstacles when secondary school students read scientific texts. Students were instructed to read short passages under different task conditions, and to ask questions if necessary. Obstacle detection was operationalised in terms of the type of questions asked by…

  13. DETECTION OF TEXT OBJECTS IN IMAGES OF REAL SCENES BASED ON CONVOLUTIONAL NEURAL NETWORK MODEL

    Directory of Open Access Journals (Sweden)

    N. N. Kuzmitsky

    2015-01-01

    Full Text Available A model of text image detector based on a convolutional neural network architecture is presented, capable of synthesizing high-level features of images in the «black box» mode. An implementation of the detector application, based on algorithms of multi-scale scanning and local responses interpretation is described, allowing to find out text samples on images of real scenes. Advantages in comparison with analogs are shown and efficiency evaluation on an example of a known database is conducted.

  14. Conversation Thread Extraction and Topic Detection in Text-Based Chat

    National Research Council Canada - National Science Library

    Adams, Paige H

    2008-01-01

    Text-based chat systems are widely used within the Department of Defense, but the standard systems available do not provide robust capabilities for search, information retrieval, or information assurance...

  15. From university research to innovation Detecting knowledge transfer via text mining

    DEFF Research Database (Denmark)

    Woltmann, Sabrina; Clemmensen, Line Katrine Harder; Alkærsig, Lars

    2016-01-01

    and indicators such as patents, collaborative publications and license agreements, to assess the contribution to the socioeconomic surrounding of universities. In this study, we present an extension of the current empirical framework by applying new computational methods, namely text mining and pattern...... associated the former with the latter to obtain insights into possible text and semantic relatedness. The text mining methods are extrapolating the correlations, semantic patterns and content comparison of the two corpora to define the document relatedness. We expect the development of a novel tool using...... recognition. Text samples for this purpose can include files containing social media contents, company websites and annual reports. The empirical focus in the present study is on the technical sciences and in particular on the case of the Technical University of Denmark (DTU). We generated two independent...

  16. Surgical tool detection in cataract surgery videos through multi-image fusion inside a convolutional neural network.

    Science.gov (United States)

    Al Hajj, Hassan; Lamard, Mathieu; Charriere, Katia; Cochener, Beatrice; Quellec, Gwenole

    2017-07-01

    The automatic detection of surgical tools in surgery videos is a promising solution for surgical workflow analysis. It paves the way to various applications, including surgical workflow optimization, surgical skill evaluation and real-time warning generation. A solution based on convolutional neural networks (CNNs) is proposed in this paper. Unlike existing solutions, the proposed CNN does not analyze images independently. it analyzes sequences of consecutive images. Features extracted from each image by the CNN are fused inside the network using the optical flow. For improved performance, this multi-image fusion strategy is also applied while training the CNN. The proposed framework was evaluated in a dataset of 30 cataract surgery videos (6 hours of videos). Ten tool categories were defined by surgeons. The proposed system was able to detect each of these categories with a high area under the ROC curve (0.953 ≤ Az ≤ 0.987). The proposed detector, based on multi-image fusion, was significantly more sensitive and specific than a similar system analyzing images independently (p = 2.98 × 10(-6) and p = 2.07 × 10(-3), respectively).

  17. Adversary phase change detection using S.O.M. and text data.

    Energy Technology Data Exchange (ETDEWEB)

    Speed, Ann Elizabeth; Doser, Adele Beatrice; Warrender, Christina E.

    2011-01-01

    In this work, we developed a self-organizing map (SOM) technique for using web-based text analysis to forecast when a group is undergoing a phase change. By 'phase change', we mean that an organization has fundamentally shifted attitudes or behaviors. For instance, when ice melts into water, the characteristics of the substance change. A formerly peaceful group may suddenly adopt violence, or a violent organization may unexpectedly agree to a ceasefire. SOM techniques were used to analyze text obtained from organization postings on the world-wide web. Results suggest it may be possible to forecast phase changes, and determine if an example of writing can be attributed to a group of interest.

  18. Adversary phase change detection using S.O.M. and text data.

    Energy Technology Data Exchange (ETDEWEB)

    Speed, Ann Elizabeth; Doser, Adele Beatrice; Warrender, Christina E.

    2010-09-01

    In this work, we developed a self-organizing map (SOM) technique for using web-based text analysis to forecast when a group is undergoing a phase change. By 'phase change', we mean that an organization has fundamentally shifted attitudes or behaviors. For instance, when ice melts into water, the characteristics of the substance change. A formerly peaceful group may suddenly adopt violence, or a violent organization may unexpectedly agree to a ceasefire. SOM techniques were used to analyze text obtained from organization postings on the world-wide web. Results suggest it may be possible to forecast phase changes, and determine if an example of writing can be attributed to a group of interest.

  19. Automated detection of discourse segment and experimental types from the text of cancer pathway results sections.

    Science.gov (United States)

    Burns, Gully A P C; Dasigi, Pradeep; de Waard, Anita; Hovy, Eduard H

    2016-01-01

    Automated machine-reading biocuration systems typically use sentence-by-sentence information extraction to construct meaning representations for use by curators. This does not directly reflect the typical discourse structure used by scientists to construct an argument from the experimental data available within a article, and is therefore less likely to correspond to representations typically used in biomedical informatics systems (let alone to the mental models that scientists have). In this study, we develop Natural Language Processing methods to locate, extract, and classify the individual passages of text from articles' Results sections that refer to experimental data. In our domain of interest (molecular biology studies of cancer signal transduction pathways), individual articles may contain as many as 30 small-scale individual experiments describing a variety of findings, upon which authors base their overall research conclusions. Our system automatically classifies discourse segments in these texts into seven categories (fact, hypothesis, problem, goal, method, result, implication) with an F-score of 0.68. These segments describe the essential building blocks of scientific discourse to (i) provide context for each experiment, (ii) report experimental details and (iii) explain the data's meaning in context. We evaluate our system on text passages from articles that were curated in molecular biology databases (the Pathway Logic Datum repository, the Molecular Interaction MINT and INTACT databases) linking individual experiments in articles to the type of assay used (coprecipitation, phosphorylation, translocation etc.). We use supervised machine learning techniques on text passages containing unambiguous references to experiments to obtain baseline F1 scores of 0.59 for MINT, 0.71 for INTACT and 0.63 for Pathway Logic. Although preliminary, these results support the notion that targeting information extraction methods to experimental results could provide

  20. Chinese Text Clustering for Topic Detection Based on Word Pattern Relation

    Science.gov (United States)

    Yang, Yen-Ju; Yu, Su-Hsin

    This research adopt the method of word expansion to compose relevant features into the same semantic concept, then conduct the corresponding documents to concept clusters, and finally merge the concepts with common documents into document clusters. We expect the mechanism, the use of semantic concept to form a feature index, can reduce the problems of polysemy and synonymy. The frequent two or three sequent nouns in the same sentence are used to form a key pattern to replace the keyword as the feature of the text. The distributive strength of key patterns is measured by Pattern Frequency, Pattern Frequency-Inverse Document Frequency, Conditional Probability, Mutual Information, and Association Norm. According to the strength the agglomerate hierarchical clustering technique is applied to cluster these key patterns into semantic concepts. Then, based on the common documents between concepts, several semantic concepts are merged to a group, in which the corresponding text will be considered as topic-related. The experimental results show that our proposed text clustering based on five strength measures of key patterns are all better than the traditional VSM clustering. PFIDF is the best in average F-measure, 97.5%.

  1. Combining machine learning, crowdsourcing and expert knowledge to detect chemical-induced diseases in text.

    Science.gov (United States)

    Bravo, Àlex; Li, Tong Shu; Su, Andrew I; Good, Benjamin M; Furlong, Laura I

    2016-01-01

    Drug toxicity is a major concern for both regulatory agencies and the pharmaceutical industry. In this context, text-mining methods for the identification of drug side effects from free text are key for the development of up-to-date knowledge sources on drug adverse reactions. We present a new system for identification of drug side effects from the literature that combines three approaches: machine learning, rule- and knowledge-based approaches. This system has been developed to address the Task 3.B of Biocreative V challenge (BC5) dealing with Chemical-induced Disease (CID) relations. The first two approaches focus on identifying relations at the sentence-level, while the knowledge-based approach is applied both at sentence and abstract levels. The machine learning method is based on the BeFree system using two corpora as training data: the annotated data provided by the CID task organizers and a new CID corpus developed by crowdsourcing. Different combinations of results from the three strategies were selected for each run of the challenge. In the final evaluation setting, the system achieved the highest Recall of the challenge (63%). By performing an error analysis, we identified the main causes of misclassifications and areas for improving of our system, and highlighted the need of consistent gold standard data sets for advancing the state of the art in text mining of drug side effects.Database URL: https://zenodo.org/record/29887?ln¼en#.VsL3yDLWR_V. © The Author(s) 2016. Published by Oxford University Press.

  2. Compositional Models for Video Event Detection: A Multiple Kernel Learning Latent Variable Approach (Open Access)

    Science.gov (United States)

    2014-03-03

    cate- gory in Fig. 1. This video contains segments focusing on the snowboard , the person jumping, is shot in an outdoor, ski-resort scene, and has fast... snowboard trick, but is unlikely to include all three. Grouping segments into their relevant scene types can improve recognition. Fi- nally, the model must

  3. Detection of text-based social cues in adults with traumatic brain injury.

    Science.gov (United States)

    Turkstra, Lyn Siobhan; Duff, Melissa Collins; Politis, Adam Michael; Mutlu, Bilge

    2017-06-08

    Written text contains verbal immediacy cues-word form or grammatical cues that indicate positive attitude or liking towards an object, action, or person. We asked if adults with moderate-severe traumatic brain injury (TBI) would respond to these cues, given evidence of TBI-related social communication impairments. Sixty-nine adults with TBI and 74 healthy comparison (HC) peers read pairs of sentences containing different types of immediacy cues (e.g., speaker A said "these Canadians" vs. B said "those Canadians.") and identified which speaker (A or B) had a more positive attitude towards the underlined entity (Task 1); and pairs of sentences comprised of a context sentence (e.g., Fred is asked, "Did you visit Joan and Sue?") and a statement sentence (Fred says, "I visited Sue and Joan.") and were asked to indicate how much Fred liked or disliked the underlined words (Task 2). HC group scores were significantly higher on Task 1, indicating more sensitivity to cues. On Task 2, TBI and HC group ratings differed across cue types and immediacy types, and the TBI group appeared to have less sensitivity to these cues. Findings suggest that TBI-related impairments may reduce sensitivity to subtle social cues in text.

  4. Facility Detection and Popularity Assessment from Text Classification of Social Media and Crowdsourced Data

    Energy Technology Data Exchange (ETDEWEB)

    Sparks, Kevin A [ORNL; Li, Roger G [ORNL; Thakur, Gautam [ORNL; Stewart, Robert N [ORNL; Urban, Marie L [ORNL

    2016-01-01

    Advances in technology have continually progressed our understanding of where people are, how they use the environment around them, and why they are at their current location. Having a better knowledge of when various locations become popular through space and time could have large impacts on research fields like urban dynamics and energy consumption. In this paper, we discuss the ability to identify and locate various facility types (e.g. restaurant, airport, stadiums) using social media, and assess methods in determining when these facilities become popular over time. We use natural language processing tools and machine learning classifiers to interpret geotagged Twitter text and determine if a user is seemingly at a location of interest when the tweet was sent. On average our classifiers are approximately 85% accurate varying across multiple facility types, with a peak precision of 98%. By using these methods to classify unstructured text, geotagged social media data can be an extremely useful tool to better understanding the composition of places and how and when people use them.

  5. Classifying smoke in laparoscopic videos using SVM

    Directory of Open Access Journals (Sweden)

    Alshirbaji Tamer Abdulbaki

    2017-09-01

    Full Text Available Smoke in laparoscopic videos usually appears due to the use of electrocautery when cutting or coagulating tissues. Therefore, detecting smoke can be used for event-based annotation in laparoscopic surgeries by retrieving the events associated with the electrocauterization. Furthermore, smoke detection can also be used for automatic smoke removal. However, detecting smoke in laparoscopic video is a challenge because of the changeability of smoke patterns, the moving camera and the different lighting conditions. In this paper, we present a video-based smoke detection algorithm to detect smoke of different densities such as fog, low and high density in laparoscopic videos. The proposed method depends on extracting various visual features from the laparoscopic images and providing them to support vector machine (SVM classifier. Features are based on motion, colour and texture patterns of the smoke. We validated our algorithm using experimental evaluation on four laparoscopic cholecystectomy videos. These four videos were manually annotated by defining every frame as smoke or non-smoke frame. The algorithm was applied to the videos by using different feature combinations for classification. Experimental results show that the combination of all proposed features gives the best classification performance. The overall accuracy (i.e. correctly classified frames is around 84%, with the sensitivity (i.e. correctly detected smoke frames and the specificity (i.e. correctly detected non-smoke frames are 89% and 80%, respectively.

  6. High-Speed Video System for Micro-Expression Detection and Recognition

    Directory of Open Access Journals (Sweden)

    Diana Borza

    2017-12-01

    Full Text Available Micro-expressions play an essential part in understanding non-verbal communication and deceit detection. They are involuntary, brief facial movements that are shown when a person is trying to conceal something. Automatic analysis of micro-expression is challenging due to their low amplitude and to their short duration (they occur as fast as 1/15 to 1/25 of a second. We propose a fully micro-expression analysis system consisting of a high-speed image acquisition setup and a software framework which can detect the frames when the micro-expressions occurred as well as determine the type of the emerged expression. The detection and classification methods use fast and simple motion descriptors based on absolute image differences. The recognition module it only involves the computation of several 2D Gaussian probabilities. The software framework was tested on two publicly available high speed micro-expression databases and the whole system was used to acquire new data. The experiments we performed show that our solution outperforms state of the art works which use more complex and computationally intensive descriptors.

  7. Dynamic detection of abnormalities in video analysis of crowd behavior with DBSCAN and neural networks

    Directory of Open Access Journals (Sweden)

    Hocine Chebi

    2016-10-01

    Full Text Available Visual analysis of human behavior is a broad field within computer vision. In this field of work, we are interested in dynamic methods in the analysis of crowd behavior which consist in detecting the abnormal entities in a group in a dense scene. These scenes are characterized by the presence of a great number of people in the camera’s field of vision. The major problem is the development of an autonomous approach for the management of a great number of anomalies which is almost impossible to carry out by human operators. We present in this paper a new approach for the detection of dynamic anomalies of very dense scenes measuring the speed of both the individuals and the whole group. The various anomalies are detected by dynamically switching between two approaches: An artificial neural network (ANN for the management of group anomalies of people, and a Density-Based Spatial Clustering of Application with Noise (DBSCAN in the case of entities. For greater robustness and effectiveness, we introduced two routines that serve to eliminate the shades and the management of occlusions. The two latter phases have proven that the results of the simulation are comparable to existing work.

  8. Detecting concept relations in clinical text: insights from a state-of-the-art model.

    Science.gov (United States)

    Zhu, Xiaodan; Cherry, Colin; Kiritchenko, Svetlana; Martin, Joel; de Bruijn, Berry

    2013-04-01

    This paper addresses an information-extraction problem that aims to identify semantic relations among medical concepts (problems, tests, and treatments) in clinical text. The objectives of the paper are twofold. First, we extend an earlier one-page description (appearing as a part of [5]) of a top-ranked model in the 2010 I2B2 NLP Challenge to a necessary level of details, with the belief that feature design is the most crucial factor to the success of our system and hence deserves a more detailed discussion. We present a precise quantification of the contributions of a wide variety of knowledge sources. In addition, we show the end-to-end results obtained on the noisy output of a top-ranked concept detector, which could help construct a more complete view of the state of the art in the real-world scenario. As the second major objective, we reformulate our models into a composite-kernel framework and present the best result, according to our knowledge, on the same dataset. Crown Copyright © 2012. Published by Elsevier Inc. All rights reserved.

  9. [The Questionnaire of Experiences Associated with Video games (CERV): an instrument to detect the problematic use of video games in Spanish adolescents].

    Science.gov (United States)

    Chamarro, Andres; Carbonell, Xavier; Manresa, Josep Maria; Munoz-Miralles, Raquel; Ortega-Gonzalez, Raquel; Lopez-Morron, M Rosa; Batalla-Martinez, Carme; Toran-Monserrat, Pere

    2014-01-01

    The aim of this study is to validate the Video Game-Related Experiences Questionnaire (CERV in Spanish). The questionnaire consists of 17 items, developed from the CERI (Internet-Related Experiences Questionnaire - Beranuy and cols.), and assesses the problematic use of non-massive video games. It was validated for adolescents in Compulsory Secondary Education. To validate the questionnaire, a confirmatory factor analysis (CFA) and an internal consistency analysis were carried out. The factor structure shows two factors: (a) Psychological dependence and use for evasion; and (b) Negative consequences of using video games. Two cut-off points were established for people with no problems in their use of video games (NP), with potential problems in their use of video games (PP), and with serious problems in their use of video games (SP). Results show that there is higher prevalence among males and that problematic use decreases with age. The CERV seems to be a good instrument for the screening of adolescents with difficulties deriving from video game use. Further research should relate problematic video game use with difficulties in other life domains, such as the academic field.

  10. Αutomated 2D shoreline detection from coastal video imagery: an example from the island of Crete

    Science.gov (United States)

    Velegrakis, A. F.; Trygonis, V.; Vousdoukas, M. I.; Ghionis, G.; Chatzipavlis, A.; Andreadis, O.; Psarros, F.; Hasiotis, Th.

    2015-06-01

    Beaches are both sensitive and critical coastal system components as they: (i) are vulnerable to coastal erosion (due to e.g. wave regime changes and the short- and long-term sea level rise) and (ii) form valuable ecosystems and economic resources. In order to identify/understand the current and future beach morphodynamics, effective monitoring of the beach spatial characteristics (e.g. the shoreline position) at adequate spatio-temporal resolutions is required. In this contribution we present the results of a new, fully-automated detection method of the (2-D) shoreline positions using high resolution video imaging from a Greek island beach (Ammoudara, Crete). A fully-automated feature detection method was developed/used to monitor the shoreline position in geo-rectified coastal imagery obtained through a video system set to collect 10 min videos every daylight hour with a sampling rate of 5 Hz, from which snapshot, time-averaged (TIMEX) and variance images (SIGMA) were generated. The developed coastal feature detector is based on a very fast algorithm using a localised kernel that progressively grows along the SIGMA or TIMEX digital image, following the maximum backscatter intensity along the feature of interest; the detector results were found to compare very well with those obtained from a semi-automated `manual' shoreline detection procedure. The automated procedure was tested on video imagery obtained from the eastern part of Ammoudara beach in two 5-day periods, a low wave energy period (6-10 April 2014) and a high wave energy period (1 -5 November 2014). The results showed that, during the high wave energy event, there have been much higher levels of shoreline variance which, however, appeared to be similarly unevenly distributed along the shoreline as that related to the low wave energy event, Shoreline variance `hot spots' were found to be related to the presence/architecture of an offshore submerged shallow beachrock reef, found at a distance of 50-80 m

  11. Rheumatoid Arthritis Educational Video Series

    Medline Plus

    Full Text Available ... Patient Webcasts / Rheumatoid Arthritis Educational Video Series Rheumatoid Arthritis Educational Video Series This series of five videos ... member of our patient care team. Managing Your Arthritis Managing Your Arthritis Managing Chronic Pain and Depression ...

  12. Rheumatoid Arthritis Educational Video Series

    Medline Plus

    Full Text Available ... Corner / Patient Webcasts / Rheumatoid Arthritis Educational Video Series Rheumatoid Arthritis Educational Video Series This series of five videos was designed to help you learn more about Rheumatoid Arthritis (RA). You will learn how the diagnosis of ...

  13. NEI You Tube Videos: Amblyopia

    Medline Plus

    Full Text Available ... questions Clinical Studies Publications Catalog Photos and Images Spanish Language Information Grants and Funding Extramural Research Division ... Low Vision Refractive Errors Retinopathy of Prematurity Science Spanish Videos Webinars NEI YouTube Videos: Amblyopia Embedded video ...

  14. Acoustic Neuroma Educational Video

    Medline Plus

    Full Text Available ... support group for me? Find a Group Upcoming Events Video Library Photo Gallery One-on-One Support ... group for me? Find a group Back Upcoming events Video Library Photo Gallery One-on-One Support ...

  15. Videos, Podcasts and Livechats

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    Full Text Available ... Doctor Find a Provider Meet the Team Blog Articles News Resources Links Videos Podcasts Webinars For the ... Doctor Find a Provider Meet the Team Blog Articles News Provider Directory Donate Resources Links Videos Podcasts ...

  16. Videos, Podcasts and Livechats

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    Full Text Available ... Doctor Find a Provider Meet the Team Blog Articles & Stories News Resources Links Videos Podcasts Webinars For ... Doctor Find a Provider Meet the Team Blog Articles & Stories News Provider Directory Donate Resources Links Videos ...

  17. Acoustic Neuroma Educational Video

    Medline Plus

    Full Text Available ... Back Support Groups Is a support group for me? Find a Group Upcoming Events Video Library Photo ... Support Groups Back Is a support group for me? Find a group Back Upcoming events Video Library ...

  18. Acoustic Neuroma Educational Video

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    Full Text Available ... group for me? Find a Group Upcoming Events Video Library Photo Gallery One-on-One Support ANetwork ... for me? Find a group Back Upcoming events Video Library Photo Gallery One-on-One Support Back ...

  19. Videos, Podcasts and Livechats

    Medline Plus

    Full Text Available ... the Team Blog Articles & Stories News Resources Links Videos Podcasts Webinars For the Media For Clinicians For ... Family Caregivers Glossary Menu In this section Links Videos Podcasts Webinars For the Media For Clinicians For ...

  20. Videos, Podcasts and Livechats

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    Full Text Available ... a Provider Meet the Team Blog Articles & Stories News Resources Links Videos Podcasts Webinars For the Media ... a Provider Meet the Team Blog Articles & Stories News Provider Directory Donate Resources Links Videos Podcasts Webinars ...

  1. Acoustic Neuroma Educational Video

    Medline Plus

    Full Text Available ... for me? Find a Group Upcoming Events Video Library Photo Gallery One-on-One Support ANetwork Peer ... me? Find a group Back Upcoming events Video Library Photo Gallery One-on-One Support Back ANetwork ...

  2. Videos, Podcasts and Livechats

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    Full Text Available ... News Resources Links Videos Podcasts Webinars For the Media For Clinicians For Policymakers For Family Caregivers Glossary ... this section Links Videos Podcasts Webinars For the Media For Clinicians For Policymakers For Family Caregivers Glossary ...

  3. Intelligent video surveillance systems

    CERN Document Server

    Dufour, Jean-Yves

    2012-01-01

    Belonging to the wider academic field of computer vision, video analytics has aroused a phenomenal surge of interest since the current millennium. Video analytics is intended to solve the problem of the incapability of exploiting video streams in real time for the purpose of detection or anticipation. It involves analyzing the videos using algorithms that detect and track objects of interest over time and that indicate the presence of events or suspect behavior involving these objects.The aims of this book are to highlight the operational attempts of video analytics, to identify possi

  4. Anomaly detection driven active learning for identifying suspicious tracks and events in WAMI video

    Science.gov (United States)

    Miller, David J.; Natraj, Aditya; Hockenbury, Ryler; Dunn, Katherine; Sheffler, Michael; Sullivan, Kevin

    2012-06-01

    We describe a comprehensive system for learning to identify suspicious vehicle tracks from wide-area motion (WAMI) video. First, since the road network for the scene of interest is assumed unknown, agglomerative hierarchical clustering is applied to all spatial vehicle measurements, resulting in spatial cells that largely capture individual road segments. Next, for each track, both at the cell (speed, acceleration, azimuth) and track (range, total distance, duration) levels, extreme value feature statistics are both computed and aggregated, to form summary (p-value based) anomaly statistics for each track. Here, to fairly evaluate tracks that travel across different numbers of spatial cells, for each cell-level feature type, a single (most extreme) statistic is chosen, over all cells traveled. Finally, a novel active learning paradigm, applied to a (logistic regression) track classifier, is invoked to learn to distinguish suspicious from merely anomalous tracks, starting from anomaly-ranked track prioritization, with ground-truth labeling by a human operator. This system has been applied to WAMI video data (ARGUS), with the tracks automatically extracted by a system developed in-house at Toyon Research Corporation. Our system gives promising preliminary results in highly ranking as suspicious aerial vehicles, dismounts, and traffic violators, and in learning which features are most indicative of suspicious tracks.

  5. Automatic detection of confusion in elderly users of a web-based health instruction video

    NARCIS (Netherlands)

    Postma-Nilsenová, Marie; Postma, Eric; Tates, Kiek

    BACKGROUND: Because of cognitive limitations and lower health literacy, many elderly patients have difficulty understanding verbal medical instructions. Automatic detection of facial movements provides a nonintrusive basis for building technological tools supporting confusion detection in healthcare

  6. Detection of patient movement during CBCT examination using video observation compared with an accelerometer-gyroscope tracking system.

    Science.gov (United States)

    Spin-Neto, Rubens; Matzen, Louise H; Schropp, Lars; Gotfredsen, Erik; Wenzel, Ann

    2017-02-01

    To compare video observation (VO) with a novel three-dimensional registration method, based on an accelerometer-gyroscope (AG) system, to detect patient movement during CBCT examination. The movements were further analyzed according to complexity and patient age. In 181 patients (118 females/63 males; age average 30 years, range: 9-84 years), 206 CBCT examinations were performed, which were video-recorded during examination. An AG was, at the same time, attached to the patient head to track head position in three dimensions. Three observers scored patient movement (yes/no) by VO. AG provided movement data on the x-, y- and z-axes. Thresholds for AG-based registration were defined at 0.5, 1, 2, 3 and 4 mm (movement distance). Movement detected by VO was compared with that registered by AG, according to movement complexity (uniplanar vs multiplanar, as defined by AG) and patient age (≤15, 16-30 and ≥31 years). According to AG, movement ≥0.5 mm was present in 160 (77.7%) examinations. According to VO, movement was present in 46 (22.3%) examinations. One VO-detected movement was not registered by AG. Overall, VO did not detect 71.9% of the movements registered by AG at the 0.5-mm threshold. At a movement distance ≥4 mm, 20% of the AG-registered movements were not detected by VO. Multiplanar movements such as lateral head rotation (72.1%) and nodding/swallowing (52.6%) were more often detected by VO in comparison with uniplanar movements, such as head lifting (33.6%) and anteroposterior translation (35.6%), at the 0.5-mm threshold. The prevalence of patients who move was highest in patients younger than 16 years (64.3% for VO and 92.3% for AG-based registration at the 0.5-mm threshold). AG-based movement registration resulted in a higher prevalence of patient movement during CBCT examination than VO-based registration. Also, AG-registered multiplanar movements were more frequently detected by VO than uniplanar movements. The prevalence of patients who move

  7. Digital Video in Research

    DEFF Research Database (Denmark)

    Frølunde, Lisbeth

    2012-01-01

    questions of our media literacy pertaining to authoring multimodal texts (visual, verbal, audial, etc.) in research practice and the status of multimodal texts in academia. The implications of academic video extend to wider issues of how researchers harness opportunities to author different types of texts......Is video becoming “the new black” in academia, if so, what are the challenges? The integration of video in research methodology (for collection, analysis) is well-known, but the use of “academic video” for dissemination is relatively new (Eriksson and Sørensen). The focus of this paper is academic...... video, or short video essays produced for the explicit purpose of communicating research processes, topics, and research-based knowledge (see the journal of academic videos: www.audiovisualthinking.org). Video is increasingly used in popular showcases for video online, such as YouTube and Vimeo, as well...

  8. DeTeCt 3.0: A software tool to detect impacts of small objects in video observations of Jupiter obtained by amateur astronomers

    Science.gov (United States)

    Juaristi, J.; Delcroix, M.; Hueso, R.; Sánchez-Lavega, A.

    2017-09-01

    Impacts of small size objects (10-20 m in diameter) with Jupiter atmosphere result in luminous superbolides that can be observed from the Earth with small size telescopes. Impacts of this kind have been observed four times by amateur astronomers since July 2010. The probability of observing one of these events is very small. Amateur astronomers observe Jupiter using fast video cameras that record thousands of frames during a few minutes which combine into a single image that generally results in a high-resolution image. Flashes are brief, faint and often lost by image reconstruction software. We present major upgrades in a software tool DeTeCt initially developed by amateur astronomer Marc Delcroix and our current project to maximize the chances of detecting more of these impacts in Jupiter.

  9. The Feasibility of Using Large-Scale Text Mining to Detect Adverse Childhood Experiences in a VA-Treated Population.

    Science.gov (United States)

    Hammond, Kenric W; Ben-Ari, Alon Y; Laundry, Ryan J; Boyko, Edward J; Samore, Matthew H

    2015-12-01

    Free text in electronic health records resists large-scale analysis. Text records facts of interest not found in encoded data, and text mining enables their retrieval and quantification. The U.S. Department of Veterans Affairs (VA) clinical data repository affords an opportunity to apply text-mining methodology to study clinical questions in large populations. To assess the feasibility of text mining, investigation of the relationship between exposure to adverse childhood experiences (ACEs) and recorded diagnoses was conducted among all VA-treated Gulf war veterans, utilizing all progress notes recorded from 2000-2011. Text processing extracted ACE exposures recorded among 44.7 million clinical notes belonging to 243,973 veterans. The relationship of ACE exposure to adult illnesses was analyzed using logistic regression. Bias considerations were assessed. ACE score was strongly associated with suicide attempts and serious mental disorders (ORs = 1.84 to 1.97), and less so with behaviorally mediated and somatic conditions (ORs = 1.02 to 1.36) per unit. Bias adjustments did not remove persistent associations between ACE score and most illnesses. Text mining to detect ACE exposure in a large population was feasible. Analysis of the relationship between ACE score and adult health conditions yielded patterns of association consistent with prior research. Copyright © 2015 International Society for Traumatic Stress Studies.

  10. Optimizing a neural network for detection of moving vehicles in video

    NARCIS (Netherlands)

    Fischer, N.M.; Kruithof, M.C.; Bouma, H.

    2017-01-01

    In the field of security and defense, it is extremely important to reliably detect moving objects, such as cars, ships, drones and missiles. Detection and analysis of moving objects in cameras near borders could be helpful to reduce illicit trading, drug trafficking, irregular border crossing,

  11. Towards real-time change detection in videos based on existing 3D models

    Science.gov (United States)

    Ruf, Boitumelo; Schuchert, Tobias

    2016-10-01

    Image based change detection is of great importance for security applications, such as surveillance and reconnaissance, in order to find new, modified or removed objects. Such change detection can generally be performed by co-registration and comparison of two or more images. However, existing 3d objects, such as buildings, may lead to parallax artifacts in case of inaccurate or missing 3d information, which may distort the results in the image comparison process, especially when the images are acquired from aerial platforms like small unmanned aerial vehicles (UAVs). Furthermore, considering only intensity information may lead to failures in detection of changes in the 3d structure of objects. To overcome this problem, we present an approach that uses Structure-from-Motion (SfM) to compute depth information, with which a 3d change detection can be performed against an existing 3d model. Our approach is capable of the change detection in real-time. We use the input frames with the corresponding camera poses to compute dense depth maps by an image-based depth estimation algorithm. Additionally we synthesize a second set of depth maps, by rendering the existing 3d model from the same camera poses as those of the image-based depth map. The actual change detection is performed by comparing the two sets of depth maps with each other. Our method is evaluated on synthetic test data with corresponding ground truth as well as on real image test data.

  12. Video Analytics

    DEFF Research Database (Denmark)

    This book collects the papers presented at two workshops during the 23rd International Conference on Pattern Recognition (ICPR): the Third Workshop on Video Analytics for Audience Measurement (VAAM) and the Second International Workshop on Face and Facial Expression Recognition (FFER) from Real...... include: re-identification, consumer behavior analysis, utilizing pupillary response for task difficulty measurement, logo detection, saliency prediction, classification of facial expressions, face recognition, face verification, age estimation, super-resolution, pose estimation, and pain recognition...

  13. Video Analytics

    DEFF Research Database (Denmark)

    include: re-identification, consumer behavior analysis, utilizing pupillary response for task difficulty measurement, logo detection, saliency prediction, classification of facial expressions, face recognition, face verification, age estimation, super-resolution, pose estimation, and pain recognition......This book collects the papers presented at two workshops during the 23rd International Conference on Pattern Recognition (ICPR): the Third Workshop on Video Analytics for Audience Measurement (VAAM) and the Second International Workshop on Face and Facial Expression Recognition (FFER) from Real...

  14. An economic evaluation of a video- and text-based computer-tailored intervention for smoking cessation: a cost-effectiveness and cost-utility analysis of a randomized controlled trial.

    Science.gov (United States)

    Stanczyk, Nicola E; Smit, Eline S; Schulz, Daniela N; de Vries, Hein; Bolman, Catherine; Muris, Jean W M; Evers, Silvia M A A

    2014-01-01

    Although evidence exists for the effectiveness of web-based smoking cessation interventions, information about the cost-effectiveness of these interventions is limited. The study investigated the cost-effectiveness and cost-utility of two web-based computer-tailored (CT) smoking cessation interventions (video- vs. text-based CT) compared to a control condition that received general text-based advice. In a randomized controlled trial, respondents were allocated to the video-based condition (N = 670), the text-based condition (N = 708) or the control condition (N = 721). Societal costs, smoking status, and quality-adjusted life years (QALYs; EQ-5D-3L) were assessed at baseline, six-and twelve-month follow-up. The incremental costs per abstinent respondent and per QALYs gained were calculated. To account for uncertainty, bootstrapping techniques and sensitivity analyses were carried out. No significant differences were found in the three conditions regarding demographics, baseline values of outcomes and societal costs over the three months prior to baseline. Analyses using prolonged abstinence as outcome measure indicated that from a willingness to pay of €1,500, the video-based intervention was likely to be the most cost-effective treatment, whereas from a willingness to pay of €50,400, the text-based intervention was likely to be the most cost-effective. With regard to cost-utilities, when quality of life was used as outcome measure, the control condition had the highest probability of being the most preferable treatment. Sensitivity analyses yielded comparable results. The video-based CT smoking cessation intervention was the most cost-effective treatment for smoking abstinence after twelve months, varying the willingness to pay per abstinent respondent from €0 up to €80,000. With regard to cost-utility, the control condition seemed to be the most preferable treatment. Probably, more time will be required to assess changes in quality of life

  15. An Economic Evaluation of a Video- and Text-Based Computer-Tailored Intervention for Smoking Cessation: A Cost-Effectiveness and Cost-Utility Analysis of a Randomized Controlled Trial

    Science.gov (United States)

    Stanczyk, Nicola E.; Smit, Eline S.; Schulz, Daniela N.; de Vries, Hein; Bolman, Catherine; Muris, Jean W. M.; Evers, Silvia M. A. A.

    2014-01-01

    Background Although evidence exists for the effectiveness of web-based smoking cessation interventions, information about the cost-effectiveness of these interventions is limited. Objective The study investigated the cost-effectiveness and cost-utility of two web-based computer-tailored (CT) smoking cessation interventions (video- vs. text-based CT) compared to a control condition that received general text-based advice. Methods In a randomized controlled trial, respondents were allocated to the video-based condition (N = 670), the text-based condition (N = 708) or the control condition (N = 721). Societal costs, smoking status, and quality-adjusted life years (QALYs; EQ-5D-3L) were assessed at baseline, six-and twelve-month follow-up. The incremental costs per abstinent respondent and per QALYs gained were calculated. To account for uncertainty, bootstrapping techniques and sensitivity analyses were carried out. Results No significant differences were found in the three conditions regarding demographics, baseline values of outcomes and societal costs over the three months prior to baseline. Analyses using prolonged abstinence as outcome measure indicated that from a willingness to pay of €1,500, the video-based intervention was likely to be the most cost-effective treatment, whereas from a willingness to pay of €50,400, the text-based intervention was likely to be the most cost-effective. With regard to cost-utilities, when quality of life was used as outcome measure, the control condition had the highest probability of being the most preferable treatment. Sensitivity analyses yielded comparable results. Conclusion The video-based CT smoking cessation intervention was the most cost-effective treatment for smoking abstinence after twelve months, varying the willingness to pay per abstinent respondent from €0 up to €80,000. With regard to cost-utility, the control condition seemed to be the most preferable treatment. Probably, more time will be

  16. A Genetic Algorithm and Fuzzy Logic Approach for Video Shot Boundary Detection.

    Science.gov (United States)

    Thounaojam, Dalton Meitei; Khelchandra, Thongam; Manglem Singh, Kh; Roy, Sudipta

    2016-01-01

    This paper proposed a shot boundary detection approach using Genetic Algorithm and Fuzzy Logic. In this, the membership functions of the fuzzy system are calculated using Genetic Algorithm by taking preobserved actual values for shot boundaries. The classification of the types of shot transitions is done by the fuzzy system. Experimental results show that the accuracy of the shot boundary detection increases with the increase in iterations or generations of the GA optimization process. The proposed system is compared to latest techniques and yields better result in terms of F1score parameter.

  17. Automatic lameness detection based on consecutive 3D-video recordings

    NARCIS (Netherlands)

    Hertem, van T.; Viazzi, S.; Steensels, M.; Maltz, E.; Antler, A.; Alchanatis, V.; Schlageter-Tello, A.; Lokhorst, C.; Romanini, C.E.B.; Bahr, C.; Berckmans, D.; Halachmi, I.

    2014-01-01

    Manual locomotion scoring for lameness detection is a time-consuming and subjective procedure. Therefore, the objective of this study is to optimise the classification output of a computer vision based algorithm for automated lameness scoring. Cow gait recordings were made during four consecutive

  18. Detection of distorted frames in retinal video-sequences via machine learning

    Science.gov (United States)

    Kolar, Radim; Liberdova, Ivana; Odstrcilik, Jan; Hracho, Michal; Tornow, Ralf P.

    2017-07-01

    This paper describes detection of distorted frames in retinal sequences based on set of global features extracted from each frame. The feature vector is consequently used in classification step, in which three types of classifiers are tested. The best classification accuracy 96% has been achieved with support vector machine approach.

  19. Real-Time Straight-Line Detection for XGA-Size Videos by Hough Transform with Parallelized Voting Procedures.

    Science.gov (United States)

    Guan, Jungang; An, Fengwei; Zhang, Xiangyu; Chen, Lei; Mattausch, Hans Jürgen

    2017-01-30

    The Hough Transform (HT) is a method for extracting straight lines from an edge image. The main limitations of the HT for usage in actual applications are computation time and storage requirements. This paper reports a hardware architecture for HT implementation on a Field Programmable Gate Array (FPGA) with parallelized voting procedure. The 2-dimensional accumulator array, namely the Hough space in parametric form (ρ, θ), for computing the strength of each line by a voting mechanism is mapped on a 1-dimensional array with regular increments of θ. Then, this Hough space is divided into a number of parallel parts. The computation of (ρ, θ) for the edge pixels and the voting procedure for straight-line determination are therefore executable in parallel. In addition, a synchronized initialization for the Hough space further increases the speed of straight-line detection, so that XGA video processing becomes possible. The designed prototype system has been synthesized on a DE4 platform with a Stratix-IV FPGA device. In the application of road-lane detection, the average processing speed of this HT implementation is 5.4ms per XGA-frame at 200 MHz working frequency.

  20. MPEG-2 Compressed-Domain Algorithms for Video Analysis

    Directory of Open Access Journals (Sweden)

    Hesseler Wolfgang

    2006-01-01

    Full Text Available This paper presents new algorithms for extracting metadata from video sequences in the MPEG-2 compressed domain. Three algorithms for efficient low-level metadata extraction in preprocessing stages are described. The first algorithm detects camera motion using the motion vector field of an MPEG-2 video. The second method extends the idea of motion detection to a limited region of interest, yielding an efficient algorithm to track objects inside video sequences. The third algorithm performs a cut detection using macroblock types and motion vectors.

  1. NEI You Tube Videos: Amblyopia

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    Full Text Available ... search for current job openings visit HHS USAJobs Home > NEI YouTube Videos > NEI YouTube Videos: Amblyopia NEI YouTube Videos YouTube Videos Home Age-Related Macular Degeneration Amblyopia Animations Blindness Cataract ...

  2. NEI You Tube Videos: Amblyopia

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    Full Text Available ... Amaurosis Low Vision Refractive Errors Retinopathy of Prematurity Science Spanish Videos Webinars NEI YouTube Videos: Amblyopia Embedded video for NEI YouTube Videos: Amblyopia NEI Home Contact Us A-Z Site Map NEI on Social Media Information in Spanish (Información en español) Website, ...

  3. Advanced video coding systems

    CERN Document Server

    Gao, Wen

    2015-01-01

    This comprehensive and accessible text/reference presents an overview of the state of the art in video coding technology. Specifically, the book introduces the tools of the AVS2 standard, describing how AVS2 can help to achieve a significant improvement in coding efficiency for future video networks and applications by incorporating smarter coding tools such as scene video coding. Topics and features: introduces the basic concepts in video coding, and presents a short history of video coding technology and standards; reviews the coding framework, main coding tools, and syntax structure of AV

  4. Real-time movement detection and analysis for video surveillance applications

    Science.gov (United States)

    Hueber, Nicolas; Hennequin, Christophe; Raymond, Pierre; Moeglin, Jean-Pierre

    2014-06-01

    Pedestrian movement along critical infrastructures like pipes, railways or highways, is of major interest in surveillance applications as well as its behavior in urban environment. The goal is to anticipate illicit or dangerous human activities. For this purpose, we propose an all-in-one small autonomous system which delivers high level statistics and reports alerts in specific cases. This situational awareness project leads us to manage efficiently the scene by performing movement analysis. A dynamic background extraction algorithm is developed to reach the degree of robustness against natural and urban environment perturbations and also to match the embedded implementation constraints. When changes are detected in the scene, specific patterns are applied to detect and highlight relevant movements. Depending on the applications, specific descriptors can be extracted and fused in order to reach a high level of interpretation. In this paper, our approach is applied to two operational use cases: pedestrian urban statistics and railway surveillance. In the first case, a grid of prototypes is deployed over a city centre to collect pedestrian movement statistics up to a macroscopic level of analysis. The results demonstrate the relevance of the delivered information; in particular, the flow density map highlights pedestrian preferential paths along the streets. In the second case, one prototype is set next to high speed train tracks to secure the area. The results exhibit a low false alarm rate and assess our approach of a large sensor network for delivering a precise operational picture without overwhelming a supervisor.

  5. Human facial skin detection in thermal video to effectively measure electrodermal activity (EDA)

    Science.gov (United States)

    Kaur, Balvinder; Hutchinson, J. Andrew; Leonard, Kevin R.; Nelson, Jill K.

    2011-06-01

    In the past, autonomic nervous system response has often been determined through measuring Electrodermal Activity (EDA), sometimes referred to as Skin Conductance (SC). Recent work has shown that high resolution thermal cameras can passively and remotely obtain an analog to EDA by assessing the activation of facial eccrine skin pores. This paper investigates a method to distinguish facial skin from non-skin portions on the face to generate a skin-only Dynamic Mask (DM), validates the DM results, and demonstrates DM performance by removing false pore counts. Moreover, this paper shows results from these techniques using data from 20+ subjects across two different experiments. In the first experiment, subjects were presented with primary screening questions for which some had jeopardy. In the second experiment, subjects experienced standard emotion-eliciting stimuli. The results from using this technique will be shown in relation to data and human perception (ground truth). This paper introduces an automatic end-to-end skin detection approach based on texture feature vectors. In doing so, the paper contributes not only a new capability of tracking facial skin in thermal imagery, but also enhances our capability to provide non-contact, remote, passive, and real-time methods for determining autonomic nervous system responses for medical and security applications.

  6. Spatio-temporal image inpainting for video applications

    Directory of Open Access Journals (Sweden)

    Voronin Viacheslav

    2017-01-01

    Full Text Available Video inpainting or completion is a vital video improvement technique used to repair or edit digital videos. This paper describes a framework for temporally consistent video completion. The proposed method allows to remove dynamic objects or restore missing or tainted regions presented in a video sequence by utilizing spatial and temporal information from neighboring scenes. Masking algorithm is used for detection of scratches or damaged portions in video frames. The algorithm iteratively performs the following operations: achieve frame; update the scene model; update positions of moving objects; replace parts of the frame occupied by the objects marked for remove by using a background model. In this paper, we extend an image inpainting algorithm based texture and structure reconstruction by incorporating an improved strategy for video. Our algorithm is able to deal with a variety of challenging situations which naturally arise in video inpainting, such as the correct reconstruction of dynamic textures, multiple moving objects and moving background. Experimental comparisons to state-of-the-art video completion methods demonstrate the effectiveness of the proposed approach. It is shown that the proposed spatio-temporal image inpainting method allows restoring a missing blocks and removing a text from the scenes on videos.

  7. Long-term accelerometry-triggered video monitoring and detection of tonic-clonic and clonic seizures in a home environment: Pilot study.

    Science.gov (United States)

    Van de Vel, Anouk; Milosevic, Milica; Bonroy, Bert; Cuppens, Kris; Lagae, Lieven; Vanrumste, Bart; Van Huffel, Sabine; Ceulemans, Berten

    2016-01-01

    The aim of our study was to test the efficacy of the VARIA system (video, accelerometry, and radar-induced activity recording) and validation of accelerometry-based detection algorithms for nocturnal tonic-clonic and clonic seizures developed by our team. We present the results of two patients with tonic-clonic and clonic seizures, measured for about one month in a home environment with four wireless accelerometers (ACM) attached to wrists and ankles. The algorithms were developed using wired ACM data synchronized with the gold standard video-/electroencephalography (EEG) and then run offline on the wireless ACM signals. Detection of seizures was compared with semicontinuous monitoring by professional caregivers (keeping an eye on multiple patients). The best result for the two patients was obtained with the semipatient-specific algorithm which was developed using all patients with tonic-clonic and clonic seizures in our database with wired ACM. It gave a mean sensitivity of 66.87% and false detection rate of 1.16 per night. This included 13 extra seizures detected (31%) compared with professional caregivers' observations. While the algorithms were previously validated in a controlled video/EEG monitoring unit with wired sensors, we now show the first results of long-term, wireless testing in a home environment.

  8. SECRETS OF SONG VIDEO

    Directory of Open Access Journals (Sweden)

    Chernyshov Alexander V.

    2014-04-01

    Full Text Available The article focuses on the origins of the song videos as TV and Internet-genre. In addition, it considers problems of screen images creation depending on the musical form and the text of a songs in connection with relevant principles of accent and phraseological video editing and filming techniques as well as with additional frames and sound elements.

  9. Reflections on academic video

    Directory of Open Access Journals (Sweden)

    Thommy Eriksson

    2012-11-01

    Full Text Available As academics we study, research and teach audiovisual media, yet rarely disseminate and mediate through it. Today, developments in production technologies have enabled academic researchers to create videos and mediate audiovisually. In academia it is taken for granted that everyone can write a text. Is it now time to assume that everyone can make a video essay? Using the online journal of academic videos Audiovisual Thinking and the videos published in it as a case study, this article seeks to reflect on the emergence and legacy of academic audiovisual dissemination. Anchoring academic video and audiovisual dissemination of knowledge in two critical traditions, documentary theory and semiotics, we will argue that academic video is in fact already present in a variety of academic disciplines, and that academic audiovisual essays are bringing trends and developments that have long been part of academic discourse to their logical conclusion.

  10. Automatic Association of Chats and Video Tracks for Activity Learning and Recognition in Aerial Video Surveillance

    Directory of Open Access Journals (Sweden)

    Riad I. Hammoud

    2014-10-01

    Full Text Available We describe two advanced video analysis techniques, including video-indexed by voice annotations (VIVA and multi-media indexing and explorer (MINER. VIVA utilizes analyst call-outs (ACOs in the form of chat messages (voice-to-text to associate labels with video target tracks, to designate spatial-temporal activity boundaries and to augment video tracking in challenging scenarios. Challenging scenarios include low-resolution sensors, moving targets and target trajectories obscured by natural and man-made clutter. MINER includes: (1 a fusion of graphical track and text data using probabilistic methods; (2 an activity pattern learning framework to support querying an index of activities of interest (AOIs and targets of interest (TOIs by movement type and geolocation; and (3 a user interface to support streaming multi-intelligence data processing. We also present an activity pattern learning framework that uses the multi-source associated data as training to index a large archive of full-motion videos (FMV. VIVA and MINER examples are demonstrated for wide aerial/overhead imagery over common data sets affording an improvement in tracking from video data alone, leading to 84% detection with modest misdetection/false alarm results due to the complexity of the scenario. The novel use of ACOs and chat Sensors 2014, 14 19844 messages in video tracking paves the way for user interaction, correction and preparation of situation awareness reports.

  11. Visual surveying platform for the automated detection of road surface distresses

    CSIR Research Space (South Africa)

    Naidoo, T

    2014-03-01

    Full Text Available from recorded video and presents it in an interactive interface for use by technical experts and maintenance schedulers. The VSP automatically detects and classifies road distresses using a two-stage artificial neural network framework. Video frames...

  12. Temporal Segmentation of MPEG Video Streams

    Directory of Open Access Journals (Sweden)

    Janko Calic

    2002-06-01

    Full Text Available Many algorithms for temporal video partitioning rely on the analysis of uncompressed video features. Since the information relevant to the partitioning process can be extracted directly from the MPEG compressed stream, higher efficiency can be achieved utilizing information from the MPEG compressed domain. This paper introduces a real-time algorithm for scene change detection that analyses the statistics of the macroblock features extracted directly from the MPEG stream. A method for extraction of the continuous frame difference that transforms the 3D video stream into a 1D curve is presented. This transform is then further employed to extract temporal units within the analysed video sequence. Results of computer simulations are reported.

  13. Video microblogging

    DEFF Research Database (Denmark)

    Bornoe, Nis; Barkhuus, Louise

    2010-01-01

    Microblogging is a recently popular phenomenon and with the increasing trend for video cameras to be built into mobile phones, a new type of microblogging has entered the arena of electronic communication: video microblogging. In this study we examine video microblogging, which is the broadcasting...... of short videos. A series of semi-structured interviews offers an understanding of why and how video microblogging is used and what the users post and broadcast....

  14. NEI You Tube Videos: Amblyopia

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    Full Text Available ... Grants and Funding Extramural Research Division of Extramural Science Programs Division of Extramural Activities Extramural Contacts NEI ... Amaurosis Low Vision Refractive Errors Retinopathy of Prematurity Science Spanish Videos Webinars NEI YouTube Videos: Amblyopia Embedded ...

  15. Rheumatoid Arthritis Educational Video Series

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    Full Text Available ... Corner / Patient Webcasts / Rheumatoid Arthritis Educational Video Series Rheumatoid Arthritis Educational Video Series This series of five ... was designed to help you learn more about Rheumatoid Arthritis (RA). You will learn how the diagnosis ...

  16. Rheumatoid Arthritis Educational Video Series

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    Full Text Available ... Our Staff Rheumatology Specialty Centers You are here: Home / Patient Corner / Patient Webcasts / Rheumatoid Arthritis Educational Video ... to take a more active role in your care. The information in these videos should not take ...

  17. Rheumatoid Arthritis Educational Video Series

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    Full Text Available ... will allow you to take a more active role in your care. The information in these videos ... Stategies to Increase your Level of Physical Activity Role of Body Weight in Osteoarthritis Educational Videos for ...

  18. Rheumatoid Arthritis Educational Video Series

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    Full Text Available ... here. Will You Support the Education of Arthritis Patients? Each year, over 1 million people visit this ... of Body Weight in Osteoarthritis Educational Videos for Patients Rheumatoid Arthritis Educational Video Series Psoriatic Arthritis 101 ...

  19. High definition colonoscopy combined with i-Scan is superior in the detection of colorectal neoplasias compared with standard video colonoscopy: a prospective randomized controlled trial.

    Science.gov (United States)

    Hoffman, A; Sar, F; Goetz, M; Tresch, A; Mudter, J; Biesterfeld, S; Galle, P R; Neurath, M F; Kiesslich, R

    2010-10-01

    Colonoscopy is the accepted gold standard for the detection of colorectal cancer. The aim of the current study was to prospectively compare high definition plus (HD+) colonoscopy with I-Scan functionality (electronic staining) vs. standard video colonoscopy. The primary endpoint was the detection of patients having colon cancer or at least one adenoma. A total of 220 patients due to undergo screening colonoscopy, postpolypectomy surveillance or with a positive occult blood test were randomized in a 1 : 1 ratio to undergo HD+ colonoscopy in conjunction with I-Scan surface enhancement (90i series, Pentax, Tokyo, Japan) or standard video colonoscopy (EC-3870FZK, Pentax). Detected colorectal lesions were judged according to type, location, and size. Lesions were characterized in the HD+ group by using further I-Scan functionality (p- and v-modes) to analyze pattern and vessel architecture. Histology was predicted and biopsies or resections were performed on all identified lesions. HD+ colonoscopy with I-Scan functionality detected significantly more patients with colorectal neoplasia (38 %) compared with standard resolution endoscopy (13 %) (200 patients finally analyzed; 100 per arm). Significantly more neoplastic (adenomatous and cancerous) lesions and more flat adenomas could be detected using high definition endoscopy with surface enhancement. Final histology could be predicted with high accuracy (98.6 %) within the HD+ group. HD+ colonoscopy with I-Scan is superior to standard video colonoscopy in detecting patients with colorectal neoplasia based on this prospective, randomized, controlled trial. © Georg Thieme Verlag KG Stuttgart · New York.

  20. Electromyography-based seizure detector: Preliminary results comparing a generalized tonic-clonic seizure detection algorithm to video-EEG recordings.

    Science.gov (United States)

    Szabó, Charles Ákos; Morgan, Lola C; Karkar, Kameel M; Leary, Linda D; Lie, Octavian V; Girouard, Michael; Cavazos, José E

    2015-09-01

    Automatic detection of generalized tonic-clonic seizures (GTCS) will facilitate patient monitoring and early intervention to prevent comorbidities, recurrent seizures, or death. Brain Sentinel (San Antonio, Texas, USA) developed a seizure-detection algorithm evaluating surface electromyography (sEMG) signals during GTCS. This study aims to validate the seizure-detection algorithm using inpatient video-electroencephalography (EEG) monitoring. sEMG was recorded unilaterally from the biceps/triceps muscles in 33 patients (17white/16 male) with a mean age of 40 (range 14-64) years who were admitted for video-EEG monitoring. Maximum voluntary biceps contraction was measured in each patient to set up the baseline physiologic muscle threshold. The raw EMG signal was recorded using conventional amplifiers, sampling at 1,024 Hz and filtered with a 60 Hz noise detection algorithm before it was processed with three band-pass filters at pass frequencies of 3-40, 130-240, and 300-400 Hz. A seizure-detection algorithm utilizing Hotelling's T-squared power analysis of compound muscle action potentials was used to identify GTCS and correlated with video-EEG recordings. In 1,399 h of continuous recording, there were 196 epileptic seizures (21 GTCS, 96 myoclonic, 28 tonic, 12 absence, and 42 focal seizures with or without loss of awareness) and 4 nonepileptic spells. During retrospective, offline evaluation of sEMG from the biceps alone, the algorithm detected 20 GTCS (95%) in 11 patients, averaging within 20 s of electroclinical onset of generalized tonic activity, as identified by video-EEG monitoring. Only one false-positive detection occurred during the postictal period following a GTCS, but false alarms were not triggered by other seizure types or spells. Brain Sentinel's seizure detection algorithm demonstrated excellent sensitivity and specificity for identifying GTCS recorded in an epilepsy monitoring unit. Further studies are needed in larger patient groups, including

  1. Veterans Crisis Line: Videos About Reaching out for Help

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    Full Text Available ... listen? see more videos from Veterans Health Administration 1 Act see more videos from Veterans Health Administration ... videos from Veterans Health Administration The Power of 1 PSA see more videos from Veterans Health Administration ...

  2. Veterans Crisis Line: Videos About Reaching out for Help

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    Full Text Available ... videos about getting help. Be There: Help Save a Life see more videos from Veterans Health Administration ... listen? see more videos from Veterans Health Administration 1 Act see more videos from Veterans Health Administration ...

  3. Video demystified

    CERN Document Server

    Jack, Keith

    2004-01-01

    This international bestseller and essential reference is the "bible" for digital video engineers and programmers worldwide. This is by far the most informative analog and digital video reference available, includes the hottest new trends and cutting-edge developments in the field. Video Demystified, Fourth Edition is a "one stop" reference guide for the various digital video technologies. The fourth edition is completely updated with all new chapters on MPEG-4, H.264, SDTV/HDTV, ATSC/DVB, and Streaming Video (Video over DSL, Ethernet, etc.), as well as discussions of the latest standards throughout. The accompanying CD-ROM is updated to include a unique set of video test files in the newest formats. *This essential reference is the "bible" for digital video engineers and programmers worldwide *Contains all new chapters on MPEG-4, H.264, SDTV/HDTV, ATSC/DVB, and Streaming Video *Completely revised with all the latest and most up-to-date industry standards.

  4. Acoustic Neuroma Educational Video

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    Full Text Available ... a Group Upcoming Events Video Library Photo Gallery One-on-One Support ANetwork Peer Support Program Community Connections Overview ... group Back Upcoming events Video Library Photo Gallery One-on-One Support Back ANetwork Peer Support Program ...

  5. Acoustic Neuroma Educational Video

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    Full Text Available ... support group for me? Find a Group Upcoming Events Video Library Photo Gallery One-on-One Support ANetwork Peer ... group for me? Find a group Back Upcoming events Video Library Photo Gallery One-on-One Support Back ANetwork ...

  6. Detecting concept mentions in biomedical text using hidden Markov model: multiple concept types at once or one at a time?

    Science.gov (United States)

    Torii, Manabu; Wagholikar, Kavishwar; Liu, Hongfang

    2014-01-17

    Identifying phrases that refer to particular concept types is a critical step in extracting information from documents. Provided with annotated documents as training data, supervised machine learning can automate this process. When building a machine learning model for this task, the model may be built to detect all types simultaneously (all-types-at-once) or it may be built for one or a few selected types at a time (one-type- or a-few-types-at-a-time). It is of interest to investigate which strategy yields better detection performance. Hidden Markov models using the different strategies were evaluated on a clinical corpus annotated with three concept types (i2b2/VA corpus) and a biology literature corpus annotated with five concept types (JNLPBA corpus). Ten-fold cross-validation tests were conducted and the experimental results showed that models trained for multiple concept types consistently yielded better performance than those trained for a single concept type. F-scores observed for the former strategies were higher than those observed for the latter by 0.9 to 2.6% on the i2b2/VA corpus and 1.4 to 10.1% on the JNLPBA corpus, depending on the target concept types. Improved boundary detection and reduced type confusion were observed for the all-types-at-once strategy. The current results suggest that detection of concept phrases could be improved by simultaneously tackling multiple concept types. This also suggests that we should annotate multiple concept types in developing a new corpus for machine learning models. Further investigation is expected to gain insights in the underlying mechanism to achieve good performance when multiple concept types are considered.

  7. An integrated video-analysis software system designed for movement detection and sleep analysis. Validation of a tool for the behavioural study of sleep.

    Science.gov (United States)

    Scatena, Michele; Dittoni, Serena; Maviglia, Riccardo; Frusciante, Roberto; Testani, Elisa; Vollono, Catello; Losurdo, Anna; Colicchio, Salvatore; Gnoni, Valentina; Labriola, Claudio; Farina, Benedetto; Pennisi, Mariano Alberto; Della Marca, Giacomo

    2012-02-01

    The aim of the present study was to develop and validate a software tool for the detection of movements during sleep, based on the automated analysis of video recordings. This software is aimed to detect and quantify movements and to evaluate periods of sleep and wake. We applied an open-source software, previously distributed on the web (Zoneminder, ZM), meant for video surveillance. A validation study was performed: computed movement analysis was compared with two standardised, 'gold standard' methods for the analysis of sleep-wake cycles: actigraphy and laboratory-based video-polysomnography. Sleep variables evaluated by ZM were not different from those measured by traditional sleep-scoring systems. Bland-Altman plots showed an overlap between the scores obtained with ZM, PSG and actigraphy, with a slight tendency of ZM to overestimate nocturnal awakenings. ZM showed a good degree of accuracy both with respect to PSG (79.9%) and actigraphy (83.1%); and had very high sensitivity (ZM vs. PSG: 90.4%; ZM vs. actigraphy: 89.5%) and relatively lower specificity (ZM vs. PSG: 42.3%; ZM vs. actigraphy: 65.4%). The computer-assisted motion analysis is reliable and reproducible, and it can allow a reliable esteem of some sleep and wake parameters. The motion-based sleep analysis shows a trend to overestimate wakefulness. The possibility to measure sleep from video recordings may be useful in those clinical and experimental conditions in which traditional PSG studies may not be performed. Copyright © 2011 International Federation of Clinical Neurophysiology. Published by Elsevier Ireland Ltd. All rights reserved.

  8. E-text

    DEFF Research Database (Denmark)

    Finnemann, Niels Ole

    2018-01-01

    the print medium, rather than written text or speech. In late 20th century, the notion of text was subject to increasing criticism as in the question raised within literary text theory: is there a text in this class? At the same time, the notion was expanded by including extra linguistic sign modalities...... (images, videos). Thus, a basic question is this: should electronic text be included in the expanded notion of text as a new digital sign modality added to the repertoire of modalities, or should it be included as a sign modality, which is both an independent modality and a container in which other...

  9. NEI You Tube Videos: Amblyopia

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    Full Text Available ... NEI YouTube Videos: Amblyopia NEI YouTube Videos YouTube Videos Home Age-Related Macular Degeneration Amblyopia Animations Blindness Cataract Convergence Insufficiency Diabetic Eye Disease Dilated Eye Exam Dry Eye For Kids Glaucoma ...

  10. SPECIAL REPORT: Creating Conference Video

    Directory of Open Access Journals (Sweden)

    Noel F. Peden

    2008-12-01

    Full Text Available Capturing video at a conference is easy. Doing it so the product is useful is another matter. Many subtle problems come into play so that video and audio obtained can be used to create a final product. This article discusses what the author learned in the two years of shooting and editing video for Code4Lib conference.

  11. Video Games and Digital Literacies

    Science.gov (United States)

    Steinkuehler, Constance

    2010-01-01

    Today's youth are situated in a complex information ecology that includes video games and print texts. At the basic level, video game play itself is a form of digital literacy practice. If we widen our focus from the "individual player + technology" to the online communities that play them, we find that video games also lie at the nexus of a…

  12. Rheumatoid Arthritis Educational Video Series

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    Full Text Available ... Corner / Patient Webcasts / Rheumatoid Arthritis Educational Video Series Rheumatoid Arthritis Educational Video Series This series of five videos ... Your Arthritis Managing Chronic Pain and Depression in Arthritis Nutrition & Rheumatoid Arthritis Arthritis and Health-related Quality of Life ...

  13. Automatic content-based analysis of georeferenced image data: Detection of Beggiatoa mats in seafloor video mosaics from the HÅkon Mosby Mud Volcano

    Science.gov (United States)

    Jerosch, K.; Lüdtke, A.; Schlüter, M.; Ioannidis, G. T.

    2007-02-01

    The combination of new underwater technology as remotely operating vehicles (ROVs), high-resolution video imagery, and software to compute georeferenced mosaics of the seafloor provides new opportunities for marine geological or biological studies and applications in offshore industry. Even during single surveys by ROVs or towed systems large amounts of images are compiled. While these underwater techniques are now well-engineered, there is still a lack of methods for the automatic analysis of the acquired image data. During ROV dives more than 4200 georeferenced video mosaics were compiled for the HÅkon Mosby Mud Volcano (HMMV). Mud volcanoes as HMMV are considered as significant source locations for methane characterised by unique chemoautotrophic communities as Beggiatoa mats. For the detection and quantification of the spatial distribution of Beggiatoa mats an automated image analysis technique was developed, which applies watershed transformation and relaxation-based labelling of pre-segmented regions. Comparison of the data derived by visual inspection of 2840 video images with the automated image analysis revealed similarities with a precision better than 90%. We consider this as a step towards a time-efficient and accurate analysis of seafloor images for computation of geochemical budgets and identification of habitats at the seafloor.

  14. Attacking Automatic Video Analysis Algorithms: A Case Study of Google Cloud Video Intelligence API

    OpenAIRE

    Hosseini, Hossein; Xiao, Baicen; Clark, Andrew; Poovendran, Radha

    2017-01-01

    Due to the growth of video data on Internet, automatic video analysis has gained a lot of attention from academia as well as companies such as Facebook, Twitter and Google. In this paper, we examine the robustness of video analysis algorithms in adversarial settings. Specifically, we propose targeted attacks on two fundamental classes of video analysis algorithms, namely video classification and shot detection. We show that an adversary can subtly manipulate a video in such a way that a human...

  15. Transcoding-Based Error-Resilient Video Adaptation for 3G Wireless Networks

    Directory of Open Access Journals (Sweden)

    Dogan Safak

    2007-01-01

    Full Text Available Transcoding is an effective method to provide video adaptation for heterogeneous internetwork video access and communication environments, which require the tailoring (i.e., repurposing of coded video properties to channel conditions, terminal capabilities, and user preferences. This paper presents a video transcoding system that is capable of applying a suite of error resilience tools on the input compressed video streams while controlling the output rates to provide robust communications over error-prone and bandwidth-limited 3G wireless networks. The transcoder is also designed to employ a new adaptive intra-refresh algorithm, which is responsive to the detected scene activity inherently embedded into the video content and the reported time-varying channel error conditions of the wireless network. Comprehensive computer simulations demonstrate significant improvements in the received video quality performances using the new transcoding architecture without an extra computational cost.

  16. Videos, Podcasts and Livechats

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  17. Videos, Podcasts and Livechats

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  18. Acoustic Neuroma Educational Video

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  19. Videos, Podcasts and Livechats

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  20. Acoustic Neuroma Educational Video

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  1. Acoustic Neuroma Educational Video

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  2. Videos, Podcasts and Livechats

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  3. Videos, Podcasts and Livechats

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  4. Acoustic Neuroma Educational Video

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  6. Acoustic Neuroma Educational Video

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  7. Videos, Podcasts and Livechats

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  8. Acoustic Neuroma Educational Video

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  9. Videos, Podcasts and Livechats

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  10. Acoustic Neuroma Educational Video

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  11. Acoustic Neuroma Educational Video

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  12. Videos, Podcasts and Livechats

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  13. Digital video.

    Science.gov (United States)

    Johnson, Don; Johnson, Mike

    2004-04-01

    The process of digital capture, editing, and archiving video has become an important aspect of documenting arthroscopic surgery. Recording the arthroscopic findings before and after surgery is an essential part of the patient's medical record. The hardware and software has become more reasonable to purchase, but the learning curve to master the software is steep. Digital video is captured at the time of arthroscopy to a hard disk, and written to a CD at the end of the operative procedure. The process of obtaining video of open procedures is more complex. Outside video of the procedure is recorded on digital tape with a digital video camera. The camera must be plugged into a computer to capture the video on the hard disk. Adobe Premiere software is used to edit the video and render the finished video to the hard drive. This finished video is burned onto a CD. We outline the choice of computer hardware and software for the manipulation of digital video. The techniques of backup and archiving the completed projects and files also are outlined. The uses of digital video for education and the formats that can be used in PowerPoint presentations are discussed.

  14. Top-Down and Bottom-Up Cues Based Moving Object Detection for Varied Background Video Sequences

    Directory of Open Access Journals (Sweden)

    Chirag I. Patel

    2014-01-01

    there is no need for background formulation and updates as it is background independent. Many bottom-up approaches and one combination of bottom-up and top-down approaches are proposed in the present paper. The proposed approaches seem more efficient due to inessential requirement of learning background model and due to being independent of previous video frames. Results indicate that the proposed approach works even against slight movements in the background and in various outdoor conditions.

  15. Immersive video

    Science.gov (United States)

    Moezzi, Saied; Katkere, Arun L.; Jain, Ramesh C.

    1996-03-01

    Interactive video and television viewers should have the power to control their viewing position. To make this a reality, we introduce the concept of Immersive Video, which employs computer vision and computer graphics technologies to provide remote users a sense of complete immersion when viewing an event. Immersive Video uses multiple videos of an event, captured from different perspectives, to generate a full 3D digital video of that event. That is accomplished by assimilating important information from each video stream into a comprehensive, dynamic, 3D model of the environment. Using this 3D digital video, interactive viewers can then move around the remote environment and observe the events taking place from any desired perspective. Our Immersive Video System currently provides interactive viewing and `walkthrus' of staged karate demonstrations, basketball games, dance performances, and typical campus scenes. In its full realization, Immersive Video will be a paradigm shift in visual communication which will revolutionize television and video media, and become an integral part of future telepresence and virtual reality systems.

  16. miRiaD: A Text Mining Tool for Detecting Associations of microRNAs with Diseases.

    Science.gov (United States)

    Gupta, Samir; Ross, Karen E; Tudor, Catalina O; Wu, Cathy H; Schmidt, Carl J; Vijay-Shanker, K

    2016-04-29

    MicroRNAs are increasingly being appreciated as critical players in human diseases, and questions concerning the role of microRNAs arise in many areas of biomedical research. There are several manually curated databases of microRNA-disease associations gathered from the biomedical literature; however, it is difficult for curators of these databases to keep up with the explosion of publications in the microRNA-disease field. Moreover, automated literature mining tools that assist manual curation of microRNA-disease associations currently capture only one microRNA property (expression) in the context of one disease (cancer). Thus, there is a clear need to develop more sophisticated automated literature mining tools that capture a variety of microRNA properties and relations in the context of multiple diseases to provide researchers with fast access to the most recent published information and to streamline and accelerate manual curation. We have developed miRiaD (microRNAs in association with Disease), a text-mining tool that automatically extracts associations between microRNAs and diseases from the literature. These associations are often not directly linked, and the intermediate relations are often highly informative for the biomedical researcher. Thus, miRiaD extracts the miR-disease pairs together with an explanation for their association. We also developed a procedure that assigns scores to sentences, marking their informativeness, based on the microRNA-disease relation observed within the sentence. miRiaD was applied to the entire Medline corpus, identifying 8301 PMIDs with miR-disease associations. These abstracts and the miR-disease associations are available for browsing at http://biotm.cis.udel.edu/miRiaD . We evaluated the recall and precision of miRiaD with respect to information of high interest to public microRNA-disease database curators (expression and target gene associations), obtaining a recall of 88.46-90.78. When we expanded the evaluation to

  17. Video games

    OpenAIRE

    Kolář, Vojtěch

    2012-01-01

    This thesis is based on a detailed analysis of various topics related to the question of whether video games can be art. In the first place it analyzes the current academic discussion on this subject and confronts different opinions of both supporters and objectors of the idea, that video games can be a full-fledged art form. The second point of this paper is to analyze the properties, that are inherent to video games, in order to find the reason, why cultural elite considers video games as i...

  18. Text Mining.

    Science.gov (United States)

    Trybula, Walter J.

    1999-01-01

    Reviews the state of research in text mining, focusing on newer developments. The intent is to describe the disparate investigations currently included under the term text mining and provide a cohesive structure for these efforts. A summary of research identifies key organizations responsible for pushing the development of text mining. A section…

  19. A Neuromorphic System for Video Object Recognition

    Directory of Open Access Journals (Sweden)

    Deepak eKhosla

    2014-11-01

    Full Text Available Automated video object recognition is a topic of emerging importance in both defense and civilian applications. This work describes an accurate and low-power neuromorphic architecture and system for real-time automated video object recognition. Our system, Neuormorphic Visual Understanding of Scenes (NEOVUS, is inspired by recent findings in computational neuroscience on feed-forward object detection and classification pipelines for processing and extracting relevant information from visual data. The NEOVUS architecture is inspired by the ventral (what and dorsal (where streams of the mammalian visual pathway and combines retinal processing, form-based and motion-based object detection, and convolutional neural nets based object classification. Our system was evaluated by the Defense Advanced Research Projects Agency (DARPA under the NEOVISION2 program on a variety of urban area video datasets collected from both stationary and moving platforms. The datasets are challenging as they include a large number of targets in cluttered scenes with varying illumination and occlusion conditions. The NEOVUS system was also mapped to commercially available off-the-shelf hardware. The dynamic power requirement for the system that includes a 5.6Mpixel retinal camera processed by object detection and classification algorithms at 30 frames per second was measured at 21.7 Watts (W, for an effective energy consumption of 5.4 nanoJoules (nJ per bit of incoming video. In a systematic evaluation of five different teams by DARPA on three aerial datasets, the NEOVUS demonstrated the best performance with the highest recognition accuracy and at least three orders of magnitude lower energy consumption than two independent state of the art computer vision systems. These unprecedented results show that the NEOVUS has the potential to revolutionize automated video object recognition towards enabling practical low-power and mobile video processing applications.

  20. Evaluation of the DTBird video-system at the Smoela wind-power plant. Detection capabilities for capturing near-turbine avian behaviour

    Energy Technology Data Exchange (ETDEWEB)

    Roel, May; Hamre, Oeyvind; Vang, Roald; Nygaard, Torgeir

    2012-07-01

    Collisions between birds and wind turbines can be a problem at wind-power plants both onshore and offshore, and the presence of endangered bird species or proximity to key functional bird areas can have major impact on the choice of site or location wind turbines. There is international consensus that one of the mail challenges in the development of measures to reduce bird collisions is the lack of good methods for assessment of the efficacy of inventions. In order to be better abe to assess the efficacy of mortality-reducing measures Statkraft wishes to find a system that can be operated under Norwegian conditions and that renders objective and quantitative information on collisions and near-flying birds. DTbird developed by Liquen Consultoria Ambiental S.L. is such a system, which is based on video-recording bird flights near turbines during the daylight period (light levels>200 lux). DTBird is a self-working system developed to detect flying birds and to take programmed actions (i.e. warming, dissuasion, collision registration, and turbine stop control) linked to real-time bird detection. This report evaluates how well the DTBird system is able to detect birds in the vicinity of a wind turbine, and assess to which extent it can be utilized to study near-turbine bird flight behaviour and possible deterrence. The evaluation was based on the video sequence recorded with the DTBird systems installed at turbine 21 and turbine 42 at the Smoela wind-power plant between March 2 2012 and September 30 2012, together with GPS telemetry data on white-tailed eagles and avian radar data. The average number of falsely triggered video sequences (false positive rate) was 1.2 per day, and during daytime the DTBird system recorded between 76% and 96% of all bird flights in the vicinity of the turbines. Visually estimated distances of recorded bird flights in the video sequences were in general assessed to be farther from the turbines com pared to the distance settings used within

  1. Design process and preliminary psychometric study of a video game to detect cognitive impairment in senior adults

    Directory of Open Access Journals (Sweden)

    Sonia Valladares-Rodriguez

    2017-06-01

    Full Text Available Introduction Assessment of episodic memory has been traditionally used to evaluate potential cognitive impairments in senior adults. Typically, episodic memory evaluation is based on personal interviews and pen-and-paper tests. This article presents the design, development and a preliminary validation of a novel digital game to assess episodic memory intended to overcome the limitations of traditional methods, such as the cost of its administration, its intrusive character, the lack of early detection capabilities, the lack of ecological validity, the learning effect and the existence of confounding factors. Materials and Methods Our proposal is based on the gamification of the California Verbal Learning Test (CVLT and it has been designed to comply with the psychometric characteristics of reliability and validity. Two qualitative focus groups and a first pilot experiment were carried out to validate the proposal. Results A more ecological, non-intrusive and better administrable tool to perform cognitive assessment was developed. Initial evidence from the focus groups and pilot experiment confirmed the developed game’s usability and offered promising results insofar its psychometric validity is concerned. Moreover, the potential of this game for the cognitive classification of senior adults was confirmed, and administration time is dramatically reduced with respect to pen-and-paper tests. Limitations Additional research is needed to improve the resolution of the game for the identification of specific cognitive impairments, as well as to achieve a complete validation of the psychometric properties of the digital game. Conclusion Initial evidence show that serious games can be used as an instrument to assess the cognitive status of senior adults, and even to predict the onset of mild cognitive impairments or Alzheimer’s disease.

  2. Video Podcasts

    DEFF Research Database (Denmark)

    Nortvig, Anne Mette; Sørensen, Birgitte Holm

    2016-01-01

    This project’s aim was to support and facilitate master’s students’ preparation and collaboration by making video podcasts of short lectures available on YouTube prior to students’ first face-to-face seminar. The empirical material stems from group interviews, from statistical data created through...... YouTube analytics and from surveys answered by students after the seminar. The project sought to explore how video podcasts support learning and reflection online and how students use and reflect on the integration of online activities in the videos. Findings showed that students engaged actively...

  3. 61214++++','DOAJ-ART-EN'); return false;" href="+++++https://jual.nipissingu.ca/wp-content/uploads/sites/25/2014/06/v61214.m4v">61214++++">Jailed - Video

    Directory of Open Access Journals (Sweden)

    Cameron CULBERT

    2012-07-01

    Full Text Available As the public education system in Northern Ontario continues to take a downward spiral, a plethora of secondary school students are being placed in an alternative educational environment. Juxtaposing the two educational settings reveals very similar methods and characteristics of educating our youth as opposed to using a truly alternative approach to education. This video reviews the relationship between public education and alternative education in a remote Northern Ontario setting. It is my belief that the traditional methods of teaching are not appropriate in educating at risk students in alternative schools. Paper and pencil worksheets do not motivate these students to learn and succeed. Alternative education should emphasize experiential learning, a just in time curriculum based on every unique individual and the students true passion for everyday life. Cameron Culbert was born on February 3rd, 1977 in North Bay, Ontario. His teenage years were split between attending public school and his willed curriculum on the ski hill. Culbert spent 10 years (1996-2002 & 2006-2010 competing for Canada as an alpine ski racer. His passion for teaching and coaching began as an athlete and has now transferred into the classroom and the community. As a graduate of Nipissing University (BA, BEd, MEd. Camerons research interests are alternative education, physical education and technology in the classroom. Currently Cameron is an active educator and coach in Northern Ontario.

  4. A Depth Video-based Human Detection and Activity Recognition using Multi-features and Embedded Hidden Markov Models for Health Care Monitoring Systems

    Directory of Open Access Journals (Sweden)

    Ahmad Jalal

    2017-08-01

    Full Text Available Increase in number of elderly people who are living independently needs especial care in the form of healthcare monitoring systems. Recent advancements in depth video technologies have made human activity recognition (HAR realizable for elderly healthcare applications. In this paper, a depth video-based novel method for HAR is presented using robust multi-features and embedded Hidden Markov Models (HMMs to recognize daily life activities of elderly people living alone in indoor environment such as smart homes. In the proposed HAR framework, initially, depth maps are analyzed by temporal motion identification method to segment human silhouettes from noisy background and compute depth silhouette area for each activity to track human movements in a scene. Several representative features, including invariant, multi-view differentiation and spatiotemporal body joints features were fused together to explore gradient orientation change, intensity differentiation, temporal variation and local motion of specific body parts. Then, these features are processed by the dynamics of their respective class and learned, modeled, trained and recognized with specific embedded HMM having active feature values. Furthermore, we construct a new online human activity dataset by a depth sensor to evaluate the proposed features. Our experiments on three depth datasets demonstrated that the proposed multi-features are efficient and robust over the state of the art features for human action and activity recognition.

  5. Know Stroke: Know the Signs, Act in Time Video

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    Full Text Available ... treatment immediately. View the Video » View the Transcript » Download the Video » Ataque Cerebral Video Loading the player... ... Jose Merino. View the Video » View the Transcript » Download the Video (75,830K) » Home | About the Campaign | ...

  6. A Method for Counting Moving People in Video Surveillance Videos

    Directory of Open Access Journals (Sweden)

    Mario Vento

    2010-01-01

    Full Text Available People counting is an important problem in video surveillance applications. This problem has been faced either by trying to detect people in the scene and then counting them or by establishing a mapping between some scene feature and the number of people (avoiding the complex detection problem. This paper presents a novel method, following this second approach, that is based on the use of SURF features and of an ϵ-SVR regressor provide an estimate of this count. The algorithm takes specifically into account problems due to partial occlusions and to perspective. In the experimental evaluation, the proposed method has been compared with the algorithm by Albiol et al., winner of the PETS 2009 contest on people counting, using the same PETS 2009 database. The provided results confirm that the proposed method yields an improved accuracy, while retaining the robustness of Albiol's algorithm.

  7. A Method for Counting Moving People in Video Surveillance Videos

    Directory of Open Access Journals (Sweden)

    Conte Donatello

    2010-01-01

    Full Text Available People counting is an important problem in video surveillance applications. This problem has been faced either by trying to detect people in the scene and then counting them or by establishing a mapping between some scene feature and the number of people (avoiding the complex detection problem. This paper presents a novel method, following this second approach, that is based on the use of SURF features and of an -SVR regressor provide an estimate of this count. The algorithm takes specifically into account problems due to partial occlusions and to perspective. In the experimental evaluation, the proposed method has been compared with the algorithm by Albiol et al., winner of the PETS 2009 contest on people counting, using the same PETS 2009 database. The provided results confirm that the proposed method yields an improved accuracy, while retaining the robustness of Albiol's algorithm.

  8. Veterans Crisis Line: Videos About Reaching out for Help

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  10. Veterans Crisis Line: Videos About Reaching out for Help

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  11. Veterans Crisis Line: Videos About Reaching out for Help

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  12. Veterans Crisis Line: Videos About Reaching out for Help

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  13. Veterans Crisis Line: Videos About Reaching out for Help

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  14. Veterans Crisis Line: Videos About Reaching out for Help

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  15. Veterans Crisis Line: Videos About Reaching out for Help

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  16. Veterans Crisis Line: Videos About Reaching out for Help

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  17. Veterans Crisis Line: Videos About Reaching out for Help

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  18. Veterans Crisis Line: Videos About Reaching out for Help

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  19. Veterans Crisis Line: Videos About Reaching out for Help

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  20. Veterans Crisis Line: Videos About Reaching out for Help

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  1. Automatic Person Identification in Camera Video by Motion Correlation

    Directory of Open Access Journals (Sweden)

    Dingbo Duan

    2014-01-01

    Full Text Available Person identification plays an important role in semantic analysis of video content. This paper presents a novel method to automatically label persons in video sequence captured from fixed camera. Instead of leveraging traditional face recognition approaches, we deal with the task of person identification by fusing information from motion sensor platforms, like smart phones, carried on human bodies and extracted from camera video. More specifically, a sequence of motion features extracted from camera video are compared with each of those collected from accelerometers of smart phones. When strong correlation is detected, identity information transmitted from the corresponding smart phone is used to identify the phone wearer. To test the feasibility and efficiency of the proposed method, extensive experiments are conducted which achieved impressive performance.

  2. ALGORITMO FONÉTICO PARA DETECCIÓN DE CADENAS DE TEXTO DUPLICADAS EN EL IDIOMA ESPAÑOL PHONETIC ALGORITHM TO DETECT DUPLICATE TEXT STRINGS IN SPANISH

    Directory of Open Access Journals (Sweden)

    Iván Amón

    2012-06-01

    Full Text Available Con frecuencia datos que deberían estar escritos de forma idéntica no lo están debido a errores ortográficos y tipográficos, variaciones en el orden de las palabras, uso de prefijos y sufijos, entre otros. Las técnicas fonéticas para detección de duplicados no están orientadas al idioma español, lo que dificulta la identificación y corrección de problemas como errores ortográficos en textos escritos en este idioma. En este artículo de investigación se propone un algoritmo denominado PhoneticSpanish para la detección de cadenas de texto duplicadas el cual considera la presencia de errores ortográficos en el idioma español. El algoritmo propuesto se comparó con nueve técnicas para la detección de duplicados. Los resultados del algoritmo fueron satisfactorios ya que se obtuvieron mejores resultados que las otras técnicas y evidencian oportunidades para mejorar el análisis de información en el idioma español.Often data that should be written so they are not identical due to misspellings and typos, variations in word order, use of prefixes and suffixes, among others. Phonetic techniques for duplicate detection are not geared toward the Spanish language, which makes the identification and correction of problems such as spelling errors in texts written in this language. In this paper we propose an algorithm called PhoneticSpanish to detect duplicate text strings which considers the presence of spelling errors in Spanish. The proposed algorithm was compared with nine techniques to detect duplicates. The results were satisfactory and the algorithm that performed better than the other techniques and demonstrate opportunities for improved analysis of information in Spanish.

  3. Simultaneous recordings of human microsaccades and drifts with a contemporary video eye tracker and the search coil technique.

    Directory of Open Access Journals (Sweden)

    Michael B McCamy

    Full Text Available Human eyes move continuously, even during visual fixation. These "fixational eye movements" (FEMs include microsaccades, intersaccadic drift and oculomotor tremor. Research in human FEMs has grown considerably in the last decade, facilitated by the manufacture of noninvasive, high-resolution/speed video-oculography eye trackers. Due to the small magnitude of FEMs, obtaining reliable data can be challenging, however, and depends critically on the sensitivity and precision of the eye tracking system. Yet, no study has conducted an in-depth comparison of human FEM recordings obtained with the search coil (considered the gold standard for measuring microsaccades and drift and with contemporary, state-of-the art video trackers. Here we measured human microsaccades and drift simultaneously with the search coil and a popular state-of-the-art video tracker. We found that 95% of microsaccades detected with the search coil were also detected with the video tracker, and 95% of microsaccades detected with video tracking were also detected with the search coil, indicating substantial agreement between the two systems. Peak/mean velocities and main sequence slopes of microsaccades detected with video tracking were significantly higher than those of the same microsaccades detected with the search coil, however. Ocular drift was significantly correlated between the two systems, but drift speeds were higher with video tracking than with the search coil. Overall, our combined results suggest that contemporary video tracking now approaches the search coil for measuring FEMs.

  4. Video Tracking dalam Digital Compositing untuk Paska Produksi Video

    Directory of Open Access Journals (Sweden)

    Ardiyan Ardiyan

    2012-04-01

    Full Text Available Video Tracking is one of the processes in video postproduction and motion picture digitally. The ability of video tracking method in the production is helpful to realize the concept of the visual. It is considered in the process of visual effects making. This paper presents how the tracking process and its benefits in visual needs, especially for video and motion picture production. Some of the things involved in the process of tracking such as failure to do so are made clear in this discussion. 

  5. Special Needs: Planning for Adulthood (Videos)

    Medline Plus

    Full Text Available ... Special Needs: Planning for Adulthood (Video) KidsHealth > For Parents > Special Needs: Planning for Adulthood (Video) Print A A A Young adults with special needs have many programs, services, and ...

  6. Celiac Family Health Education Video Series

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    Full Text Available ... Program Growth and Nutrition Program Celiac Disease Program | Videos Contact the Celiac Disease Program 1-617-355- ... live happy and productive lives. Each of our video segments provides practical information about celiac disease from ...

  7. Special Needs: Planning for Adulthood (Videos)

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    Full Text Available ... Search English Español Special Needs: Planning for Adulthood (Video) KidsHealth / For Parents / Special Needs: Planning for Adulthood (Video) Print Young adults with special needs have many ...

  8. Special Needs: Planning for Adulthood (Videos)

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    Full Text Available ... Healthy Drinks for Kids Special Needs: Planning for Adulthood (Video) KidsHealth > For Parents > Special Needs: Planning for Adulthood (Video) Print A A A Young adults with ...

  9. Special Needs: Planning for Adulthood (Videos)

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    Full Text Available ... Health Food & Fitness Diseases & Conditions Infections Drugs & Alcohol School & Jobs Sports Expert Answers (Q&A) Staying Safe Videos for Educators Search English Español Special Needs: Planning for Adulthood (Video) KidsHealth / ...

  10. Video Analysis: Lessons from Professional Video Editing Practice

    Directory of Open Access Journals (Sweden)

    Eric Laurier

    2008-09-01

    Full Text Available In this paper we join a growing body of studies that learn from vernacular video analysts quite what video analysis as an intelligible course of action might be. Rather than pursuing epistemic questions regarding video as a number of other studies of video analysis have done, our concern here is with the crafts of producing the filmic. As such we examine how audio and video clips are indexed and brought to hand during the logging process, how a first assembly of the film is built at the editing bench and how logics of shot sequencing relate to wider concerns of plotting, genre and so on. In its conclusion we make a number of suggestions about the future directions of studying video and film editors at work. URN: urn:nbn:de:0114-fqs0803378

  11. Handbook of video databases design and applications

    CERN Document Server

    Furht, Borko

    2003-01-01

    INTRODUCTIONIntroduction to Video DatabasesOge Marques and Borko FurhtVIDEO MODELING AND REPRESENTATIONModeling Video Using Input/Output Markov Models with Application to Multi-Modal Event DetectionAshutosh Garg, Milind R. Naphade, and Thomas S. HuangStatistical Models of Video Structure and SemanticsNuno VasconcelosFlavor: A Language for Media RepresentationAlexandros Eleftheriadis and Danny HongIntegrating Domain Knowledge and Visual Evidence to Support Highlight Detection in Sports VideosJuergen Assfalg, Marco Bertini, Carlo Colombo, and Alberto Del BimboA Generic Event Model and Sports Vid

  12. Surveillance Video Synopsis in GIS

    Directory of Open Access Journals (Sweden)

    Yujia Xie

    2017-10-01

    Full Text Available Surveillance videos contain a considerable amount of data, wherein interesting information to the user is sparsely distributed. Researchers construct video synopsis that contain key information extracted from a surveillance video for efficient browsing and analysis. Geospatial–temporal information of a surveillance video plays an important role in the efficient description of video content. Meanwhile, current approaches of video synopsis lack the introduction and analysis of geospatial-temporal information. Owing to the preceding problems mentioned, this paper proposes an approach called “surveillance video synopsis in GIS”. Based on an integration model of video moving objects and GIS, the virtual visual field and the expression model of the moving object are constructed by spatially locating and clustering the trajectory of the moving object. The subgraphs of the moving object are reconstructed frame by frame in a virtual scene. Results show that the approach described in this paper comprehensively analyzed and created fusion expression patterns between video dynamic information and geospatial–temporal information in GIS and reduced the playback time of video content.

  13. Automated and accurate carotid bulb detection, its verification and validation in low quality frozen frames and motion video.

    Science.gov (United States)

    Ikeda, N; Araki, T; Dey, N; Bose, S; Shafique, S; El-Baz, A; Cuadrado Godia, E; Anzidei, M; Saba, L; Suri, J S

    2014-12-01

    Carotid intima-media thickness (cIMT) measurements during clinical trials need to have a fixed reference point (also called as bulb edge points) in the anatomy from which the cIMT can be measured. Identification of the bulb edge points in carotid ultrasound images faces the challenge to be detected automatically due to low image quality and variations in ultrasound images, motion artefacts, image acquisition protocols, position of the patient, and orientation of the linear probe with respect to bulb and ultrasound gain controls during acquisition. This paper presents a patented comprehensive methodology for carotid bulb localization and bulb edge detection as a reference point. The method consists of estimating the lumen-intima borders accurately using classification paradigm. Transition points are located automatically based on curvature characteristics. Further we verify and validate the locations of bulb edge points using combination of several local image processing methods such as (i) lumen-intima shapes, (ii) bulb slopes, (iii) bulb curvature, (iv) mean lumen thickness and its variations, and (v) geometric shape fitting. Our database consists of 155 ultrasound bulb images taken from various ultrasound machines with varying resolutions and imaging conditions. Further we run our automated system blindly to spot out the bulbs in a mixture database of 336 images consisting of bulbs and no-bulbs. We are able to detect the bulbs in the bulb database with 100% accuracy having 92% as close as to a neurologists's bulb location. Our mean lumen-intima error is 0.0133 mm with precision against the manual tracings to be 98.92%. Our bulb detection system is fast and takes on an average 9 seconds per image for detection for the bulb edge points and 4 seconds for verification/validation of the bulb edge points.

  14. Customizing Multiprocessor Implementation of an Automated Video Surveillance System

    Directory of Open Access Journals (Sweden)

    Morteza Biglari-Abhari

    2006-09-01

    Full Text Available This paper reports on the development of an automated embedded video surveillance system using two customized embedded RISC processors. The application is partitioned into object tracking and video stream encoding subsystems. The real-time object tracker is able to detect and track moving objects by video images of scenes taken by stationary cameras. It is based on the block-matching algorithm. The video stream encoding involves the optimization of an international telecommunications union (ITU-T H.263 baseline video encoder for quarter common intermediate format (QCIF and common intermediate format (CIF resolution images. The two subsystems running on two processor cores were integrated and a simple protocol was added to realize the automated video surveillance system. The experimental results show that the system is capable of detecting, tracking, and encoding QCIF and CIF resolution images with object movements in them in real-time. With low cycle-count, low-transistor count, and low-power consumption requirements, the system is ideal for deployment in remote locations.

  15. Can fractal methods applied to video tracking detect the effects of deltamethrin pesticide or mercury on the locomotion behavior of shrimps?

    Science.gov (United States)

    Tenorio, Bruno Mendes; da Silva Filho, Eurípedes Alves; Neiva, Gentileza Santos Martins; da Silva, Valdemiro Amaro; Tenorio, Fernanda das Chagas Angelo Mendes; da Silva, Themis de Jesus; Silva, Emerson Carlos Soares E; Nogueira, Romildo de Albuquerque

    2017-08-01

    Shrimps can accumulate environmental toxicants and suffer behavioral changes. However, methods to quantitatively detect changes in the behavior of these shrimps are still needed. The present study aims to verify whether mathematical and fractal methods applied to video tracking can adequately describe changes in the locomotion behavior of shrimps exposed to low concentrations of toxic chemicals, such as 0.15µgL-1 deltamethrin pesticide or 10µgL-1 mercuric chloride. Results showed no change after 1min, 4, 24, and 48h of treatment. However, after 72 and 96h of treatment, both the linear methods describing the track length, mean speed, mean distance from the current to the previous track point, as well as the non-linear methods of fractal dimension (box counting or information entropy) and multifractal analysis were able to detect changes in the locomotion behavior of shrimps exposed to deltamethrin. Analysis of angular parameters of the track points vectors and lacunarity were not sensitive to those changes. None of the methods showed adverse effects to mercury exposure. These mathematical and fractal methods applicable to software represent low cost useful tools in the toxicological analyses of shrimps for quality of food, water and biomonitoring of ecosystems. Copyright © 2017 Elsevier Inc. All rights reserved.

  16. ABOUT SOUNDS IN VIDEO GAMES

    Directory of Open Access Journals (Sweden)

    Denikin Anton A.

    2012-12-01

    Full Text Available The article considers the aesthetical and practical possibilities for sounds (sound design in video games and interactive applications. Outlines the key features of the game sound, such as simulation, representativeness, interactivity, immersion, randomization, and audio-visuality. The author defines the basic terminology in study of game audio, as well as identifies significant aesthetic differences between film sounds and sounds in video game projects. It is an attempt to determine the techniques of art analysis for the approaches in study of video games including aesthetics of their sounds. The article offers a range of research methods, considering the video game scoring as a contemporary creative practice.

  17. Robust Watermarking of Video Streams

    Directory of Open Access Journals (Sweden)

    T. Polyák

    2006-01-01

    Full Text Available In the past few years there has been an explosion in the use of digital video data. Many people have personal computers at home, and with the help of the Internet users can easily share video files on their computer. This makes possible the unauthorized use of digital media, and without adequate protection systems the authors and distributors have no means to prevent it.Digital watermarking techniques can help these systems to be more effective by embedding secret data right into the video stream. This makes minor changes in the frames of the video, but these changes are almost imperceptible to the human visual system. The embedded information can involve copyright data, access control etc. A robust watermark is resistant to various distortions of the video, so it cannot be removed without affecting the quality of the host medium. In this paper I propose a video watermarking scheme that fulfills the requirements of a robust watermark. 

  18. Estimation of Web video multiplicity

    Science.gov (United States)

    Cheung, SenChing S.; Zakhor, Avideh

    1999-12-01

    With ever more popularity of video web-publishing, many popular contents are being mirrored, reformatted, modified and republished, resulting in excessive content duplication. While such redundancy provides fault tolerance for continuous availability of information, it could potentially create problems for multimedia search engines in that the search results for a given query might become repetitious, and cluttered with a large number of duplicates. As such, developing techniques for detecting similarity and duplication is important to multimedia search engines. In addition, content providers might be interested in identifying duplicates of their content for legal, contractual or other business related reasons. In this paper, we propose an efficient algorithm called video signature to detect similar video sequences for large databases such as the web. The idea is to first form a 'signature' for each video sequence by selection a small number of its frames that are most similar to a number of randomly chosen seed images. Then the similarity between any tow video sequences can be reliably estimated by comparing their respective signatures. Using this method, we achieve 85 percent recall and precision ratios on a test database of 377 video sequences. As a proof of concept, we have applied our proposed algorithm to a collection of 1800 hours of video corresponding to around 45000 clips from the web. Our results indicate that, on average, every video in our collection from the web has around five similar copies.

  19. Instructional Effectiveness of Video Media.

    Science.gov (United States)

    Wetzel, C. Douglas; And Others

    This volume is a blend of media research, cognitive science research, and tradecraft knowledge regarding video production techniques. The research covers: visual learning; verbal-auditory information; news broadcasts; the value of motion and animation in film and video; simulation (including realism and fidelity); the relationship of text and…

  20. NEI You Tube Videos: Amblyopia

    Medline Plus

    Full Text Available ... National Eye Institute’s mission is to “conduct and support research, training, health information dissemination, and other programs ... search for current job openings visit HHS USAJobs Home > NEI YouTube Videos > NEI YouTube Videos: Amblyopia NEI ...

  1. Effectiveness of slow motion video compared to real time video in improving the accuracy and consistency of subjective gait analysis in dogs

    Directory of Open Access Journals (Sweden)

    D.M. Lane

    2015-11-01

    Full Text Available Objective measures of canine gait quality via force plates, pressure mats or kinematic analysis are considered superior to subjective gait assessment (SGA. Despite research demonstrating that SGA does not accurately detect subtle lameness, it remains the most commonly performed diagnostic test for detecting lameness in dogs. This is largely because the financial, temporal and spatial requirements for existing objective gait analysis equipment makes this technology impractical for use in general practice. The utility of slow motion video as a potential tool to augment SGA is currently untested. To evaluate a more accessible way to overcome the limitations of SGA, a slow motion video study was undertaken. Three experienced veterinarians reviewed video footage of 30 dogs, 15 with a diagnosis of primary limb lameness based on history and physical examination, and 15 with no indication of limb lameness based on history and physical examination. Four different videos were made for each dog, demonstrating each dog walking and trotting in real time, and then again walking and trotting in 50% slow motion. For each video, the veterinary raters assessed both the degree of lameness, and which limb(s they felt represented the source of the lameness. Spearman’s rho, Cramer’s V, and t-tests were performed to determine if slow motion video increased either the accuracy or consistency of raters’ SGA relative to real time video. Raters demonstrated no significant increase in consistency or accuracy in their SGA of slow motion video relative to real time video. Based on these findings, slow motion video does not increase the consistency or accuracy of SGA values. Further research is required to determine if slow motion video will benefit SGA in other ways.

  2. Akademisk video

    DEFF Research Database (Denmark)

    Frølunde, Lisbeth

    2017-01-01

    Dette kapitel har fokus på metodiske problemstillinger, der opstår i forhold til at bruge (digital) video i forbindelse med forskningskommunikation, ikke mindst online. Video har længe været benyttet i forskningen til dataindsamling og forskningskommunikation. Med digitaliseringen og internettet er...... der dog opstået nye muligheder og udfordringer i forhold til at formidle og distribuere forskningsresultater til forskellige målgrupper via video. Samtidig er klassiske metodologiske problematikker som forskerens positionering i forhold til det undersøgte stadig aktuelle. Både klassiske og nye...... problemstillinger diskuteres i kapitlet, som rammesætter diskussionen ud fra forskellige positioneringsmuligheder: formidler, historiefortæller, eller dialogist. Disse positioner relaterer sig til genrer inden for ’akademisk video’. Afslutningsvis præsenteres en metodisk værktøjskasse med redskaber til planlægning...

  3. Video Analytics

    DEFF Research Database (Denmark)

    This book collects the papers presented at two workshops during the 23rd International Conference on Pattern Recognition (ICPR): the Third Workshop on Video Analytics for Audience Measurement (VAAM) and the Second International Workshop on Face and Facial Expression Recognition (FFER) from Real...... World Videos. The workshops were run on December 4, 2016, in Cancun in Mexico. The two workshops together received 13 papers. Each paper was then reviewed by at least two expert reviewers in the field. In all, 11 papers were accepted to be presented at the workshops. The topics covered in the papers...

  4. Video databases: automatic retrieval based on content.

    Science.gov (United States)

    Bolle, R. M.; Yeo, B.-L.; Yeung, M.

    Digital video databases are becoming more and more pervasive and finding video of interest in large databases is rapidly becoming a problem. Intelligent means of quick content-based video retrieval and content-based rapid video viewing is, therefore, an important topic of research. Video is a rich source of data, it contains visual and audio information, and in many cases, there is text associated with the video. Content-based video retrieval should use all this information in an efficient and effective way. From a human perspective, a video query can be viewed as an iterated sequence of navigating, searching, browsing, and viewing. This paper addresses video search in terms of these phases.

  5. Videos, Podcasts and Livechats

    Medline Plus

    Full Text Available ... Donate Resources Links Videos Podcasts Webinars For the Media For Clinicians For Policymakers For Family Caregivers Glossary Sign Up for Our Blog Subscribe to Blog Enter your email address to subscribe to this blog and receive notifications of new posts by email. Email Address CLOSE Home About ...

  6. Video processing project

    CSIR Research Space (South Africa)

    Globisch, R

    2009-03-01

    Full Text Available Video processing source code for algorithms and tools used in software media pipelines (e.g. image scalers, colour converters, etc.) The currently available source code is written in C++ with their associated libraries and DirectShow- Filters....

  7. Videos, Podcasts and Livechats

    Medline Plus

    Full Text Available Home About Donate Search Search What Is It Definition Pediatric Palliative Care Disease Types FAQ Handout for Patients and Families Is It Right for You How to Get It Talk to your Doctor Find a Provider Meet the Team Blog Articles & Stories News Resources Links Videos Podcasts ...

  8. Acoustic Neuroma Educational Video

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    Full Text Available ... Surgery What is acoustic neuroma Diagnosing Symptoms Side effects ... Groups Is a support group for me? Find a Group Upcoming Events Video Library Photo Gallery One-on-One Support ANetwork Peer Support Program Community Connections Overview Find a Meeting ...

  9. Probability distribution of intersymbol distances in random symbolic sequences: Applications to improving detection of keywords in texts and of amino acid clustering in proteins.

    Science.gov (United States)

    Carpena, Pedro; Bernaola-Galván, Pedro A; Carretero-Campos, Concepción; Coronado, Ana V

    2016-11-01

    Symbolic sequences have been extensively investigated in the past few years within the framework of statistical physics. Paradigmatic examples of such sequences are written texts, and deoxyribonucleic acid (DNA) and protein sequences. In these examples, the spatial distribution of a given symbol (a word, a DNA motif, an amino acid) is a key property usually related to the symbol importance in the sequence: The more uneven and far from random the symbol distribution, the higher the relevance of the symbol to the sequence. Thus, many techniques of analysis measure in some way the deviation of the symbol spatial distribution with respect to the random expectation. The problem is then to know the spatial distribution corresponding to randomness, which is typically considered to be either the geometric or the exponential distribution. However, these distributions are only valid for very large symbolic sequences and for many occurrences of the analyzed symbol. Here, we obtain analytically the exact, randomly expected spatial distribution valid for any sequence length and any symbol frequency, and we study its main properties. The knowledge of the distribution allows us to define a measure able to properly quantify the deviation from randomness of the symbol distribution, especially for short sequences and low symbol frequency. We apply the measure to the problem of keyword detection in written texts and to study amino acid clustering in protein sequences. In texts, we show how the results improve with respect to previous methods when short texts are analyzed. In proteins, which are typically short, we show how the measure quantifies unambiguously the amino acid clustering and characterize its spatial distribution.

  10. P2P Video Streaming Strategies based on Scalable Video Coding

    Directory of Open Access Journals (Sweden)

    F.A. López-Fuentes

    2015-02-01

    Full Text Available Video streaming over the Internet has gained significant popularity during the last years, and the academy and industry have realized a great research effort in this direction. In this scenario, scalable video coding (SVC has emerged as an important video standard to provide more functionality to video transmission and storage applications. This paper proposes and evaluates two strategies based on scalable video coding for P2P video streaming services. In the first strategy, SVC is used to offer differentiated quality video to peers with heterogeneous capacities. The second strategy uses SVC to reach a homogeneous video quality between different videos from different sources. The obtained results show that our proposed strategies enable a system to improve its performance and introduce benefits such as differentiated quality of video for clients with heterogeneous capacities and variable network conditions.

  11. Video Analytics

    DEFF Research Database (Denmark)

    This book collects the papers presented at two workshops during the 23rd International Conference on Pattern Recognition (ICPR): the Third Workshop on Video Analytics for Audience Measurement (VAAM) and the Second International Workshop on Face and Facial Expression Recognition (FFER) from Real W...

  12. The Video Head Impulse Test

    Directory of Open Access Journals (Sweden)

    G. M. Halmagyi

    2017-06-01

    Full Text Available In 1988, we introduced impulsive testing of semicircular canal (SCC function measured with scleral search coils and showed that it could accurately and reliably detect impaired function even of a single lateral canal. Later we showed that it was also possible to test individual vertical canal function in peripheral and also in central vestibular disorders and proposed a physiological mechanism for why this might be so. For the next 20 years, between 1988 and 2008, impulsive testing of individual SCC function could only be accurately done by a few aficionados with the time and money to support scleral search-coil systems—an expensive, complicated and cumbersome, semi-invasive technique that never made the transition from the research lab to the dizzy clinic. Then, in 2009 and 2013, we introduced a video method of testing function of each of the six canals individually. Since 2009, the method has been taken up by most dizzy clinics around the world, with now close to 100 refereed articles in PubMed. In many dizzy clinics around the world, video Head Impulse Testing has supplanted caloric testing as the initial and in some cases the final test of choice in patients with suspected vestibular disorders. Here, we consider seven current, interesting, and controversial aspects of video Head Impulse Testing: (1 introduction to the test; (2 the progress from the head impulse protocol (HIMPs to the new variant—suppression head impulse protocol (SHIMPs; (3 the physiological basis for head impulse testing; (4 practical aspects and potential pitfalls of video head impulse testing; (5 problems of vestibulo-ocular reflex gain calculations; (6 head impulse testing in central vestibular disorders; and (7 to stay right up-to-date—new clinical disease patterns emerging from video head impulse testing. With thanks and appreciation we dedicate this article to our friend, colleague, and mentor, Dr Bernard Cohen of Mount Sinai Medical School, New York, who

  13. Video y desarrollo rural

    Directory of Open Access Journals (Sweden)

    Fraser Colin

    2015-01-01

    Full Text Available Las primeras experiencias de video rural fueron realizadas en Perú y México. El proyecto peruano es conocido como CESPAC (Centro de Servicios de Pedagogía Audiovisual para la Capacitación. Con financiamiento externo de la FAO fue iniciado en la década del 70. El proyecto mexicano fue bautizado con el nombre de PRODERITH (Programa de Desarrollo Rural Integrado del Trópico Húmedo. Su componente de video rural tuvo un éxito muy particular a nivel de base.La evaluación concluyó en que el video rural como sistema de comunicación social para el desarrollo es excelente y de bajo costo

  14. Judgments of Nonverbal Behaviour by Children with High-Functioning Autism Spectrum Disorder: Can They Detect Signs of Winning and Losing from Brief Video Clips?

    Science.gov (United States)

    Ryan, Christian; Furley, Philip; Mulhall, Kathleen

    2016-01-01

    Typically developing children are able to judge who is winning or losing from very short clips of video footage of behaviour between active match play across a number of sports. Inferences from "thin slices" (short video clips) allow participants to make complex judgments about the meaning of posture, gesture and body language. This…

  15. TRAFFIC SIGN RECOGNATION WITH VIDEO PROCESSING TECHNIQUE

    Directory of Open Access Journals (Sweden)

    Musa AYDIN

    2013-01-01

    Full Text Available In this study, traffic signs are aimed to be recognized and identified from a video image which is taken through a video camera. To accomplish our aim, a traffic sign recognition program has been developed in MATLAB/Simulink environment. The target traffic sign are recognized in the video image with the developed program.

  16. Quantification of phenylpropanoids in commercial Echinacea products using TLC with video densitometry as detection technique and ANN for data modelling.

    Science.gov (United States)

    Agatonovic-Kustrin, S; Loescher, Christine M; Singh, Ragini

    2013-01-01

    Echinacea preparations are among the most popular herbal remedies worldwide. Although it is generally assigned immune enhancement activities, the effectiveness of Echinacea is highly dependent on the Echinacea species, part of the plant used, the age of the plant, its location and the method of extraction. The aim of this study was to investigate the capacity of an artificial neural network (ANN) to analyse thin-layer chromatography (TLC) chromatograms as fingerprint patterns for quantitative estimation of three phenylpropanoid markers (chicoric acid, chlorogenic acid and echinacoside) in commercial Echinacea products. By applying samples with different weight ratios of marker compounds to the system, a database of chromatograms was constructed. One hundred and one signal intensities in each of the TLC chromatograms were correlated to the amounts of applied echinacoside, chlorogenic acid and chicoric acid using an ANN. The developed ANN correlation was used to quantify the amounts of three marker compounds in Echinacea commercial formulations. The minimum quantifiable level of 63, 154 and 98 ng and the limit of detection of 19, 46 and 29 ng were established for echinacoside, chlorogenic acid and chicoric acid respectively. A novel method for quality control of herbal products, based on TLC separation, high-resolution digital plate imaging and ANN data analysis has been developed. The method proposed can be adopted for routine evaluation of the phytochemical variability in Echinacea formulations available in the market. Copyright © 2012 John Wiley & Sons, Ltd.

  17. Design process and preliminary psychometric study of a video game to detect cognitive impairment in senior adults

    Science.gov (United States)

    Perez-Rodriguez, Roberto; Facal, David; Fernandez-Iglesias, Manuel J.; Anido-Rifon, Luis; Mouriño-Garcia, Marcos

    2017-01-01

    Introduction Assessment of episodic memory has been traditionally used to evaluate potential cognitive impairments in senior adults. Typically, episodic memory evaluation is based on personal interviews and pen-and-paper tests. This article presents the design, development and a preliminary validation of a novel digital game to assess episodic memory intended to overcome the limitations of traditional methods, such as the cost of its administration, its intrusive character, the lack of early detection capabilities, the lack of ecological validity, the learning effect and the existence of confounding factors. Materials and Methods Our proposal is based on the gamification of the California Verbal Learning Test (CVLT) and it has been designed to comply with the psychometric characteristics of reliability and validity. Two qualitative focus groups and a first pilot experiment were carried out to validate the proposal. Results A more ecological, non-intrusive and better administrable tool to perform cognitive assessment was developed. Initial evidence from the focus groups and pilot experiment confirmed the developed game’s usability and offered promising results insofar its psychometric validity is concerned. Moreover, the potential of this game for the cognitive classification of senior adults was confirmed, and administration time is dramatically reduced with respect to pen-and-paper tests. Limitations Additional research is needed to improve the resolution of the game for the identification of specific cognitive impairments, as well as to achieve a complete validation of the psychometric properties of the digital game. Conclusion Initial evidence show that serious games can be used as an instrument to assess the cognitive status of senior adults, and even to predict the onset of mild cognitive impairments or Alzheimer’s disease. PMID:28674661

  18. Forensic analysis of video steganography tools

    Directory of Open Access Journals (Sweden)

    Thomas Sloan

    2015-05-01

    Full Text Available Steganography is the art and science of concealing information in such a way that only the sender and intended recipient of a message should be aware of its presence. Digital steganography has been used in the past on a variety of media including executable files, audio, text, games and, notably, images. Additionally, there is increasing research interest towards the use of video as a media for steganography, due to its pervasive nature and diverse embedding capabilities. In this work, we examine the embedding algorithms and other security characteristics of several video steganography tools. We show how all feature basic and severe security weaknesses. This is potentially a very serious threat to the security, privacy and anonymity of their users. It is important to highlight that most steganography users have perfectly legal and ethical reasons to employ it. Some common scenarios would include citizens in oppressive regimes whose freedom of speech is compromised, people trying to avoid massive surveillance or censorship, political activists, whistle blowers, journalists, etc. As a result of our findings, we strongly recommend ceasing any use of these tools, and to remove any contents that may have been hidden, and any carriers stored, exchanged and/or uploaded online. For many of these tools, carrier files will be trivial to detect, potentially compromising any hidden data and the parties involved in the communication. We finish this work by presenting our steganalytic results, that highlight a very poor current state of the art in practical video steganography tools. There is unfortunately a complete lack of secure and publicly available tools, and even commercial tools offer very poor security. We therefore encourage the steganography community to work towards the development of more secure and accessible video steganography tools, and make them available for the general public. The results presented in this work can also be seen as a useful

  19. Veterans Crisis Line: Videos About Reaching out for Help

    Medline Plus

    Full Text Available ... see more videos from Blue Star Families These Hands PSA see more videos from Veterans Health Administration ... Line text-messaging service does not store mobile phone numbers of users who access information via text ...

  20. Video systems for alarm assessment

    Energy Technology Data Exchange (ETDEWEB)

    Greenwoll, D.A.; Matter, J.C. (Sandia National Labs., Albuquerque, NM (United States)); Ebel, P.E. (BE, Inc., Barnwell, SC (United States))

    1991-09-01

    The purpose of this NUREG is to present technical information that should be useful to NRC licensees in designing closed-circuit television systems for video alarm assessment. There is a section on each of the major components in a video system: camera, lens, lighting, transmission, synchronization, switcher, monitor, and recorder. Each section includes information on component selection, procurement, installation, test, and maintenance. Considerations for system integration of the components are contained in each section. System emphasis is focused on perimeter intrusion detection and assessment systems. A glossary of video terms is included. 13 figs., 9 tabs.

  1. Rheumatoid Arthritis Educational Video Series

    Medline Plus

    Full Text Available ... Arthritis Center since 2000, currently serving as the Nurse Manager. She is a critical member of our patient care ... of Body Weight in Osteoarthritis Educational Videos for ...

  2. Rheumatoid Arthritis Educational Video Series

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    Full Text Available ... will allow you to take a more active role in your care. The information in these videos ... Arthritis and Health-related Quality of Life Rehabilitation Management for Rheumatoid Arthritis Patients Rehabilitation of Older Adult ...

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  9. A Review and Comparison of Measures for Automatic Video Surveillance Systems

    Directory of Open Access Journals (Sweden)

    Baumann Axel

    2008-01-01

    Full Text Available Abstract Today's video surveillance systems are increasingly equipped with video content analysis for a great variety of applications. However, reliability and robustness of video content analysis algorithms remain an issue. They have to be measured against ground truth data in order to quantify the performance and advancements of new algorithms. Therefore, a variety of measures have been proposed in the literature, but there has neither been a systematic overview nor an evaluation of measures for specific video analysis tasks yet. This paper provides a systematic review of measures and compares their effectiveness for specific aspects, such as segmentation, tracking, and event detection. Focus is drawn on details like normalization issues, robustness, and representativeness. A software framework is introduced for continuously evaluating and documenting the performance of video surveillance systems. Based on many years of experience, a new set of representative measures is proposed as a fundamental part of an evaluation framework.

  10. A Review and Comparison of Measures for Automatic Video Surveillance Systems

    Directory of Open Access Journals (Sweden)

    Jie Yu

    2008-10-01

    Full Text Available Today's video surveillance systems are increasingly equipped with video content analysis for a great variety of applications. However, reliability and robustness of video content analysis algorithms remain an issue. They have to be measured against ground truth data in order to quantify the performance and advancements of new algorithms. Therefore, a variety of measures have been proposed in the literature, but there has neither been a systematic overview nor an evaluation of measures for specific video analysis tasks yet. This paper provides a systematic review of measures and compares their effectiveness for specific aspects, such as segmentation, tracking, and event detection. Focus is drawn on details like normalization issues, robustness, and representativeness. A software framework is introduced for continuously evaluating and documenting the performance of video surveillance systems. Based on many years of experience, a new set of representative measures is proposed as a fundamental part of an evaluation framework.

  11. Multimodal Semantics Extraction from User-Generated Videos

    Directory of Open Access Journals (Sweden)

    Francesco Cricri

    2012-01-01

    Full Text Available User-generated video content has grown tremendously fast to the point of outpacing professional content creation. In this work we develop methods that analyze contextual information of multiple user-generated videos in order to obtain semantic information about public happenings (e.g., sport and live music events being recorded in these videos. One of the key contributions of this work is a joint utilization of different data modalities, including such captured by auxiliary sensors during the video recording performed by each user. In particular, we analyze GPS data, magnetometer data, accelerometer data, video- and audio-content data. We use these data modalities to infer information about the event being recorded, in terms of layout (e.g., stadium, genre, indoor versus outdoor scene, and the main area of interest of the event. Furthermore we propose a method that automatically identifies the optimal set of cameras to be used in a multicamera video production. Finally, we detect the camera users which fall within the field of view of other cameras recording at the same public happening. We show that the proposed multimodal analysis methods perform well on various recordings obtained in real sport events and live music performances.

  12. Visual instance mining of news videos using a graph-based approach

    OpenAIRE

    Almendros Gutiérrez, David

    2014-01-01

    [ANGLÈS] The aim of this thesis is to design a tool that performs visual instance search mining for news video summarization. This means to extract the relevant content of the video in order to be able to recognize the storyline of the news. Initially, a sampling of the video is required to get the frames with a desired rate. Then, different relevant contents are detected from each frame, focusing on faces, text and several objects that the user can select. Next, we use a graph-based clusteri...

  13. Color image and video enhancement

    CERN Document Server

    Lecca, Michela; Smolka, Bogdan

    2015-01-01

    This text covers state-of-the-art color image and video enhancement techniques. The book examines the multivariate nature of color image/video data as it pertains to contrast enhancement, color correction (equalization, harmonization, normalization, balancing, constancy, etc.), noise removal and smoothing. This book also discusses color and contrast enhancement in vision sensors and applications of image and video enhancement.   ·         Focuses on enhancement of color images/video ·         Addresses algorithms for enhancing color images and video ·         Presents coverage on super resolution, restoration, in painting, and colorization.

  14. GPS-Aided Video Tracking

    Directory of Open Access Journals (Sweden)

    Udo Feuerhake

    2015-08-01

    Full Text Available Tracking moving objects is both challenging and important for a large variety of applications. Different technologies based on the global positioning system (GPS and video or radio data are used to obtain the trajectories of the observed objects. However, in some use cases, they fail to provide sufficiently accurate, complete and correct data at the same time. In this work we present an approach for fusing GPS- and video-based tracking in order to exploit their individual advantages. In this way we aim to combine the reliability of GPS tracking with the high geometric accuracy of camera detection. For the fusion of the movement data provided by the different devices we use a hidden Markov model (HMM formulation and the Viterbi algorithm to extract the most probable trajectories. In three experiments, we show that our approach is able to deal with challenging situations like occlusions or objects which are temporarily outside the monitored area. The results show the desired increase in terms of accuracy, completeness and correctness.

  15. Performance Analysis of Video Transmission Using Sequential Distortion Minimization Method for Digital Video Broadcasting Terrestrial

    Directory of Open Access Journals (Sweden)

    Novita Astin

    2016-12-01

    Full Text Available This paper presents about the transmission of Digital Video Broadcasting system with streaming video resolution 640x480 on different IQ rate and modulation. In the video transmission, distortion often occurs, so the received video has bad quality. Key frames selection algorithm is flexibel on a change of video, but on these methods, the temporal information of a video sequence is omitted. To minimize distortion between the original video and received video, we aimed at adding methodology using sequential distortion minimization algorithm. Its aim was to create a new video, better than original video without significant loss of content between the original video and received video, fixed sequentially. The reliability of video transmission was observed based on a constellation diagram, with the best result on IQ rate 2 Mhz and modulation 8 QAM. The best video transmission was also investigated using SEDIM (Sequential Distortion Minimization Method and without SEDIM. The experimental result showed that the PSNR (Peak Signal to Noise Ratio average of video transmission using SEDIM was an increase from 19,855 dB to 48,386 dB and SSIM (Structural Similarity average increase 10,49%. The experimental results and comparison of proposed method obtained a good performance. USRP board was used as RF front-end on 2,2 GHz.

  16. A Framework for Advanced Video Traces: Evaluating Visual Quality for Video Transmission Over Lossy Networks

    Directory of Open Access Journals (Sweden)

    Reisslein Martin

    2006-01-01

    Full Text Available Conventional video traces (which characterize the video encoding frame sizes in bits and frame quality in PSNR are limited to evaluating loss-free video transmission. To evaluate robust video transmission schemes for lossy network transport, generally experiments with actual video are required. To circumvent the need for experiments with actual videos, we propose in this paper an advanced video trace framework. The two main components of this framework are (i advanced video traces which combine the conventional video traces with a parsimonious set of visual content descriptors, and (ii quality prediction schemes that based on the visual content descriptors provide an accurate prediction of the quality of the reconstructed video after lossy network transport. We conduct extensive evaluations using a perceptual video quality metric as well as the PSNR in which we compare the visual quality predicted based on the advanced video traces with the visual quality determined from experiments with actual video. We find that the advanced video trace methodology accurately predicts the quality of the reconstructed video after frame losses.

  17. Intelligent Model for Video Survillance Security System

    Directory of Open Access Journals (Sweden)

    J. Vidhya

    2013-12-01

    Full Text Available Video surveillance system senses and trails out all the threatening issues in the real time environment. It prevents from security threats with the help of visual devices which gather the information related to videos like CCTV’S and IP (Internet Protocol cameras. Video surveillance system has become a key for addressing problems in the public security. They are mostly deployed on the IP based network. So, all the possible security threats exist in the IP based application might also be the threats available for the reliable application which is available for video surveillance. In result, it may increase cybercrime, illegal video access, mishandling videos and so on. Hence, in this paper an intelligent model is used to propose security for video surveillance system which ensures safety and it provides secured access on video.

  18. Problems in using p-curve analysis and text-mining to detect rate of p-hacking and evidential value

    Directory of Open Access Journals (Sweden)

    Dorothy V.M. Bishop

    2016-02-01

    Full Text Available Background. The p-curve is a plot of the distribution of p-values reported in a set of scientific studies. Comparisons between ranges of p-values have been used to evaluate fields of research in terms of the extent to which studies have genuine evidential value, and the extent to which they suffer from bias in the selection of variables and analyses for publication, p-hacking. Methods. p-hacking can take various forms. Here we used R code to simulate the use of ghost variables, where an experimenter gathers data on several dependent variables but reports only those with statistically significant effects. We also examined a text-mined dataset used by Head et al. (2015 and assessed its suitability for investigating p-hacking. Results. We show that when there is ghost p-hacking, the shape of the p-curve depends on whether dependent variables are intercorrelated. For uncorrelated variables, simulated p-hacked data do not give the “p-hacking bump” just below .05 that is regarded as evidence of p-hacking, though there is a negative skew when simulated variables are inter-correlated. The way p-curves vary according to features of underlying data poses problems when automated text mining is used to detect p-values in heterogeneous sets of published papers. Conclusions. The absence of a bump in the p-curve is not indicative of lack of p-hacking. Furthermore, while studies with evidential value will usually generate a right-skewed p-curve, we cannot treat a right-skewed p-curve as an indicator of the extent of evidential value, unless we have a model specific to the type of p-values entered into the analysis. We conclude that it is not feasible to use the p-curve to estimate the extent of p-hacking and evidential value unless there is considerable control over the type of data entered into the analysis. In particular, p-hacking with ghost variables is likely to be missed.

  19. Celiac Family Health Education Video Series

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  1. Robust Video Stabilization Using Particle Keypoint Update and l1-Optimized Camera Path

    Directory of Open Access Journals (Sweden)

    Semi Jeon

    2017-02-01

    Full Text Available Acquisition of stabilized video is an important issue for various type of digital cameras. This paper presents an adaptive camera path estimation method using robust feature detection to remove shaky artifacts in a video. The proposed algorithm consists of three steps: (i robust feature detection using particle keypoints between adjacent frames; (ii camera path estimation and smoothing; and (iii rendering to reconstruct a stabilized video. As a result, the proposed algorithm can estimate the optimal homography by redefining important feature points in the flat region using particle keypoints. In addition, stabilized frames with less holes can be generated from the optimal, adaptive camera path that minimizes a temporal total variation (TV. The proposed video stabilization method is suitable for enhancing the visual quality for various portable cameras and can be applied to robot vision, driving assistant systems, and visual surveillance systems.

  2. Video Quality Prediction Models Based on Video Content Dynamics for H.264 Video over UMTS Networks

    Directory of Open Access Journals (Sweden)

    Asiya Khan

    2010-01-01

    Full Text Available The aim of this paper is to present video quality prediction models for objective non-intrusive, prediction of H.264 encoded video for all content types combining parameters both in the physical and application layer over Universal Mobile Telecommunication Systems (UMTS networks. In order to characterize the Quality of Service (QoS level, a learning model based on Adaptive Neural Fuzzy Inference System (ANFIS and a second model based on non-linear regression analysis is proposed to predict the video quality in terms of the Mean Opinion Score (MOS. The objective of the paper is two-fold. First, to find the impact of QoS parameters on end-to-end video quality for H.264 encoded video. Second, to develop learning models based on ANFIS and non-linear regression analysis to predict video quality over UMTS networks by considering the impact of radio link loss models. The loss models considered are 2-state Markov models. Both the models are trained with a combination of physical and application layer parameters and validated with unseen dataset. Preliminary results show that good prediction accuracy was obtained from both the models. The work should help in the development of a reference-free video prediction model and QoS control methods for video over UMTS networks.

  3. Problems in using p-curve analysis and text-mining to detect rate of p-hacking and evidential value.

    Science.gov (United States)

    Bishop, Dorothy V M; Thompson, Paul A

    2016-01-01

    Background. The p-curve is a plot of the distribution of p-values reported in a set of scientific studies. Comparisons between ranges of p-values have been used to evaluate fields of research in terms of the extent to which studies have genuine evidential value, and the extent to which they suffer from bias in the selection of variables and analyses for publication, p-hacking. Methods. p-hacking can take various forms. Here we used R code to simulate the use of ghost variables, where an experimenter gathers data on several dependent variables but reports only those with statistically significant effects. We also examined a text-mined dataset used by Head et al. (2015) and assessed its suitability for investigating p-hacking. Results. We show that when there is ghost p-hacking, the shape of the p-curve depends on whether dependent variables are intercorrelated. For uncorrelated variables, simulated p-hacked data do not give the "p-hacking bump" just below .05 that is regarded as evidence of p-hacking, though there is a negative skew when simulated variables are inter-correlated. The way p-curves vary according to features of underlying data poses problems when automated text mining is used to detect p-values in heterogeneous sets of published papers. Conclusions. The absence of a bump in the p-curve is not indicative of lack of p-hacking. Furthermore, while studies with evidential value will usually generate a right-skewed p-curve, we cannot treat a right-skewed p-curve as an indicator of the extent of evidential value, unless we have a model specific to the type of p-values entered into the analysis. We conclude that it is not feasible to use the p-curve to estimate the extent of p-hacking and evidential value unless there is considerable control over the type of data entered into the analysis. In particular, p-hacking with ghost variables is likely to be missed.

  4. Fast Aerial Video Stitching

    Directory of Open Access Journals (Sweden)

    Jing Li

    2014-10-01

    Full Text Available The highly efficient and robust stitching of aerial video captured by unmanned aerial vehicles (UAVs is a challenging problem in the field of robot vision. Existing commercial image stitching systems have seen success with offline stitching tasks, but they cannot guarantee high-speed performance when dealing with online aerial video sequences. In this paper, we present a novel system which has an unique ability to stitch high-frame rate aerial video at a speed of 150 frames per second (FPS. In addition, rather than using a high-speed vision platform such as FPGA or CUDA, our system is running on a normal personal computer. To achieve this, after the careful comparison of the existing invariant features, we choose the FAST corner and binary descriptor for efficient feature extraction and representation, and present a spatial and temporal coherent filter to fuse the UAV motion information into the feature matching. The proposed filter can remove the majority of feature correspondence outliers and significantly increase the speed of robust feature matching by up to 20 times. To achieve a balance between robustness and efficiency, a dynamic key frame-based stitching framework is used to reduce the accumulation errors. Extensive experiments on challenging UAV datasets demonstrate that our approach can break through the speed limitation and generate an accurate stitching image for aerial video stitching tasks.

  5. Problem with multi-video format M-learning applications

    CSIR Research Space (South Africa)

    Adeyeye, MO

    2014-01-01

    Full Text Available in conjunction with the technical aspects of video display in browsers, when varying media formats are used. The <video> tag used in this work renders videos from two sources with different MIME types. Feeds from the video sources, namely YouTube and UCT...

  6. Know Stroke: Know the Signs, Act in Time Video

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    Full Text Available ... Stroke Home » Stroke Materials » Loading the player... Video Transcript Weakness on one Side. Trouble Speaking. Trouble Seeing. ... medical treatment immediately. View the Video » View the Transcript » Download the Video » Ataque Cerebral Video Loading the ...

  7. Improving the Quality of Color Colonoscopy Videos

    Directory of Open Access Journals (Sweden)

    Dahyot Rozenn

    2008-01-01

    Full Text Available Abstract Colonoscopy is currently one of the best methods to detect colorectal cancer. Nowadays, one of the widely used colonoscopes has a monochrome chipset recording successively at 60 Hz and components merged into one color video stream. Misalignments of the channels occur each time the camera moves, and this artefact impedes both online visual inspection by doctors and offline computer analysis of the image data. We propose to restore this artefact by first equalizing the color channels and then performing a robust camera motion estimation and compensation.

  8. Using Video in the English Language Clasroom

    Directory of Open Access Journals (Sweden)

    Amado Vicente

    2002-08-01

    Full Text Available Video is a popular and a motivating potential medium in schools. Using video in the language classroom helps the language teachers in many different ways. Video, for instance, brings the outside world into the language classroom, providing the class with many different topics and reasons to talk about. It can provide comprehensible input to the learners through contextualised models of language use. It also offers good opportunities to introduce native English speech into the language classroom. Through this article I will try to show what the benefits of using video are and, at the end, I present an instrument to select and classify video materials.

  9. Video streaming in the Wild West

    Directory of Open Access Journals (Sweden)

    Helen Gail Prosser

    2006-11-01

    Full Text Available Northern Lakes College in north-central Alberta is the first post-secondary institution in Canada to use the Media on Demand digital video system to stream large video files between dispersed locations (Karlsen. Staff and students at distant locations of Northern Lakes College are now viewing more than 350 videos using video streaming technology. This has been made possible by SuperNet, a high capacity broadband network that connects schools, hospitals, libraries and government offices throughout the province of Alberta (Alberta SuperNet. This article describes the technical process of implementing video streaming at Northern Lakes College from March 2005 until March 2006.

  10. Automatic Story Segmentation for TV News Video Using Multiple Modalities

    Directory of Open Access Journals (Sweden)

    Émilie Dumont

    2012-01-01

    Full Text Available While video content is often stored in rather large files or broadcasted in continuous streams, users are often interested in retrieving only a particular passage on a topic of interest to them. It is, therefore, necessary to split video documents or streams into shorter segments corresponding to appropriate retrieval units. We propose here a method for the automatic segmentation of TV news videos into stories. A-multiple-descriptor based segmentation approach is proposed. The selected multimodal features are complementary and give good insights about story boundaries. Once extracted, these features are expanded with a local temporal context and combined by an early fusion process. The story boundaries are then predicted using machine learning techniques. We investigate the system by experiments conducted using TRECVID 2003 data and protocol of the story boundary detection task, and we show that the proposed approach outperforms the state-of-the-art methods while requiring a very small amount of manual annotation.

  11. Veterans Crisis Line: Videos About Reaching out for Help

    Medline Plus

    Full Text Available ... Help see more videos from Veterans Health Administration Suicide Prevention PSA for Military Families see more videos ... About About the Veterans Crisis Line FAQs Veteran Suicide The Veterans Crisis Line text-messaging service does ...

  12. Obscene Video Recognition Using Fuzzy SVM and New Sets of Features

    Directory of Open Access Journals (Sweden)

    Alireza Behrad

    2013-02-01

    Full Text Available In this paper, a novel approach for identifying normal and obscene videos is proposed. In order to classify different episodes of a video independently and discard the need to process all frames, first, key frames are extracted and skin regions are detected for groups of video frames starting with key frames. In the second step, three different features including 1- structural features based on single frame information, 2- features based on spatiotemporal volume and 3-motion-based features, are extracted for each episode of video. The PCA-LDA method is then applied to reduce the size of structural features and select more distinctive features. For the final step, we use fuzzy or a Weighted Support Vector Machine (WSVM classifier to identify video episodes. We also employ a multilayer Kohonen network as an initial clustering algorithm to increase the ability to discriminate between the extracted features into two classes of videos. Features based on motion and periodicity characteristics increase the efficiency of the proposed algorithm in videos with bad illumination and skin colour variation. The proposed method is evaluated using 1100 videos in different environmental and illumination conditions. The experimental results show a correct recognition rate of 94.2% for the proposed algorithm.

  13. Six degrees of video game narrative: a classification for narrative in video games

    OpenAIRE

    Şengün, Sercan

    2013-01-01

    158 pages This study aims to construct a systematical approach to classification of narrative usage in video games. The most recent dominant approaches of reading a video game text – narratology and ludology - are discussed. By inquiring the place of interactivity and autonomy inside the discourse of video game narrative, a classification is proposed. Consequently six groups of video games are determined, depending on the levels of combination of narration and ludic context. These Six Degr...

  14. Developing an accessible video player

    Directory of Open Access Journals (Sweden)

    Juan José Rodríguez Soler

    2012-05-01

    Full Text Available Online Channels in financial institutions allows customers with disabilities to access services in a convenient way for them.However, one of the current challenges of this sector is to improve web accessibility and to incorporate technological resources to provide access to multimedia and video content, which has become a new form of internet communication.The present work shows in detail the strategy followed when designing and developing the new video player used by Bankinter for these purposes.

  15. Video otoscopy as a diagnostic tool for canine otoacariasis

    Directory of Open Access Journals (Sweden)

    Clarissa Pimentel de Souza

    Full Text Available Canine otoacariasis, or otodectic mange, is a common parasitic disorder of dogs' ear canals caused by the mite Otodectes cynotis. Infestation can be detected through diverse protocols of varying sensitivity. We evaluated the use of video otoscopy in comparison with conventional otoscopy and cerumen examination under a microscope for diagnosingO. cynotis in dogs. Thirty-five dogs were evaluated bilaterally for the presence of ear mites, using a veterinary otoscope (Gowlands®, a video otoscope (Welch Allyn® and the gold-standard technique of examination of swab-collected cerumen under a microscope. Each ear was considered to represent one sample, and 69 ears were examined, since one dog presented with one completely stenotic ear canal. Ear mites were diagnosed in 59.42% (41/69 through video otoscopy. The same 41 infested ear canals were detected by means of cerumen examination under a microscope, whereas conventional otoscopy was able to diagnose mites in only 39.13% (27/69. This difference was statistically significant (p < 0.001. Video otoscopy proved to be superior to conventional otoscopy, and equivalent to the gold standard for detection of O. cynotis in canine ear canals, and should be recommended for controlled trials on drug efficacy for treatment of canine otoacariasis.

  16. Creating and Editing Video to Accompany Manuscripts.

    Science.gov (United States)

    Gordon, Shayna L; Porto, Dennis A; Ozog, David M; Council, M Laurin

    2016-02-01

    The use of video can enhance the learning experience by demonstrating procedural techniques that are difficult to relay in writing. Several peer-reviewed journals allow publication of videos alongside articles to complement the written text. The purpose of this article is to instruct the dermatologic surgeon on how to create and edit a video using a smartphone, to accompany a article. The authors describe simple tips to optimize surgical videography. The video that accompanies this article further demonstrates the techniques described. Creating a surgical video requires little experience or equipment and can be completed in a modest amount of time. Making and editing a video to accompany a article can be accomplished by following the simple recommendations in this article. In addition, the increased use of video in dermatologic surgery education can enhance the learning opportunity.

  17. Video Game Accessibility: A Legal Approach

    Directory of Open Access Journals (Sweden)

    George Powers

    2015-02-01

    Full Text Available Video game accessibility may not seem of significance to some, and it may sound trivial to anyone who does not play video games. This assumption is false. With the digitalization of our culture, video games are an ever increasing part of our life. They contribute to peer to peer interactions, education, music and the arts. A video game can be created by hundreds of musicians and artists, and they can have production budgets that exceed modern blockbuster films. Inaccessible video games are analogous to movie theaters without closed captioning or accessible facilities. The movement to have accessible video games is small, unorganized and misdirected. Just like the other battles to make society accessible were accomplished through legislation and law, the battle for video game accessibility must be focused toward the law and not the market.

  18. CARACTERIZACION VOZ Y VIDEO

    Directory of Open Access Journals (Sweden)

    Octavio José Salcedo Parra

    2011-11-01

    Full Text Available La motivación para caracterizar el tráfico de voz y video está en la necesidad de las empresas proveedoras de servicio en mantener redes de transporte de información con capacidades acordes a los requerimientos de los usuarios.  Poder determinar en forma oportuna como los elementos técnicos que hacen parte de las redes afectan su desempeño, teniendo en cuenta que cada tipo de servicio es afectado en mayor o menor medida por dichos elementos dentro de los que tenemos el jitter, las demoras y las pérdidas de paquetes entre otros. El presente trabajo muestra varios casos de caracterización de tráfico tanto de voz como de video en las que se utilizan una diversidad de técnicas para diferentes tipos de servicio.

  19. Video in foreign language teaching

    Directory of Open Access Journals (Sweden)

    Joe Hambrook

    2013-02-01

    Full Text Available Much of the attention paid to video in foreign language teaching is focused upon a relatively small amount of commercially produced and distributed material. This paper briefly describes the development of this material in the EFLIESL field; looks at some current issues and concerns, and considers future possibilities with particular reference to computer assisted interactive video. Heelwat van die aandag wat video geniet as hulpmiddel by tweedetaalonderrig is toegespits op 'n relatief klein hoeveelheid kommersieel vervaardigde en verspreide materiaal. Hierdie artikel beskryf kortliks die ontwikkeling van bogenoemde materiaal waar dit Engels as tweede of vreemde taal betref. Verder word daar aandag gegee aan huidige tendense en toekomstige moontlikhede word oorweeg, met spesifieke verwysing na rekenaarondersteunde interaktiewe video.

  20. Intelligent video surveillance systems and technology

    CERN Document Server

    Ma, Yunqian

    2009-01-01

    From the streets of London to subway stations in New York City, hundreds of thousands of surveillance cameras ubiquitously collect hundreds of thousands of videos, often running 24/7. How can such vast volumes of video data be stored, analyzed, indexed, and searched? How can advanced video analysis and systems autonomously recognize people and detect targeted activities real-time? Collating and presenting the latest information Intelligent Video Surveillance: Systems and Technology explores these issues, from fundamentals principle to algorithmic design and system implementation.An Integrated

  1. NEI You Tube Videos: Amblyopia

    Science.gov (United States)

    ... YouTube Videos > NEI YouTube Videos: Amblyopia NEI YouTube Videos YouTube Videos Home Age-Related Macular Degeneration Amblyopia ... of Prematurity Science Spanish Videos Webinars NEI YouTube Videos: Amblyopia Embedded video for NEI YouTube Videos: Amblyopia ...

  2. Video surveillance using distance maps

    NARCIS (Netherlands)

    Schouten, Theo E.; Kuppens, Harco C.; van den Broek, Egon; Kehtarnavaz, Nasser; Laplante, Phillip A,

    2006-01-01

    Human vigilance is limited; hence, automatic motion and distance detection is one of the central issues in video surveillance. Hereby, many aspects are of importance, this paper specially addresses: efficiency, achieving real-time performance, accuracy, and robustness against various noise factors.

  3. Automated Video Surveillance for the Study of Marine Mammal Behavior and Cognition

    Directory of Open Access Journals (Sweden)

    Jeremy Karnowski

    2016-11-01

    Full Text Available Systems for detecting and tracking social marine mammals, including dolphins, can provide data to help explain their social dynamics, predict their behavior, and measure the impact of human interference. Data collected from video surveillance methods can be consistently and systematically sampled for studies of behavior, and frame-by-frame analyses can uncover insights impossible to observe from real-time, freely occurring natural behavior. Advances in boat-based, aerial, and underwater recording platforms provide opportunities to document the behavior of marine mammals and create massive datasets. The use of human experts to detect, track, identify individuals, and recognize activity in video demands significant time and financial investment. This paper examines automated methods designed to analyze large video corpora containing marine mammals. While research is converging on best solutions for some automated tasks, particularly detection and classification, many research domains are ripe for exploration.

  4. Ethnic Drama: Video-Texts and Study Guides.

    Science.gov (United States)

    Valletta, Clement, Ed.; And Others

    The document contains scripts, study guides, and discussion questions for two ethnic dramas suitable for ethnic studies at the secondary school level. The first, "A Glass Rose," an adaptation of the novel by Richard Bankowsky, depicts the hopes, dreams, and problems of a Polish immigrant family who reside in an ethnic neighborhood in an…

  5. Video Analytics

    DEFF Research Database (Denmark)

    include: re-identification, consumer behavior analysis, utilizing pupillary response for task difficulty measurement, logo detection, saliency prediction, classification of facial expressions, face recognition, face verification, age estimation, super-resolution, pose estimation, and pain recognition...

  6. Video Malware - Behavioral Analysis

    Directory of Open Access Journals (Sweden)

    Rajdeepsinh Dodia

    2015-04-01

    Full Text Available Abstract The counts of malware attacks exploiting the internet increasing day by day and has become a serious threat. The latest malware spreading out through the media players embedded using the video clip of funny in nature to lure the end users. Once it is executed and installed then the behavior of the malware is in the malware authors hand. The spread of the malware emulates through Internet USB drives sharing of the files and folders can be anything which makes presence concealed. The funny video named as it connected to the film celebrity where the malware variant was collected from the laptop of the terror outfit organization .It runs in the backend which it contains malicious code which steals the user sensitive information like banking credentials username amp password and send it to the remote host user called command amp control. The stealed data is directed to the email encapsulated in the malicious code. The potential malware will spread through the USB and other devices .In summary the analysis reveals the presence of malicious code in executable video file and its behavior.

  7. Video Design Games

    DEFF Research Database (Denmark)

    Smith, Rachel Charlotte; Christensen, Kasper Skov; Iversen, Ole Sejer

    We introduce Video Design Games to train educators in teaching design. The Video Design Game is a workshop format consisting of three rounds in which participants observe, reflect and generalize based on video snippets from their own practice. The paper reports on a Video Design Game workshop...

  8. Fragile watermarking scheme for H.264 video authentication

    Science.gov (United States)

    Wang, Chuen-Ching; Hsu, Yu-Chang

    2010-02-01

    A novel H.264 advanced video coding fragile watermarking method is proposed that enables the authenticity and integrity of the video streams to be verified. The effectiveness of the proposed scheme is demonstrated by way of experimental simulations. The results show that by embedding the watermark information in the last nonzero-quantized coefficient in each discrete cosine transform block, the proposed scheme induces no more than a minor distortion of the video content. In addition, we show that the proposed scheme is able to detect unauthorized changes in the watermarked video content without the original video. The experimental results demonstrate the feasibility of the proposed video authentication system.

  9. Using Learning Styles and Viewing Styles in Streaming Video

    Science.gov (United States)

    de Boer, Jelle; Kommers, Piet A. M.; de Brock, Bert

    2011-01-01

    Improving the effectiveness of learning when students observe video lectures becomes urgent with the rising advent of (web-based) video materials. Vital questions are how students differ in their learning preferences and what patterns in viewing video can be detected in log files. Our experiments inventory students' viewing patterns while watching…

  10. Using learning styles and viewing styles in streaming video

    NARCIS (Netherlands)

    de Boer, Jelle; Kommers, Petrus A.M.; de Brock, Bert

    2011-01-01

    Improving the effectiveness of learning when students observe video lectures becomes urgent with the rising advent of (web-based) video materials. Vital questions are how students differ in their learning preferences and what patterns in viewing video can be detected in log files. Our experiments

  11. Using learning styles and viewing styles in streaming video

    NARCIS (Netherlands)

    de Boer, Jelle; Kommers, Piet A. M.; de Brock, Bert

    Improving the effectiveness of learning when students observe video lectures becomes urgent with the rising advent of (web-based) video materials. Vital questions are how students differ in their learning preferences and what patterns in viewing video can be detected in log files. Our experiments

  12. Indexed Captioned Searchable Videos: A Learning Companion for STEM Coursework

    Science.gov (United States)

    Tuna, Tayfun; Subhlok, Jaspal; Barker, Lecia; Shah, Shishir; Johnson, Olin; Hovey, Christopher

    2017-02-01

    Videos of classroom lectures have proven to be a popular and versatile learning resource. A key shortcoming of the lecture video format is accessing the content of interest hidden in a video. This work meets this challenge with an advanced video framework featuring topical indexing, search, and captioning (ICS videos). Standard optical character recognition (OCR) technology was enhanced with image transformations for extraction of text from video frames to support indexing and search. The images and text on video frames is analyzed to divide lecture videos into topical segments. The ICS video player integrates indexing, search, and captioning in video playback providing instant access to the content of interest. This video framework has been used by more than 70 courses in a variety of STEM disciplines and assessed by more than 4000 students. Results presented from the surveys demonstrate the value of the videos as a learning resource and the role played by videos in a students learning process. Survey results also establish the value of indexing and search features in a video platform for education. This paper reports on the development and evaluation of ICS videos framework and over 5 years of usage experience in several STEM courses.

  13. Characterization of social video

    Science.gov (United States)

    Ostrowski, Jeffrey R.; Sarhan, Nabil J.

    2009-01-01

    The popularity of social media has grown dramatically over the World Wide Web. In this paper, we analyze the video popularity distribution of well-known social video websites (YouTube, Google Video, and the AOL Truveo Video Search engine) and characterize their workload. We identify trends in the categories, lengths, and formats of those videos, as well as characterize the evolution of those videos over time. We further provide an extensive analysis and comparison of video content amongst the main regions of the world.

  14. The utility of video capsule endoscopy (VCE) for the detection of small bowel bleeding arising from benign and malignant tumors: report of two cases.

    Science.gov (United States)

    Van Heukelom, Jesse G; Gutnik, Steven H

    2011-10-01

    Video capsule endoscopy (VCE) has become a first-line diagnostic tool for the diagnosis of small bowel bleeding. Prior to VCE, procedures to visualize the site of small intestinal bleeding were rudimentary and frequently delivered inconclusive findings. VCE allows for improved visualization, resulting in more accurate identification of benign and malignant tumors of the small bowel. VCE provides the opportunity for prompt identification while allowing for precise surgical excision of the involved bowel, with maximum salvage of normal tissue. We report two clinical cases of small bowel tumors diagnosed by using video capsule endoscopy. The pathology showed one lesion to be a lipoma and the other a carcinoid tumor. Both cases presented in similar fashions as intestinal hemorrhage, yet one lesion was benign and the other malignant.

  15. Scene change detection based on multimodal integration

    Science.gov (United States)

    Zhu, Yingying; Zhou, Dongru

    2003-09-01

    Scene change detection is an essential step to automatic and content-based video indexing, retrieval and browsing. In this paper, a robust scene change detection and classification approach is presented, which analyzes audio, visual and textual sources and accounts for their inter-relations and coincidence to semantically identify and classify video scenes. Audio analysis focuses on the segmentation of audio stream into four types of semantic data such as silence, speech, music and environmental sound. Further processing on speech segments aims at locating speaker changes. Video analysis partitions visual stream into shots. Text analysis can provide a supplemental source of clues for scene classification and indexing information. We integrate the video and audio analysis results to identify video scenes and use the text information detected by the video OCR technology or derived from transcripts available to refine scene classification. Results from single source segmentation are in some cases suboptimal. By combining visual, aural features adn the accessorial text information, the scence extraction accuracy is enhanced, and more semantic segmentations are developed. Experimental results are proven to rather promising.

  16. An Automatic Multimedia Content Summarization System for Video Recommendation

    Science.gov (United States)

    Yang, Jie Chi; Huang, Yi Ting; Tsai, Chi Cheng; Chung, Ching I.; Wu, Yu Chieh

    2009-01-01

    In recent years, using video as a learning resource has received a lot of attention and has been successfully applied to many learning activities. In comparison with text-based learning, video learning integrates more multimedia resources, which usually motivate learners more than texts. However, one of the major limitations of video learning is…

  17. A low false negative filter for detecting rare bird species from short video segments using a probable observation data set-based EKF method.

    Science.gov (United States)

    Song, Dezhen; Xu, Yiliang

    2010-09-01

    We report a new filter to assist the search for rare bird species. Since a rare bird only appears in front of a camera with very low occurrence (e.g., less than ten times per year) for very short duration (e.g., less than a fraction of a second), our algorithm must have a very low false negative rate. We verify the bird body axis information with the known bird flying dynamics from the short video segment. Since a regular extended Kalman filter (EKF) cannot converge due to high measurement error and limited data, we develop a novel probable observation data set (PODS)-based EKF method. The new PODS-EKF searches the measurement error range for all probable observation data that ensures the convergence of the corresponding EKF in short time frame. The algorithm has been extensively tested using both simulated inputs and real video data of four representative bird species. In the physical experiments, our algorithm has been tested on rock pigeons and red-tailed hawks with 119 motion sequences. The area under the ROC curve is 95.0%. During the one-year search of ivory-billed woodpeckers, the system reduces the raw video data of 29.41 TB to only 146.7 MB (reduction rate 99.9995%).

  18. Video visual analytics

    OpenAIRE

    Höferlin, Markus Johannes

    2013-01-01

    The amount of video data recorded world-wide is tremendously growing and has already reached hardly manageable dimensions. It originates from a wide range of application areas, such as surveillance, sports analysis, scientific video analysis, surgery documentation, and entertainment, and its analysis represents one of the challenges in computer science. The vast amount of video data renders manual analysis by watching the video data impractical. However, automatic evaluation of video material...

  19. Veterans Crisis Line: Videos About Reaching out for Help

    Medline Plus

    Full Text Available ... Call see more videos from Veterans Health Administration I'm Good. But are you ready to listen? ... PSA see more videos from Veterans Health Administration I am A Veteran Family/Friend Active Duty/Reserve ...

  20. Veterans Crisis Line: Videos About Reaching out for Help

    Medline Plus

    Full Text Available ... in crisis, find a facility near you. Spread the Word Download logos, Web ads, and materials and ... Videos from Veterans Health Administration Watch additional videos about getting help. Be There: Help Save a Life ...

  1. Veterans Crisis Line: Videos About Reaching out for Help

    Medline Plus

    Full Text Available ... in crisis, find a facility near you. Spread the Word Download logos, Web ads, and materials and ... Administration Watch additional videos about getting help. Be There: Help Save a Life see more videos from ...

  2. Veterans Crisis Line: Videos About Reaching out for Help

    Medline Plus

    Full Text Available ... in crisis, find a facility near you. Spread the Word Download logos, Web ads, and materials and ... videos about getting help. Be There: Help Save a Life see more videos from Veterans Health Administration ...

  3. Veterans Crisis Line: Videos About Reaching out for Help

    Medline Plus

    Full Text Available ... Resources Spread the Word Videos Homeless Resources Additional Information Make the Connection Get Help When To Call ... Suicide Spread the Word Videos Homeless Resources Additional Information Make the Connection Resource Locator If you or ...

  4. Know Stroke: Know the Signs, Act in Time Video

    Medline Plus

    Full Text Available ... Stroke Materials  » Loading the player... Video Transcript Weakness on one Side. Trouble Speaking. Trouble ... Stroke: Know the Signs. Act in Time. Ambulance Video Loading the player... This PSA alerts audiences about ...

  5. Veterans Crisis Line: Videos About Reaching out for Help

    Medline Plus

    Full Text Available ... from Veterans Health Administration Be There: Help Save a Life see more videos from Veterans Health Administration ... more videos from Veterans Health Administration I am A Veteran Family/Friend Active Duty/Reserve and Guard ...

  6. Veterans Crisis Line: Videos About Reaching out for Help

    Medline Plus

    Full Text Available ... Involved Crisis Centers About Be There Show You Care Find Resources Graphic Generator Toolkit Signs of Crisis ... out for help. Bittersweet More Videos from Veterans Health Administration Watch additional videos about getting help. Be ...

  7. Veterans Crisis Line: Videos About Reaching out for Help

    Medline Plus

    Full Text Available ... in crisis, find a facility near you. Spread the Word Download logos, Web ads, and materials and ... Administration Watch additional videos about getting help. Behind the Scenes see more videos from Veterans Health Administration ...

  8. Know Stroke: Know the Signs, Act in Time Video

    Medline Plus

    Full Text Available ... Know Stroke Home » Stroke Materials » Loading the player... Video Transcript Weakness on one Side. Trouble Speaking. Trouble ... Stroke: Know the Signs. Act in Time. Ambulance Video Loading the player... This PSA alerts audiences about ...

  9. A Blind Video Watermarking Scheme Robust To Frame Attacks Combined With MPEG2 Compression

    Directory of Open Access Journals (Sweden)

    C. Cruz-Ramos

    2010-12-01

    Full Text Available ABSTRACTIn this paper, we propose a robust digital video watermarking scheme with completely blind extraction process wherethe original video data, original watermark or any other information derivative of them are not required in order toretrieve the embedded watermark. The proposed algorithm embeds 2D binary visually recognizable patterns such ascompany trademarks and owner’s logotype, etc., in the DWT domain of the video frames for copyright protection.Before the embedding process, only two numerical keys are required to transform the watermark data into a noise-likepattern using the chaotic mixing method which helps to increase the security. The main advantages of the proposedscheme are its completely blind detection scheme, robustness against common video attacks, combined attacks andits low complexity implementation. The combined attacks consist of MPEG-2 compression and common video attackssuch as noise contamination, collusion attacks, frame dropping and swapping. Extensive simulation results also showthat the watermark imperceptibility and robustness outperform other previously reported methods. The extractedwatermark data from the watermarked video sequences is clear enough even after the watermarked video hadsuffered from several attacks.

  10. An Attention-Information-Based Spatial Adaptation Framework for Browsing Videos via Mobile Devices

    Directory of Open Access Journals (Sweden)

    Li Houqiang

    2007-01-01

    Full Text Available With the growing popularity of personal digital assistant devices and smart phones, more and more consumers are becoming quite enthusiastic to appreciate videos via mobile devices. However, limited display size of the mobile devices has been imposing significant barriers for users to enjoy browsing high-resolution videos. In this paper, we present an attention-information-based spatial adaptation framework to address this problem. The whole framework includes two major parts: video content generation and video adaptation system. During video compression, the attention information in video sequences will be detected using an attention model and embedded into bitstreams with proposed supplement-enhanced information (SEI structure. Furthermore, we also develop an innovative scheme to adaptively adjust quantization parameters in order to simultaneously improve the quality of overall encoding and the quality of transcoding the attention areas. When the high-resolution bitstream is transmitted to mobile users, a fast transcoding algorithm we developed earlier will be applied to generate a new bitstream for attention areas in frames. The new low-resolution bitstream containing mostly attention information, instead of the high-resolution one, will be sent to users for display on the mobile devices. Experimental results show that the proposed spatial adaptation scheme is able to improve both subjective and objective video qualities.

  11. Improving a full-text search engine: the importance of negation detection and family history context to identify cases in a biomedical data warehouse.

    Science.gov (United States)

    Garcelon, Nicolas; Neuraz, Antoine; Benoit, Vincent; Salomon, Rémi; Burgun, Anita

    2017-05-01

    The repurposing of electronic health records (EHRs) can improve clinical and genetic research for rare diseases. However, significant information in rare disease EHRs is embedded in the narrative reports, which contain many negated clinical signs and family medical history. This paper presents a method to detect family history and negation in narrative reports and evaluates its impact on selecting populations from a clinical data warehouse (CDW). We developed a pipeline to process 1.6 million reports from multiple sources. This pipeline is part of the load process of the Necker Hospital CDW. We identified patients with "Lupus and diarrhea," "Crohn's and diabetes," and "NPHP1" from the CDW. The overall precision, recall, specificity, and F-measure were 0.85, 0.98, 0.93, and 0.91, respectively. The proposed method generates a highly accurate identification of cases from a CDW of rare disease EHRs.

  12. Advanced real-time manipulation of video streams

    CERN Document Server

    Herling, Jan

    2014-01-01

    Diminished Reality is a new fascinating technology that removes real-world content from live video streams. This sensational live video manipulation actually removes real objects and generates a coherent video stream in real-time. Viewers cannot detect modified content. Existing approaches are restricted to moving objects and static or almost static cameras and do not allow real-time manipulation of video content. Jan Herling presents a new and innovative approach for real-time object removal with arbitrary camera movements.

  13. Video Game Training and the Reward System

    Directory of Open Access Journals (Sweden)

    Robert C. Lorenz

    2015-02-01

    Full Text Available Video games contain elaborate reinforcement and reward schedules that have the potential to maximize motivation. Neuroimaging studies suggest that video games might have an influence on the reward system. However, it is not clear whether reward-related properties represent a precondition, which biases an individual towards playing video games, or if these changes are the result of playing video games. Therefore, we conducted a longitudinal study to explore reward-related functional predictors in relation to video gaming experience as well as functional changes in the brain in response to video game training.Fifty healthy participants were randomly assigned to a video game training (TG or control group (CG. Before and after training/control period, functional magnetic resonance imaging (fMRI was conducted using a non-video game related reward task.At pretest, both groups showed strongest activation in ventral striatum (VS during reward anticipation. At posttest, the TG showed very similar VS activity compared to pretest. In the CG, the VS activity was significantly attenuated.This longitudinal study revealed that video game training may preserve reward responsiveness in the ventral striatum in a retest situation over time. We suggest that video games are able to keep striatal responses to reward flexible, a mechanism which might be of critical value for applications such as therapeutic cognitive training.

  14. Video Analytics for Business Intelligence

    CERN Document Server

    Porikli, Fatih; Xiang, Tao; Gong, Shaogang

    2012-01-01

    Closed Circuit TeleVision (CCTV) cameras have been increasingly deployed pervasively in public spaces including retail centres and shopping malls. Intelligent video analytics aims to automatically analyze content of massive amount of public space video data and has been one of the most active areas of computer vision research in the last two decades. Current focus of video analytics research has been largely on detecting alarm events and abnormal behaviours for public safety and security applications. However, increasingly CCTV installations have also been exploited for gathering and analyzing business intelligence information, in order to enhance marketing and operational efficiency. For example, in retail environments, surveillance cameras can be utilised to collect statistical information about shopping behaviour and preference for marketing (e.g., how many people entered a shop; how many females/males or which age groups of people showed interests to a particular product; how long did they stay in the sho...

  15. Are Female Applicants Disadvantaged in National Institutes of Health Peer Review? Combining Algorithmic Text Mining and Qualitative Methods to Detect Evaluative Differences in R01 Reviewers' Critiques.

    Science.gov (United States)

    Magua, Wairimu; Zhu, Xiaojin; Bhattacharya, Anupama; Filut, Amarette; Potvien, Aaron; Leatherberry, Renee; Lee, You-Geon; Jens, Madeline; Malikireddy, Dastagiri; Carnes, Molly; Kaatz, Anna

    2017-05-01

    Women are less successful than men in renewing R01 grants from the National Institutes of Health. Continuing to probe text mining as a tool to identify gender bias in peer review, we used algorithmic text mining and qualitative analysis to examine a sample of critiques from men's and women's R01 renewal applications previously analyzed by counting and comparing word categories. We analyzed 241 critiques from 79 Summary Statements for 51 R01 renewals awarded to 45 investigators (64% male, 89% white, 80% PhD) at the University of Wisconsin-Madison between 2010 and 2014. We used latent Dirichlet allocation to discover evaluative "topics" (i.e., words that co-occur with high probability). We then qualitatively examined the context in which evaluative words occurred for male and female investigators. We also examined sex differences in assigned scores controlling for investigator productivity. Text analysis results showed that male investigators were described as "leaders" and "pioneers" in their "fields," with "highly innovative" and "highly significant research." By comparison, female investigators were characterized as having "expertise" and working in "excellent" environments. Applications from men received significantly better priority, approach, and significance scores, which could not be accounted for by differences in productivity. Results confirm our previous analyses suggesting that gender stereotypes operate in R01 grant peer review. Reviewers may more easily view male than female investigators as scientific leaders with significant and innovative research, and score their applications more competitively. Such implicit bias may contribute to sex differences in award rates for R01 renewals.

  16. Video Playback Modifications for a DSpace Repository

    Directory of Open Access Journals (Sweden)

    Keith Gilbertson

    2016-01-01

    Full Text Available This paper focuses on modifications to an institutional repository system using the open source DSpace software to support playback of digital videos embedded within item pages. The changes were made in response to the formation and quick startup of an event capture group within the library that was charged with creating and editing video recordings of library events and speakers. This paper specifically discusses the selection of video formats, changes to the visual theme of the repository to allow embedded playback and captioning support, and modifications and bug fixes to the file downloading subsystem to enable skip-ahead playback of videos via byte-range requests. This paper also describes workflows for transcoding videos in the required formats, creating captions, and depositing videos into the repository.

  17. Implementation of video surveillance in crime control

    Directory of Open Access Journals (Sweden)

    Kovačević-Lepojević Marina

    2012-01-01

    Full Text Available Modern trends in crime control include a variety of technological innovations, including video surveillance systems. The aim of this paper is to review the implementation of video surveillance in contemporary context, considering fundamental theoretical aspects, the legislation and the effectiveness in controlling crime. While considering the theoretical source of ideas on the implementation of video surveillance, priority was given to the concept of situational prevention that focuses on the contextual factors of crime. Capacities for the implementation of video surveillance in Serbia are discussed based on the analysis of the relevant international and domestic legislation, the shortcomings in regulation of this area and possible solutions. Special attention was paid to the effectiveness of video surveillance in public places, in schools and prisons. Starting from the results of studies of video surveillance effectiveness, strengths and weaknesses of these measures and recommendations for improving practice were discussed.

  18. Statistical Analysis of Video Frame Size Distribution Originating from Scalable Video Codec (SVC

    Directory of Open Access Journals (Sweden)

    Sima Ahmadpour

    2017-01-01

    Full Text Available Designing an effective and high performance network requires an accurate characterization and modeling of network traffic. The modeling of video frame sizes is normally applied in simulation studies and mathematical analysis and generating streams for testing and compliance purposes. Besides, video traffic assumed as a major source of multimedia traffic in future heterogeneous network. Therefore, the statistical distribution of video data can be used as the inputs for performance modeling of networks. The finding of this paper comprises the theoretical definition of distribution which seems to be relevant to the video trace in terms of its statistical properties and finds the best distribution using both the graphical method and the hypothesis test. The data set used in this article consists of layered video traces generating from Scalable Video Codec (SVC video compression technique of three different movies.

  19. Quality of Experience Assessment of Video Quality in Social Clouds

    Directory of Open Access Journals (Sweden)

    Asif Ali Laghari

    2017-01-01

    Full Text Available Video sharing on social clouds is popular among the users around the world. High-Definition (HD videos have big file size so the storing in cloud storage and streaming of videos with high quality from cloud to the client are a big problem for service providers. Social clouds compress the videos to save storage and stream over slow networks to provide quality of service (QoS. Compression of video decreases the quality compared to original video and parameters are changed during the online play as well as after download. Degradation of video quality due to compression decreases the quality of experience (QoE level of end users. To assess the QoE of video compression, we conducted subjective (QoE experiments by uploading, sharing, and playing videos from social clouds. Three popular social clouds, Facebook, Tumblr, and Twitter, were selected to upload and play videos online for users. The QoE was recorded by using questionnaire given to users to provide their experience about the video quality they perceive. Results show that Facebook and Twitter compressed HD videos more as compared to other clouds. However, Facebook gives a better quality of compressed videos compared to Twitter. Therefore, users assigned low ratings for Twitter for online video quality compared to Tumblr that provided high-quality online play of videos with less compression.

  20. Video Segmentation Using Fast Marching and Region Growing Algorithms

    Directory of Open Access Journals (Sweden)

    Eftychis Sifakis

    2002-04-01

    Full Text Available The algorithm presented in this paper is comprised of three main stages: (1 classification of the image sequence and, in the case of a moving camera, parametric motion estimation, (2 change detection having as reference a fixed frame, an appropriately selected frame or a displaced frame, and (3 object localization using local colour features. The image sequence classification is based on statistical tests on the frame difference. The change detection module uses a two-label fast marching algorithm. Finally, the object localization uses a region growing algorithm based on the colour similarity. Video object segmentation results are shown using the COST 211 data set.