Hierarchical vs non-hierarchical audio indexation and classification for video genres

NASA Astrophysics Data System (ADS)

Dammak, Nouha; BenAyed, Yassine

2018-04-01

In this paper, Support Vector Machines (SVMs) are used for segmenting and indexing video genres based on only audio features extracted at block level, which has a prominent asset by capturing local temporal information. The main contribution of our study is to show the wide effect on the classification accuracies while using an hierarchical categorization structure based on Mel Frequency Cepstral Coefficients (MFCC) audio descriptor. In fact, the classification consists in three common video genres: sports videos, music clips and news scenes. The sub-classification may divide each genre into several multi-speaker and multi-dialect sub-genres. The validation of this approach was carried out on over 360 minutes of video span yielding a classification accuracy of over 99%.

Seeing is believing: video classification for computed tomographic colonography using multiple-instance learning.

PubMed

Wang, Shijun; McKenna, Matthew T; Nguyen, Tan B; Burns, Joseph E; Petrick, Nicholas; Sahiner, Berkman; Summers, Ronald M

2012-05-01

In this paper, we present development and testing results for a novel colonic polyp classification method for use as part of a computed tomographic colonography (CTC) computer-aided detection (CAD) system. Inspired by the interpretative methodology of radiologists using 3-D fly-through mode in CTC reading, we have developed an algorithm which utilizes sequences of images (referred to here as videos) for classification of CAD marks. For each CAD mark, we created a video composed of a series of intraluminal, volume-rendered images visualizing the detection from multiple viewpoints. We then framed the video classification question as a multiple-instance learning (MIL) problem. Since a positive (negative) bag may contain negative (positive) instances, which in our case depends on the viewing angles and camera distance to the target, we developed a novel MIL paradigm to accommodate this class of problems. We solved the new MIL problem by maximizing a L2-norm soft margin using semidefinite programming, which can optimize relevant parameters automatically. We tested our method by analyzing a CTC data set obtained from 50 patients from three medical centers. Our proposed method showed significantly better performance compared with several traditional MIL methods.

Seeing is Believing: Video Classification for Computed Tomographic Colonography Using Multiple-Instance Learning

PubMed Central

Wang, Shijun; McKenna, Matthew T.; Nguyen, Tan B.; Burns, Joseph E.; Petrick, Nicholas; Sahiner, Berkman

2012-01-01

In this paper we present development and testing results for a novel colonic polyp classification method for use as part of a computed tomographic colonography (CTC) computer-aided detection (CAD) system. Inspired by the interpretative methodology of radiologists using 3D fly-through mode in CTC reading, we have developed an algorithm which utilizes sequences of images (referred to here as videos) for classification of CAD marks. For each CAD mark, we created a video composed of a series of intraluminal, volume-rendered images visualizing the detection from multiple viewpoints. We then framed the video classification question as a multiple-instance learning (MIL) problem. Since a positive (negative) bag may contain negative (positive) instances, which in our case depends on the viewing angles and camera distance to the target, we developed a novel MIL paradigm to accommodate this class of problems. We solved the new MIL problem by maximizing a L2-norm soft margin using semidefinite programming, which can optimize relevant parameters automatically. We tested our method by analyzing a CTC data set obtained from 50 patients from three medical centers. Our proposed method showed significantly better performance compared with several traditional MIL methods. PMID:22552333

Age and violent-content labels make video games forbidden fruits for youth.

PubMed

Bijvank, Marije Nije; Konijn, Elly A; Bushman, Brad J; Roelofsma, Peter H M P

2009-03-01

To protect minors from exposure to video games with objectionable content (eg, violence and sex), the Pan European Game Information developed a classification system for video games (eg, 18+). We tested the hypothesis that this classification system may actually increase the attractiveness of games for children younger than the age rating. Participants were 310 Dutch youth. The design was a 3 (age group: 7-8, 12-13, and 16-17 years) x 2 (participant gender) x 7 (label: 7+, 12+, 16+, 18+, violence, no violence, or no label control) x 2 (game description: violent or nonviolent) mixed factorial. The first 2 factors were between subjects, whereas the last 2 factors were within subjects. Three personality traits (ie, reactance, trait aggressiveness, and sensation seeking) were also included in the analyses. Participants read fictitious video game descriptions and rated how much they wanted to play each game. Results revealed that restrictive age labels and violent-content labels increased the attractiveness of video games for all of the age groups (even 7- to 8-year-olds and girls). Although the Pan European Game Information system was developed to protect youth from objectionable content, this system actually makes such games forbidden fruits. Pediatricians should be aware of this forbidden-fruit effect, because video games with objectionable content can have harmful effects on children and adolescents.

Tiny videos: a large data set for nonparametric video retrieval and frame classification.

PubMed

Karpenko, Alexandre; Aarabi, Parham

2011-03-01

In this paper, we present a large database of over 50,000 user-labeled videos collected from YouTube. We develop a compact representation called "tiny videos" that achieves high video compression rates while retaining the overall visual appearance of the video as it varies over time. We show that frame sampling using affinity propagation-an exemplar-based clustering algorithm-achieves the best trade-off between compression and video recall. We use this large collection of user-labeled videos in conjunction with simple data mining techniques to perform related video retrieval, as well as classification of images and video frames. The classification results achieved by tiny videos are compared with the tiny images framework [24] for a variety of recognition tasks. The tiny images data set consists of 80 million images collected from the Internet. These are the largest labeled research data sets of videos and images available to date. We show that tiny videos are better suited for classifying scenery and sports activities, while tiny images perform better at recognizing objects. Furthermore, we demonstrate that combining the tiny images and tiny videos data sets improves classification precision in a wider range of categories.

An optimized video system for augmented reality in endodontics: a feasibility study.

PubMed

Bruellmann, D D; Tjaden, H; Schwanecke, U; Barth, P

2013-03-01

We propose an augmented reality system for the reliable detection of root canals in video sequences based on a k-nearest neighbor color classification and introduce a simple geometric criterion for teeth. The new software was implemented using C++, Qt, and the image processing library OpenCV. Teeth are detected in video images to restrict the segmentation of the root canal orifices by using a k-nearest neighbor algorithm. The location of the root canal orifices were determined using Euclidean distance-based image segmentation. A set of 126 human teeth with known and verified locations of the root canal orifices was used for evaluation. The software detects root canals orifices for automatic classification of the teeth in video images and stores location and size of the found structures. Overall 287 of 305 root canals were correctly detected. The overall sensitivity was about 94 %. Classification accuracy for molars ranged from 65.0 to 81.2 % and from 85.7 to 96.7 % for premolars. The realized software shows that observations made in anatomical studies can be exploited to automate real-time detection of root canal orifices and tooth classification with a software system. Automatic storage of location, size, and orientation of the found structures with this software can be used for future anatomical studies. Thus, statistical tables with canal locations will be derived, which can improve anatomical knowledge of the teeth to alleviate root canal detection in the future. For this purpose the software is freely available at: http://www.dental-imaging.zahnmedizin.uni-mainz.de/.

Real-time video analysis for retail stores

NASA Astrophysics Data System (ADS)

Hassan, Ehtesham; Maurya, Avinash K.

2015-03-01

With the advancement in video processing technologies, we can capture subtle human responses in a retail store environment which play decisive role in the store management. In this paper, we present a novel surveillance video based analytic system for retail stores targeting localized and global traffic estimate. Development of an intelligent system for human traffic estimation in real-life poses a challenging problem because of the variation and noise involved. In this direction, we begin with a novel human tracking system by an intelligent combination of motion based and image level object detection. We demonstrate the initial evaluation of this approach on available standard dataset yielding promising result. Exact traffic estimate in a retail store require correct separation of customers from service providers. We present a role based human classification framework using Gaussian mixture model for this task. A novel feature descriptor named graded colour histogram is defined for object representation. Using, our role based human classification and tracking system, we have defined a novel computationally efficient framework for two types of analytics generation i.e., region specific people count and dwell-time estimation. This system has been extensively evaluated and tested on four hours of real-life video captured from a retail store.

Hierarchical structure for audio-video based semantic classification of sports video sequences

NASA Astrophysics Data System (ADS)

Kolekar, M. H.; Sengupta, S.

2005-07-01

A hierarchical structure for sports event classification based on audio and video content analysis is proposed in this paper. Compared to the event classifications in other games, those of cricket are very challenging and yet unexplored. We have successfully solved cricket video classification problem using a six level hierarchical structure. The first level performs event detection based on audio energy and Zero Crossing Rate (ZCR) of short-time audio signal. In the subsequent levels, we classify the events based on video features using a Hidden Markov Model implemented through Dynamic Programming (HMM-DP) using color or motion as a likelihood function. For some of the game-specific decisions, a rule-based classification is also performed. Our proposed hierarchical structure can easily be applied to any other sports. Our results are very promising and we have moved a step forward towards addressing semantic classification problems in general.

Lameness detection in dairy cattle: single predictor v. multivariate analysis of image-based posture processing and behaviour and performance sensing.

PubMed

Van Hertem, T; Bahr, C; Schlageter Tello, A; Viazzi, S; Steensels, M; Romanini, C E B; Lokhorst, C; Maltz, E; Halachmi, I; Berckmans, D

2016-09-01

The objective of this study was to evaluate if a multi-sensor system (milk, activity, body posture) was a better classifier for lameness than the single-sensor-based detection models. Between September 2013 and August 2014, 3629 cow observations were collected on a commercial dairy farm in Belgium. Human locomotion scoring was used as reference for the model development and evaluation. Cow behaviour and performance was measured with existing sensors that were already present at the farm. A prototype of three-dimensional-based video recording system was used to quantify automatically the back posture of a cow. For the single predictor comparisons, a receiver operating characteristics curve was made. For the multivariate detection models, logistic regression and generalized linear mixed models (GLMM) were developed. The best lameness classification model was obtained by the multi-sensor analysis (area under the receiver operating characteristics curve (AUC)=0.757±0.029), containing a combination of milk and milking variables, activity and gait and posture variables from videos. Second, the multivariate video-based system (AUC=0.732±0.011) performed better than the multivariate milk sensors (AUC=0.604±0.026) and the multivariate behaviour sensors (AUC=0.633±0.018). The video-based system performed better than the combined behaviour and performance-based detection model (AUC=0.669±0.028), indicating that it is worthwhile to consider a video-based lameness detection system, regardless the presence of other existing sensors in the farm. The results suggest that Θ2, the feature variable for the back curvature around the hip joints, with an AUC of 0.719 is the best single predictor variable for lameness detection based on locomotion scoring. In general, this study showed that the video-based back posture monitoring system is outperforming the behaviour and performance sensing techniques for locomotion scoring-based lameness detection. A GLMM with seven specific variables (walking speed, back posture measurement, daytime activity, milk yield, lactation stage, milk peak flow rate and milk peak conductivity) is the best combination of variables for lameness classification. The accuracy on four-level lameness classification was 60.3%. The accuracy improved to 79.8% for binary lameness classification. The binary GLMM obtained a sensitivity of 68.5% and a specificity of 87.6%, which both exceed the sensitivity (52.1%±4.7%) and specificity (83.2%±2.3%) of the multi-sensor logistic regression model. This shows that the repeated measures analysis in the GLMM, taking into account the individual history of the animal, outperforms the classification when thresholds based on herd level (a statistical population) are used.

The Effect of Normalization in Violence Video Classification Performance

NASA Astrophysics Data System (ADS)

Ali, Ashikin; Senan, Norhalina

2017-08-01

Basically, data pre-processing is an important part of data mining. Normalization is a pre-processing stage for any type of problem statement, especially in video classification. Challenging problems that arises in video classification is because of the heterogeneous content, large variations in video quality and complex semantic meanings of the concepts involved. Therefore, to regularize this problem, it is thoughtful to ensure normalization or basically involvement of thorough pre-processing stage aids the robustness of classification performance. This process is to scale all the numeric variables into certain range to make it more meaningful for further phases in available data mining techniques. Thus, this paper attempts to examine the effect of 2 normalization techniques namely Min-max normalization and Z-score in violence video classifications towards the performance of classification rate using Multi-layer perceptron (MLP) classifier. Using Min-Max Normalization range of [0,1] the result shows almost 98% of accuracy, meanwhile Min-Max Normalization range of [-1,1] accuracy is 59% and for Z-score the accuracy is 50%.

Otitis Media Diagnosis for Developing Countries Using Tympanic Membrane Image-Analysis.

PubMed

Myburgh, Hermanus C; van Zijl, Willemien H; Swanepoel, DeWet; Hellström, Sten; Laurent, Claude

2016-03-01

Otitis media is one of the most common childhood diseases worldwide, but because of lack of doctors and health personnel in developing countries it is often misdiagnosed or not diagnosed at all. This may lead to serious, and life-threatening complications. There is, thus a need for an automated computer based image-analyzing system that could assist in making accurate otitis media diagnoses anywhere. A method for automated diagnosis of otitis media is proposed. The method uses image-processing techniques to classify otitis media. The system is trained using high quality pre-assessed images of tympanic membranes, captured by digital video-otoscopes, and classifies undiagnosed images into five otitis media categories based on predefined signs. Several verification tests analyzed the classification capability of the method. An accuracy of 80.6% was achieved for images taken with commercial video-otoscopes, while an accuracy of 78.7% was achieved for images captured on-site with a low cost custom-made video-otoscope. The high accuracy of the proposed otitis media classification system compares well with the classification accuracy of general practitioners and pediatricians (~64% to 80%) using traditional otoscopes, and therefore holds promise for the future in making automated diagnosis of otitis media in medically underserved populations.

Otitis Media Diagnosis for Developing Countries Using Tympanic Membrane Image-Analysis

PubMed Central

Myburgh, Hermanus C.; van Zijl, Willemien H.; Swanepoel, DeWet; Hellström, Sten; Laurent, Claude

2016-01-01

Background Otitis media is one of the most common childhood diseases worldwide, but because of lack of doctors and health personnel in developing countries it is often misdiagnosed or not diagnosed at all. This may lead to serious, and life-threatening complications. There is, thus a need for an automated computer based image-analyzing system that could assist in making accurate otitis media diagnoses anywhere. Methods A method for automated diagnosis of otitis media is proposed. The method uses image-processing techniques to classify otitis media. The system is trained using high quality pre-assessed images of tympanic membranes, captured by digital video-otoscopes, and classifies undiagnosed images into five otitis media categories based on predefined signs. Several verification tests analyzed the classification capability of the method. Findings An accuracy of 80.6% was achieved for images taken with commercial video-otoscopes, while an accuracy of 78.7% was achieved for images captured on-site with a low cost custom-made video-otoscope. Interpretation The high accuracy of the proposed otitis media classification system compares well with the classification accuracy of general practitioners and pediatricians (~ 64% to 80%) using traditional otoscopes, and therefore holds promise for the future in making automated diagnosis of otitis media in medically underserved populations. PMID:27077122

Video event classification and image segmentation based on noncausal multidimensional hidden Markov models.

PubMed

Ma, Xiang; Schonfeld, Dan; Khokhar, Ashfaq A

2009-06-01

In this paper, we propose a novel solution to an arbitrary noncausal, multidimensional hidden Markov model (HMM) for image and video classification. First, we show that the noncausal model can be solved by splitting it into multiple causal HMMs and simultaneously solving each causal HMM using a fully synchronous distributed computing framework, therefore referred to as distributed HMMs. Next we present an approximate solution to the multiple causal HMMs that is based on an alternating updating scheme and assumes a realistic sequential computing framework. The parameters of the distributed causal HMMs are estimated by extending the classical 1-D training and classification algorithms to multiple dimensions. The proposed extension to arbitrary causal, multidimensional HMMs allows state transitions that are dependent on all causal neighbors. We, thus, extend three fundamental algorithms to multidimensional causal systems, i.e., 1) expectation-maximization (EM), 2) general forward-backward (GFB), and 3) Viterbi algorithms. In the simulations, we choose to limit ourselves to a noncausal 2-D model whose noncausality is along a single dimension, in order to significantly reduce the computational complexity. Simulation results demonstrate the superior performance, higher accuracy rate, and applicability of the proposed noncausal HMM framework to image and video classification.

Effects of short-term active video game play on community adults: under International Classification of Functioning, Disability and Health consideration.

PubMed

Tseng, Wei-Che; Hsieh, Ru-Lan

2013-06-01

The effects of active video game play on healthy individuals remain uncertain. A person's functional health status constitutes a dynamic interaction between components identified in the International Classification of Functioning, Disability, and Health (ICF). The aim of this study was to investigate the short-term effects of active video game play on community adults using the ICF. Sixty community adults with an average age of 59.3 years and without physical disabilities were recruited. Over 2 weeks, each adult participated in six sessions of active video game play lasting 20 minutes each. Participants were assessed before and after the intervention. Variables were collected using sources related to the ICF components, including the Hospital Anxiety and Depression Scale, Multidimensional Fatigue Inventory, Biodex Stability System, chair- rising time, Frenchay Activity Index, Rivermead Mobility Index, Chronic Pain Grade Questionnaire, Work Ability Index, and World Health Organization Quality of Life-Brief Version. Compared to baseline data, significantly reduced risk of a fall measured by Biodex Stability System and improvements in disability scores measured by the Chronic Pain Grade Questionnaire were noted. There was no significant change in the other variables measured. Short-term, active video game play reduces fall risks and ameliorates disabilities in community adults.

White Paper: Movement System Diagnoses in Neurologic Physical Therapy.

PubMed

Hedman, Lois D; Quinn, Lori; Gill-Body, Kathleen; Brown, David A; Quiben, Myla; Riley, Nora; Scheets, Patricia L

2018-04-01

The APTA recently established a vision for physical therapists to transform society by optimizing movement to promote health and wellness, mitigate impairments, and prevent disability. An important element of this vision entails the integration of the movement system into the profession, and necessitates the development of movement system diagnoses by physical therapists. At this point in time, the profession as a whole has not agreed upon diagnostic classifications or guidelines to assist in developing movement system diagnoses that will consistently capture an individual's movement problems. We propose that, going forward, diagnostic classifications of movement system problems need to be developed, tested, and validated. The Academy of Neurologic Physical Therapy's Movement System Task Force was convened to address these issues with respect to management of movement system problems in patients with neurologic conditions. The purpose of this article is to report on the work and recommendations of the Task Force. The Task Force identified 4 essential elements necessary to develop and implement movement system diagnoses for patients with primarily neurologic involvement from existing movement system classifications. The Task Force considered the potential impact of using movement system diagnoses on clinical practice, education and, research. Recommendations were developed and provided recommendations for potential next steps to broaden this discussion and foster the development of movement system diagnostic classifications. The Task Force proposes that diagnostic classifications of movement system problems need to be developed, tested, and validated with the long-range goal to reach consensus on and adoption of a movement system diagnostic framework for clients with neurologic injury or disease states.Video Abstract available for more insights from the authors (see Video, Supplemental Digital Content 1, available at: http://links.lww.com/JNPT/A198).

Video Games: Instructional Potential and Classification.

ERIC Educational Resources Information Center

Nawrocki, Leon H.; Winner, Janet L.

1983-01-01

Intended to provide a framework and impetus for future investigations of video games, this paper summarizes activities investigating the instructional use of such games, observations by the authors, and a proposed classification scheme and a paradigm to assist in the preliminary selection of instructional video games. Nine references are listed.…

A novel summary report of colonoscopy: timeline visualization providing meaningful colonoscopy video information.

PubMed

Cho, Minwoo; Kim, Jee Hyun; Kong, Hyoun Joong; Hong, Kyoung Sup; Kim, Sungwan

2018-05-01

The colonoscopy adenoma detection rate depends largely on physician experience and skill, and overlooked colorectal adenomas could develop into cancer. This study assessed a system that detects polyps and summarizes meaningful information from colonoscopy videos. One hundred thirteen consecutive patients had colonoscopy videos prospectively recorded at the Seoul National University Hospital. Informative video frames were extracted using a MATLAB support vector machine (SVM) model and classified as bleeding, polypectomy, tool, residue, thin wrinkle, folded wrinkle, or common. Thin wrinkle, folded wrinkle, and common frames were reanalyzed using SVM for polyp detection. The SVM model was applied hierarchically for effective classification and optimization of the SVM. The mean classification accuracy according to type was over 93%; sensitivity was over 87%. The mean sensitivity for polyp detection was 82.1%, and the positive predicted value (PPV) was 39.3%. Polyps detected using the system were larger (6.3 ± 6.4 vs. 4.9 ± 2.5 mm; P = 0.003) with a more pedunculated morphology (Yamada type III, 10.2 vs. 0%; P < 0.001; Yamada type IV, 2.8 vs. 0%; P < 0.001) than polyps missed by the system. There were no statistically significant differences in polyp distribution or histology between the groups. Informative frames and suspected polyps were presented on a timeline. This summary was evaluated using the system usability scale questionnaire; 89.3% of participants expressed positive opinions. We developed and verified a system to extract meaningful information from colonoscopy videos. Although further improvement and validation of the system is needed, the proposed system is useful for physicians and patients.

A comparison of basic deinterlacing approaches for a computer assisted diagnosis approach of videoscope images

NASA Astrophysics Data System (ADS)

Kage, Andreas; Canto, Marcia; Gorospe, Emmanuel; Almario, Antonio; Münzenmayer, Christian

2010-03-01

In the near future, Computer Assisted Diagnosis (CAD) which is well known in the area of mammography might be used to support clinical experts in the diagnosis of images derived from imaging modalities such as endoscopy. In the recent past, a few first approaches for computer assisted endoscopy have been presented already. These systems use a video signal as an input that is provided by the endoscopes video processor. Despite the advent of high-definition systems most standard endoscopy systems today still provide only analog video signals. These signals consist of interlaced images that can not be used in a CAD approach without deinterlacing. Of course, there are many different deinterlacing approaches known today. But most of them are specializations of some basic approaches. In this paper we present four basic deinterlacing approaches. We have used a database of non-interlaced images which have been degraded by artificial interlacing and afterwards processed by these approaches. The database contains regions of interest (ROI) of clinical relevance for the diagnosis of abnormalities in the esophagus. We compared the classification rates on these ROIs on the original images and after the deinterlacing. The results show that the deinterlacing has an impact on the classification rates. The Bobbing approach and the Motion Compensation approach achieved the best classification results in most cases.

Hybrid three-dimensional and support vector machine approach for automatic vehicle tracking and classification using a single camera

NASA Astrophysics Data System (ADS)

Kachach, Redouane; Cañas, José María

2016-05-01

Using video in traffic monitoring is one of the most active research domains in the computer vision community. TrafficMonitor, a system that employs a hybrid approach for automatic vehicle tracking and classification on highways using a simple stationary calibrated camera, is presented. The proposed system consists of three modules: vehicle detection, vehicle tracking, and vehicle classification. Moving vehicles are detected by an enhanced Gaussian mixture model background estimation algorithm. The design includes a technique to resolve the occlusion problem by using a combination of two-dimensional proximity tracking algorithm and the Kanade-Lucas-Tomasi feature tracking algorithm. The last module classifies the shapes identified into five vehicle categories: motorcycle, car, van, bus, and truck by using three-dimensional templates and an algorithm based on histogram of oriented gradients and the support vector machine classifier. Several experiments have been performed using both real and simulated traffic in order to validate the system. The experiments were conducted on GRAM-RTM dataset and a proper real video dataset which is made publicly available as part of this work.

78 FR 66709 - Privacy Act of 1974; Systems of Records

Federal Register 2010, 2011, 2012, 2013, 2014

2013-11-06

...: Online Forms. SECURITY CLASSIFICATION: None. SYSTEM LOCATION: Federal Housing Finance Agency, 400 Seventh... or fraud, or harm to the security or integrity of this system or other systems or programs (whether... ``Photographic, Video, Voice, and Similar Files.'' The proposed new system, ``Online Forms'' (FHFA-22), will...

Gender classification under extended operating conditions

NASA Astrophysics Data System (ADS)

Rude, Howard N.; Rizki, Mateen

2014-06-01

Gender classification is a critical component of a robust image security system. Many techniques exist to perform gender classification using facial features. In contrast, this paper explores gender classification using body features extracted from clothed subjects. Several of the most effective types of features for gender classification identified in literature were implemented and applied to the newly developed Seasonal Weather And Gender (SWAG) dataset. SWAG contains video clips of approximately 2000 samples of human subjects captured over a period of several months. The subjects are wearing casual business attire and outer garments appropriate for the specific weather conditions observed in the Midwest. The results from a series of experiments are presented that compare the classification accuracy of systems that incorporate various types and combinations of features applied to multiple looks at subjects at different image resolutions to determine a baseline performance for gender classification.

Gender classification in low-resolution surveillance video: in-depth comparison of random forests and SVMs

NASA Astrophysics Data System (ADS)

Geelen, Christopher D.; Wijnhoven, Rob G. J.; Dubbelman, Gijs; de With, Peter H. N.

2015-03-01

This research considers gender classification in surveillance environments, typically involving low-resolution images and a large amount of viewpoint variations and occlusions. Gender classification is inherently difficult due to the large intra-class variation and interclass correlation. We have developed a gender classification system, which is successfully evaluated on two novel datasets, which realistically consider the above conditions, typical for surveillance. The system reaches a mean accuracy of up to 90% and approaches our human baseline of 92.6%, proving a high-quality gender classification system. We also present an in-depth discussion of the fundamental differences between SVM and RF classifiers. We conclude that balancing the degree of randomization in any classifier is required for the highest classification accuracy. For our problem, an RF-SVM hybrid classifier exploiting the combination of HSV and LBP features results in the highest classification accuracy of 89.9 0.2%, while classification computation time is negligible compared to the detection time of pedestrians.

Challenges for Deploying Man-Portable Robots into Hostile Environments

DTIC Science & Technology

2000-11-01

video, JAUGS , MDARS 1. BACKGROUND In modern-day warfare the most likely battlefield is an urban environment, which poses many threats to today’s...teleoperation, reconnaissance, surveillance, digital video, JAUGS , MDARS 15. SUBJECT TERMS 16. SECURITY CLASSIFICATION OF: 17. LIMITATION OF ABSTRACT 18...Architecture (MRHA) and the Joint Architecture for Unmanned Ground Systems ( JAUGS ). The hybrid architecture is termed SMART for Small Robotic Technology. It

Concept-oriented indexing of video databases: toward semantic sensitive retrieval and browsing.

PubMed

Fan, Jianping; Luo, Hangzai; Elmagarmid, Ahmed K

2004-07-01

Digital video now plays an important role in medical education, health care, telemedicine and other medical applications. Several content-based video retrieval (CBVR) systems have been proposed in the past, but they still suffer from the following challenging problems: semantic gap, semantic video concept modeling, semantic video classification, and concept-oriented video database indexing and access. In this paper, we propose a novel framework to make some advances toward the final goal to solve these problems. Specifically, the framework includes: 1) a semantic-sensitive video content representation framework by using principal video shots to enhance the quality of features; 2) semantic video concept interpretation by using flexible mixture model to bridge the semantic gap; 3) a novel semantic video-classifier training framework by integrating feature selection, parameter estimation, and model selection seamlessly in a single algorithm; and 4) a concept-oriented video database organization technique through a certain domain-dependent concept hierarchy to enable semantic-sensitive video retrieval and browsing.

Know your data: understanding implicit usage versus explicit action in video content classification

NASA Astrophysics Data System (ADS)

Yew, Jude; Shamma, David A.

2011-02-01

In this paper, we present a method for video category classification using only social metadata from websites like YouTube. In place of content analysis, we utilize communicative and social contexts surrounding videos as a means to determine a categorical genre, e.g. Comedy, Music. We hypothesize that video clips belonging to different genre categories would have distinct signatures and patterns that are reflected in their collected metadata. In particular, we define and describe social metadata as usage or action to aid in classification. We trained a Naive Bayes classifier to predict categories from a sample of 1,740 YouTube videos representing the top five genre categories. Using just a small number of the available metadata features, we compare the classifications produced by our Naive Bayes classifier with those provided by the uploader of that particular video. Compared to random predictions with the YouTube data (21% accurate), our classifier attained a mediocre 33% accuracy in predicting video genres. However, we found that the accuracy of our classifier significantly improves by nominal factoring of the explicit data features. By factoring the ratings of the videos in the dataset, the classifier was able to accurately predict the genres of 75% of the videos. We argue that the patterns of social activity found in the metadata are not just meaningful in their own right, but are indicative of the meaning of the shared video content. The results presented by this project represents a first step in investigating the potential meaning and significance of social metadata and its relation to the media experience.

Interactive color display for multispectral imagery using correlation clustering

NASA Technical Reports Server (NTRS)

Haskell, R. E. (Inventor)

1979-01-01

A method for processing multispectral data is provided, which permits an operator to make parameter level changes during the processing of the data. The system is directed to production of a color classification map on a video display in which a given color represents a localized region in multispectral feature space. Interactive controls permit an operator to alter the size and change the location of these regions, permitting the classification of such region to be changed from a broad to a narrow classification.

Adaptive video-based vehicle classification technique for monitoring traffic.

DOT National Transportation Integrated Search

2015-08-01

This report presents a methodology for extracting two vehicle features, vehicle length and number of axles in order : to classify the vehicles from video, based on Federal Highway Administration (FHWA)s recommended vehicle : classification scheme....

Real-Time Control of a Video Game Using Eye Movements and Two Temporal EEG Sensors.

PubMed

Belkacem, Abdelkader Nasreddine; Saetia, Supat; Zintus-art, Kalanyu; Shin, Duk; Kambara, Hiroyuki; Yoshimura, Natsue; Berrached, Nasreddine; Koike, Yasuharu

2015-01-01

EEG-controlled gaming applications range widely from strictly medical to completely nonmedical applications. Games can provide not only entertainment but also strong motivation for practicing, thereby achieving better control with rehabilitation system. In this paper we present real-time control of video game with eye movements for asynchronous and noninvasive communication system using two temporal EEG sensors. We used wavelets to detect the instance of eye movement and time-series characteristics to distinguish between six classes of eye movement. A control interface was developed to test the proposed algorithm in real-time experiments with opened and closed eyes. Using visual feedback, a mean classification accuracy of 77.3% was obtained for control with six commands. And a mean classification accuracy of 80.2% was obtained using auditory feedback for control with five commands. The algorithm was then applied for controlling direction and speed of character movement in two-dimensional video game. Results showed that the proposed algorithm had an efficient response speed and timing with a bit rate of 30 bits/min, demonstrating its efficacy and robustness in real-time control.

Real-Time Control of a Video Game Using Eye Movements and Two Temporal EEG Sensors

PubMed Central

Saetia, Supat; Zintus-art, Kalanyu; Shin, Duk; Kambara, Hiroyuki; Yoshimura, Natsue; Berrached, Nasreddine; Koike, Yasuharu

2015-01-01

EEG-controlled gaming applications range widely from strictly medical to completely nonmedical applications. Games can provide not only entertainment but also strong motivation for practicing, thereby achieving better control with rehabilitation system. In this paper we present real-time control of video game with eye movements for asynchronous and noninvasive communication system using two temporal EEG sensors. We used wavelets to detect the instance of eye movement and time-series characteristics to distinguish between six classes of eye movement. A control interface was developed to test the proposed algorithm in real-time experiments with opened and closed eyes. Using visual feedback, a mean classification accuracy of 77.3% was obtained for control with six commands. And a mean classification accuracy of 80.2% was obtained using auditory feedback for control with five commands. The algorithm was then applied for controlling direction and speed of character movement in two-dimensional video game. Results showed that the proposed algorithm had an efficient response speed and timing with a bit rate of 30 bits/min, demonstrating its efficacy and robustness in real-time control. PMID:26690500

Automatic video shot boundary detection using k-means clustering and improved adaptive dual threshold comparison

NASA Astrophysics Data System (ADS)

Sa, Qila; Wang, Zhihui

2018-03-01

At present, content-based video retrieval (CBVR) is the most mainstream video retrieval method, using the video features of its own to perform automatic identification and retrieval. This method involves a key technology, i.e. shot segmentation. In this paper, the method of automatic video shot boundary detection with K-means clustering and improved adaptive dual threshold comparison is proposed. First, extract the visual features of every frame and divide them into two categories using K-means clustering algorithm, namely, one with significant change and one with no significant change. Then, as to the classification results, utilize the improved adaptive dual threshold comparison method to determine the abrupt as well as gradual shot boundaries.Finally, achieve automatic video shot boundary detection system.

Polyp morphology: an interobserver evaluation for the Paris classification among international experts.

PubMed

van Doorn, Sascha C; Hazewinkel, Y; East, James E; van Leerdam, Monique E; Rastogi, Amit; Pellisé, Maria; Sanduleanu-Dascalescu, Silvia; Bastiaansen, Barbara A J; Fockens, Paul; Dekker, Evelien

2015-01-01

The Paris classification is an international classification system for describing polyp morphology. Thus far, the validity and reproducibility of this classification have not been assessed. We aimed to determine the interobserver agreement for the Paris classification among seven Western expert endoscopists. A total of 85 short endoscopic video clips depicting polyps were created and assessed by seven expert endoscopists according to the Paris classification. After a digital training module, the same 85 polyps were assessed again. We calculated the interobserver agreement with a Fleiss kappa and as the proportion of pairwise agreement. The interobserver agreement of the Paris classification among seven experts was moderate with a Fleiss kappa of 0.42 and a mean pairwise agreement of 67%. The proportion of lesions assessed as "flat" by the experts ranged between 13 and 40% (P<0.001). After the digital training, the interobserver agreement did not change (kappa 0.38, pairwise agreement 60%). Our study is the first to validate the Paris classification for polyp morphology. We demonstrated only a moderate interobserver agreement among international Western experts for this classification system. Our data suggest that, in its current version, the use of this classification system in daily practice is questionable and it is unsuitable for comparative endoscopic research. We therefore suggest introduction of a simplification of the classification system.

The groningen laryngomalacia classification system--based on systematic review and dynamic airway changes.

PubMed

van der Heijden, Martijn; Dikkers, Frederik G; Halmos, Gyorgy B

2015-12-01

Laryngomalacia is the most common cause of dyspnea and stridor in newborn infants. Laryngomalacia is a dynamic change of the upper airway based on abnormally pliable supraglottic structures, which causes upper airway obstruction. In the past, different classification systems have been introduced. Until now no classification system is widely accepted and applied. Our goal is to provide a simple and complete classification system based on systematic literature search and our experiences. Retrospective cohort study with literature review. All patients with laryngomalacia under the age of 5 at time of diagnosis were included. Photo and video documentation was used to confirm diagnosis and characteristics of dynamic airway change. Outcome was compared with available classification systems in literature. Eighty-five patients were included. In contrast to other classification systems, only three typical different dynamic changes have been identified in our series. Two existing classification systems covered 100% of our findings, but there was an unnecessary overlap between different types in most of the systems. Based on our finding, we propose a new a classification system for laryngomalacia, which is purely based on dynamic airway changes. The groningen laryngomalacia classification is a new, simplified classification system with three types, based on purely dynamic laryngeal changes, tested in a tertiary referral center: Type 1: inward collapse of arytenoids cartilages, Type 2: medial displacement of aryepiglottic folds, and Type 3: posterocaudal displacement of epiglottis against the posterior pharyngeal wall. © 2015 Wiley Periodicals, Inc.

Video based object representation and classification using multiple covariance matrices.

PubMed

Zhang, Yurong; Liu, Quan

2017-01-01

Video based object recognition and classification has been widely studied in computer vision and image processing area. One main issue of this task is to develop an effective representation for video. This problem can generally be formulated as image set representation. In this paper, we present a new method called Multiple Covariance Discriminative Learning (MCDL) for image set representation and classification problem. The core idea of MCDL is to represent an image set using multiple covariance matrices with each covariance matrix representing one cluster of images. Firstly, we use the Nonnegative Matrix Factorization (NMF) method to do image clustering within each image set, and then adopt Covariance Discriminative Learning on each cluster (subset) of images. At last, we adopt KLDA and nearest neighborhood classification method for image set classification. Promising experimental results on several datasets show the effectiveness of our MCDL method.

Support-vector-machine tree-based domain knowledge learning toward automated sports video classification

NASA Astrophysics Data System (ADS)

Xiao, Guoqiang; Jiang, Yang; Song, Gang; Jiang, Jianmin

2010-12-01

We propose a support-vector-machine (SVM) tree to hierarchically learn from domain knowledge represented by low-level features toward automatic classification of sports videos. The proposed SVM tree adopts a binary tree structure to exploit the nature of SVM's binary classification, where each internal node is a single SVM learning unit, and each external node represents the classified output type. Such a SVM tree presents a number of advantages, which include: 1. low computing cost; 2. integrated learning and classification while preserving individual SVM's learning strength; and 3. flexibility in both structure and learning modules, where different numbers of nodes and features can be added to address specific learning requirements, and various learning models can be added as individual nodes, such as neural networks, AdaBoost, hidden Markov models, dynamic Bayesian networks, etc. Experiments support that the proposed SVM tree achieves good performances in sports video classifications.

A fuzzy measure approach to motion frame analysis for scene detection. M.S. Thesis - Houston Univ.

NASA Technical Reports Server (NTRS)

Leigh, Albert B.; Pal, Sankar K.

1992-01-01

This paper addresses a solution to the problem of scene estimation of motion video data in the fuzzy set theoretic framework. Using fuzzy image feature extractors, a new algorithm is developed to compute the change of information in each of two successive frames to classify scenes. This classification process of raw input visual data can be used to establish structure for correlation. The algorithm attempts to fulfill the need for nonlinear, frame-accurate access to video data for applications such as video editing and visual document archival/retrieval systems in multimedia environments.

Major morbidity after video-assisted thoracic surgery lung resections: a comparison between the European Society of Thoracic Surgeons definition and the Thoracic Morbidity and Mortality system.

PubMed

Sandri, Alberto; Papagiannopoulos, Kostas; Milton, Richard; Kefaloyannis, Emmanuel; Chaudhuri, Nilanjan; Poyser, Emily; Spencer, Nicholas; Brunelli, Alessandro

2015-07-01

The thoracic morbidity and mortality (TM&M) classification system univocally encodes the postoperative adverse events by their management complexity. This study aims to compare the distribution of the severity of complications according to the TM&M system versus the distribution according to the classification proposed by European Society of Thoracic Surgeons (ESTS) Database in a population of patients submitted to video assisted thoracoscopic surgery (VATS) lung resection. A total of 227 consecutive patients submitted to VATS lobectomy for lung cancer were analyzed. Any complication developed postoperatively was graded from I to V according to the TM&M system, reflecting the increasing severity of its management. We verified the distribution of the different grades of complications and analyzed their frequency among those defined as "major cardiopulmonary complications" by the ESTS Database. Following the ESTS definitions, 20 were the major cardiopulmonary complications [atrial fibrillation (AF): 10, 50%; adult respiratory distress syndrome (ARDS): 1, 5%; pulmonary embolism: 2, 10%; mechanical ventilation >24 h: 1, 5%; pneumonia: 3, 15%; myocardial infarct: 1, 5%; atelectasis requiring bronchoscopy: 2, 10%] of which 9 (45%) were reclassified as minor complications (grade II) by the TM&M classification system. According to the TM&M system, 10/34 (29.4%) of all complications were considered minor (grade I or II) while 21/34 (71.4%) as major (IIIa: 8, 23.5%; IIIb: 4, 11.7%; IVa: 8, 23.5%; IVb: 1, 2.9%; V: 3, 8.8%). Other 14 surgical complications occurred and were classified as major complications according to the TM&M system. The distribution of postoperative complications differs between the two classification systems. The TM&M grading system questions the traditional classification of major complications following VATS lung resection and may be used as an additional endpoint for outcome analyses.

Ranking Highlights in Personal Videos by Analyzing Edited Videos.

PubMed

Sun, Min; Farhadi, Ali; Chen, Tseng-Hung; Seitz, Steve

2016-11-01

We present a fully automatic system for ranking domain-specific highlights in unconstrained personal videos by analyzing online edited videos. A novel latent linear ranking model is proposed to handle noisy training data harvested online. Specifically, given a targeted domain such as "surfing," our system mines the YouTube database to find pairs of raw and their corresponding edited videos. Leveraging the assumption that an edited video is more likely to contain highlights than the trimmed parts of the raw video, we obtain pair-wise ranking constraints to train our model. The learning task is challenging due to the amount of noise and variation in the mined data. Hence, a latent loss function is incorporated to mitigate the issues caused by the noise. We efficiently learn the latent model on a large number of videos (about 870 min in total) using a novel EM-like procedure. Our latent ranking model outperforms its classification counterpart and is fairly competitive compared with a fully supervised ranking system that requires labels from Amazon Mechanical Turk. We further show that a state-of-the-art audio feature mel-frequency cepstral coefficients is inferior to a state-of-the-art visual feature. By combining both audio-visual features, we obtain the best performance in dog activity, surfing, skating, and viral video domains. Finally, we show that impressive highlights can be detected without additional human supervision for seven domains (i.e., skating, surfing, skiing, gymnastics, parkour, dog activity, and viral video) in unconstrained personal videos.

Characterizing the uncertainty of classification methods and its impact on the performance of crowdsourcing

NASA Astrophysics Data System (ADS)

Ribera, Javier; Tahboub, Khalid; Delp, Edward J.

2015-03-01

Video surveillance systems are widely deployed for public safety. Real-time monitoring and alerting are some of the key requirements for building an intelligent video surveillance system. Real-life settings introduce many challenges that can impact the performance of real-time video analytics. Video analytics are desired to be resilient to adverse and changing scenarios. In this paper we present various approaches to characterize the uncertainty of a classifier and incorporate crowdsourcing at the times when the method is uncertain about making a particular decision. Incorporating crowdsourcing when a real-time video analytic method is uncertain about making a particular decision is known as online active learning from crowds. We evaluate our proposed approach by testing a method we developed previously for crowd flow estimation. We present three different approaches to characterize the uncertainty of the classifier in the automatic crowd flow estimation method and test them by introducing video quality degradations. Criteria to aggregate crowdsourcing results are also proposed and evaluated. An experimental evaluation is conducted using a publicly available dataset.

Patterns of Negotiation

NASA Astrophysics Data System (ADS)

Sood, Suresh; Pattinson, Hugh

Traditionally, face-to-face negotiations in the real world have not been looked at as a complex systems interaction of actors resulting in a dynamic and potentially emergent system. If indeed negotiations are an outcome of a dynamic interaction of simpler behavior just as with a complex system, we should be able to see the patterns contributing to the complexities of a negotiation under study. This paper and the supporting research sets out to show B2B (business-to-business) negotiations as complex systems of interacting actors exhibiting dynamic and emergent behavior. This paper discusses the exploratory research based on negotiation simulations in which a large number of business students participate as buyers and sellers. The student interactions are captured on video and a purpose built research method attempts to look for patterns of interactions between actors using visualization techniques traditionally reserved to observe the algorithmic complexity of complex systems. Students are videoed negotiating with partners. Each video is tagged according to a recognized classification and coding scheme for negotiations. The classification relates to the phases through which any particular negotiation might pass, such as laughter, aggression, compromise, and so forth — through some 30 possible categories. Were negotiations more or less successful if they progressed through the categories in different ways? Furthermore, does the data depict emergent pathway segments considered to be more or less successful? This focus on emergence within the data provides further strong support for face-to-face (F2F) negotiations to be construed as complex systems.

Learning Styles, Online Content Usage and Exam Performance in a Mixed-Format Introductory Computer Information Systems Course

ERIC Educational Resources Information Center

Lang, Guido; O'Connell, Stephen D.

2015-01-01

We investigate the relationship between learning styles, online content usage and exam performance in an undergraduate introductory Computer Information Systems class comprised of both online video tutorials and in-person classes. Our findings suggest that, across students, (1) traditional learning style classification methodologies do not predict…

Unmanned Aerial Vehicles (UAVs) and Artificial Intelligence Revolutionizing Wildlife Monitoring and Conservation

PubMed Central

Gonzalez, Luis F.; Montes, Glen A.; Puig, Eduard; Johnson, Sandra; Mengersen, Kerrie; Gaston, Kevin J.

2016-01-01

Surveying threatened and invasive species to obtain accurate population estimates is an important but challenging task that requires a considerable investment in time and resources. Estimates using existing ground-based monitoring techniques, such as camera traps and surveys performed on foot, are known to be resource intensive, potentially inaccurate and imprecise, and difficult to validate. Recent developments in unmanned aerial vehicles (UAV), artificial intelligence and miniaturized thermal imaging systems represent a new opportunity for wildlife experts to inexpensively survey relatively large areas. The system presented in this paper includes thermal image acquisition as well as a video processing pipeline to perform object detection, classification and tracking of wildlife in forest or open areas. The system is tested on thermal video data from ground based and test flight footage, and is found to be able to detect all the target wildlife located in the surveyed area. The system is flexible in that the user can readily define the types of objects to classify and the object characteristics that should be considered during classification. PMID:26784196

Unmanned Aerial Vehicles (UAVs) and Artificial Intelligence Revolutionizing Wildlife Monitoring and Conservation.

PubMed

Gonzalez, Luis F; Montes, Glen A; Puig, Eduard; Johnson, Sandra; Mengersen, Kerrie; Gaston, Kevin J

2016-01-14

Surveying threatened and invasive species to obtain accurate population estimates is an important but challenging task that requires a considerable investment in time and resources. Estimates using existing ground-based monitoring techniques, such as camera traps and surveys performed on foot, are known to be resource intensive, potentially inaccurate and imprecise, and difficult to validate. Recent developments in unmanned aerial vehicles (UAV), artificial intelligence and miniaturized thermal imaging systems represent a new opportunity for wildlife experts to inexpensively survey relatively large areas. The system presented in this paper includes thermal image acquisition as well as a video processing pipeline to perform object detection, classification and tracking of wildlife in forest or open areas. The system is tested on thermal video data from ground based and test flight footage, and is found to be able to detect all the target wildlife located in the surveyed area. The system is flexible in that the user can readily define the types of objects to classify and the object characteristics that should be considered during classification.

Identifying sports videos using replay, text, and camera motion features

NASA Astrophysics Data System (ADS)

Kobla, Vikrant; DeMenthon, Daniel; Doermann, David S.

1999-12-01

Automated classification of digital video is emerging as an important piece of the puzzle in the design of content management systems for digital libraries. The ability to classify videos into various classes such as sports, news, movies, or documentaries, increases the efficiency of indexing, browsing, and retrieval of video in large databases. In this paper, we discuss the extraction of features that enable identification of sports videos directly from the compressed domain of MPEG video. These features include detecting the presence of action replays, determining the amount of scene text in vide, and calculating various statistics on camera and/or object motion. The features are derived from the macroblock, motion,and bit-rate information that is readily accessible from MPEG video with very minimal decoding, leading to substantial gains in processing speeds. Full-decoding of selective frames is required only for text analysis. A decision tree classifier built using these features is able to identify sports clips with an accuracy of about 93 percent.

Interrater reliability of a Pilates movement-based classification system.

PubMed

Yu, Kwan Kenny; Tulloch, Evelyn; Hendrick, Paul

2015-01-01

To determine the interrater reliability for identification of a specific movement pattern using a Pilates Classification system. Videos of 5 subjects performing specific movement tasks were sent to raters trained in the DMA-CP classification system. Ninety-six raters completed the survey. Interrater reliability for the detection of a directional bias was excellent (Pi = 0.92, and K(free) = 0.89). Interrater reliability for classifying an individual into a specific subgroup was moderate (Pi = 0.64, K(free) = 0.55) however raters who had completed levels 1-4 of the DMA-CP training and reported using the assessment daily demonstrated excellent reliability (Pi = 0.89 and K(free) = 0.87). The reliability of the classification system demonstrated almost perfect agreement in determining the existence of a specific movement pattern and classifying into a subgroup for experienced raters. There was a trend for greater reliability associated with increased levels of training and experience of the raters. Copyright © 2014 Elsevier Ltd. All rights reserved.

Observer reliability of the Gross Motor Performance Measure and the Quality of Upper Extremity Skills Test, based on video recordings.

PubMed

Sorsdahl, Anne Brit; Moe-Nilssen, Rolf; Strand, Liv Inger

2008-02-01

The aim of this study was to examine observer reliability of the Gross Motor Performance Measure (GMPM) and the Quality of Upper Extremity Skills Test (QUEST) based on video clips. The tests were administered to 26 children with cerebral palsy (CP; 14 males, 12 females; range 2-13y, mean 7y 6mo), 24 with spastic CP, and two with dyskinesia. Respectively, five, six, five, four, and six children were classified in Gross Motor Function Classification System Levels I to V; and four, nine, five, five, and three children were classified in Manual Ability Classification System levels I to V. The children's performances were recorded and edited. Two experienced paediatric physical therapists assessed the children from watching the video clips. Intraobserver and interobserver reliability values of the total scores were mostly high, intraclass correlation coefficient (ICC)(1,1) varying from 0.69 to 0.97 with only one coefficient below 0.89. The ICCs of subscores varied from 0.36 to 0.95, finding'Alignment'and'Weight shift'in GMPM and'Protective extension'in QUEST highly reliable. The subscores'Dissociated movements'in GMPM and QUEST, and'Grasp'in QUEST were the least reliable, and recommendations are made to increase reliability of these subscores. Video scoring was time consuming, but was found to offer many advantages; the possibility to review performance, to use special trained observers for scoring and less demanding assessment for the children.

Application of the Coastal and Marine Ecological Classification Standard to ROV Video Data for Enhanced Analysis of Deep-Sea Habitats in the Gulf of Mexico

NASA Astrophysics Data System (ADS)

Ruby, C.; Skarke, A. D.; Mesick, S.

2016-02-01

The Coastal and Marine Ecological Classification Standard (CMECS) is a network of common nomenclature that provides a comprehensive framework for organizing physical, biological, and chemical information about marine ecosystems. It was developed by the National Oceanic and Atmospheric Administration (NOAA) Coastal Services Center, in collaboration with other feral agencies and academic institutions, as a means for scientists to more easily access, compare, and integrate marine environmental data from a wide range of sources and time frames. CMECS has been endorsed by the Federal Geographic Data Committee (FGDC) as a national metadata standard. The research presented here is focused on the application of CMECS to deep-sea video and environmental data collected by the NOAA ROV Deep Discoverer and the NOAA Ship Okeanos Explorer in the Gulf of Mexico in 2011-2014. Specifically, a spatiotemporal index of the physical, chemical, biological, and geological features observed in ROV video records was developed in order to allow scientist, otherwise unfamiliar with the specific content of existing video data, to rapidly determine the abundance and distribution of features of interest, and thus evaluate the applicability of those video data to their research. CMECS units (setting, component, or modifier) for seafloor images extracted from high-definition ROV video data were established based upon visual assessment as well as analysis of coincident environmental sensor (temperature, conductivity), navigation (ROV position, depth, attitude), and log (narrative dive summary) data. The resulting classification units were integrated into easily searchable textual and geo-databases as well as an interactive web map. The spatial distribution and associations of deep-sea habitats as indicated by CMECS classifications are described and optimized methodological approaches for application of CMECS to deep-sea video and environmental data are presented.

The use of video clips in teleconsultation for preschool children with movement disorders.

PubMed

Gorter, Hetty; Lucas, Cees; Groothuis-Oudshoorn, Karin; Maathuis, Carel; van Wijlen-Hempel, Rietje; Elvers, Hans

2013-01-01

To investigate the reliability and validity of video clips in assessing movement disorders in preschool children. The study group included 27 children with neuromotor concerns. The explorative validity group included children with motor problems (n = 21) or with typical development (n = 9). Hempel screening was used for live observation of the child, full recording, and short video clips. The explorative study tested the validity of the clinical classifications "typical" or "suspect." Agreement between live observation and the full recording was almost perfect; Agreement for the clinical classification "typical" or "suspect" was substantial. Agreement between the full recording and short video clips was substantial to moderate. The explorative validity study, based on short video clips and the presence of a neuromotor developmental disorder, showed substantial agreement. Hempel screening enables reliable and valid observation of video clips, but further research is necessary to demonstrate the predictive value.

People counting and re-identification using fusion of video camera and laser scanner

NASA Astrophysics Data System (ADS)

Ling, Bo; Olivera, Santiago; Wagley, Raj

2016-05-01

We present a system for people counting and re-identification. It can be used by transit and homeland security agencies. Under FTA SBIR program, we have developed a preliminary system for transit passenger counting and re-identification using a laser scanner and video camera. The laser scanner is used to identify the locations of passenger's head and shoulder in an image, a challenging task in crowed environment. It can also estimate the passenger height without prior calibration. Various color models have been applied to form color signatures. Finally, using a statistical fusion and classification scheme, passengers are counted and re-identified.

5 CFR 532.267 - Special wage schedules for aircraft, electronic, and optical instrument overhaul and repair...

Code of Federal Regulations, 2011 CFR

2011-01-01

... Industry Classification System (NAICS) codes: 2007 NAICS codes 2007 NAICS industry titles 3341 Computer and peripheral equipment manufacturing. 33422 Radio and television broadcasting and wireless communications equipment manufacturing. 33429 Other communications equipment manufacturing. 3343 Audio and video equipment...

5 CFR 532.267 - Special wage schedules for aircraft, electronic, and optical instrument overhaul and repair...

Code of Federal Regulations, 2012 CFR

2012-01-01

... Industry Classification System (NAICS) codes: 2007 NAICS codes 2007 NAICS industry titles 3341 Computer and peripheral equipment manufacturing. 33422 Radio and television broadcasting and wireless communications equipment manufacturing. 33429 Other communications equipment manufacturing. 3343 Audio and video equipment...

5 CFR 532.267 - Special wage schedules for aircraft, electronic, and optical instrument overhaul and repair...

Code of Federal Regulations, 2013 CFR

2013-01-01

... Industry Classification System (NAICS) codes: 2007 NAICS codes 2007 NAICS industry titles 3341 Computer and peripheral equipment manufacturing. 33422 Radio and television broadcasting and wireless communications equipment manufacturing. 33429 Other communications equipment manufacturing. 3343 Audio and video equipment...

Classification of video sequences into chosen generalized use classes of target size and lighting level.

PubMed

Leszczuk, Mikołaj; Dudek, Łukasz; Witkowski, Marcin

The VQiPS (Video Quality in Public Safety) Working Group, supported by the U.S. Department of Homeland Security, has been developing a user guide for public safety video applications. According to VQiPS, five parameters have particular importance influencing the ability to achieve a recognition task. They are: usage time-frame, discrimination level, target size, lighting level, and level of motion. These parameters form what are referred to as Generalized Use Classes (GUCs). The aim of our research was to develop algorithms that would automatically assist classification of input sequences into one of the GUCs. Target size and lighting level parameters were approached. The experiment described reveals the experts' ambiguity and hesitation during the manual target size determination process. However, the automatic methods developed for target size classification make it possible to determine GUC parameters with 70 % compliance to the end-users' opinion. Lighting levels of the entire sequence can be classified with an efficiency reaching 93 %. To make the algorithms available for use, a test application has been developed. It is able to process video files and display classification results, the user interface being very simple and requiring only minimal user interaction.

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

NASA Astrophysics Data System (ADS)

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.

Recognition of Indian Sign Language in Live Video

NASA Astrophysics Data System (ADS)

Singha, Joyeeta; Das, Karen

2013-05-01

Sign Language Recognition has emerged as one of the important area of research in Computer Vision. The difficulty faced by the researchers is that the instances of signs vary with both motion and appearance. Thus, in this paper a novel approach for recognizing various alphabets of Indian Sign Language is proposed where continuous video sequences of the signs have been considered. The proposed system comprises of three stages: Preprocessing stage, Feature Extraction and Classification. Preprocessing stage includes skin filtering, histogram matching. Eigen values and Eigen Vectors were considered for feature extraction stage and finally Eigen value weighted Euclidean distance is used to recognize the sign. It deals with bare hands, thus allowing the user to interact with the system in natural way. We have considered 24 different alphabets in the video sequences and attained a success rate of 96.25%.

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

NASA Astrophysics Data System (ADS)

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.

Geological sampling data and benthic biota classification: Buzzards Bay and Vineyard Sound, Massachusetts

USGS Publications Warehouse

Ackerman, Seth D.; Pappal, Adrienne L.; Huntley, Emily C.; Blackwood, Dann S.; Schwab, William C.

2015-01-01

Sea-floor sample collection is an important component of a statewide cooperative mapping effort between the U.S. Geological Survey (USGS) and the Massachusetts Office of Coastal Zone Management (CZM). Sediment grab samples, bottom photographs, and video transects were collected within Vineyard Sound and Buzzards Bay in 2010 aboard the research vesselConnecticut. This report contains sample data and related information, including analyses of surficial-sediment grab samples, locations and images of sea-floor photography, survey lines along which sea-floor video was collected, and a classification of benthic biota observed in sea-floor photographs and based on the Coastal and Marine Ecological Classification Standard (CMECS). These sample data and analyses information are used to verify interpretations of geophysical data and are an essential part of geologic maps of the sea floor. These data also provide a valuable inventory of benthic habitat and resources. Geographic information system (GIS) data, maps, and interpretations, produced through the USGS and CZM mapping cooperative, are intended to aid efforts to manage coastal and marine resources and to provide baseline information for research focused on coastal evolution and environmental change.

Audio-guided audiovisual data segmentation, indexing, and retrieval

NASA Astrophysics Data System (ADS)

Zhang, Tong; Kuo, C.-C. Jay

1998-12-01

While current approaches for video segmentation and indexing are mostly focused on visual information, audio signals may actually play a primary role in video content parsing. In this paper, we present an approach for automatic segmentation, indexing, and retrieval of audiovisual data, based on audio content analysis. The accompanying audio signal of audiovisual data is first segmented and classified into basic types, i.e., speech, music, environmental sound, and silence. This coarse-level segmentation and indexing step is based upon morphological and statistical analysis of several short-term features of the audio signals. Then, environmental sounds are classified into finer classes, such as applause, explosions, bird sounds, etc. This fine-level classification and indexing step is based upon time- frequency analysis of audio signals and the use of the hidden Markov model as the classifier. On top of this archiving scheme, an audiovisual data retrieval system is proposed. Experimental results show that the proposed approach has an accuracy rate higher than 90 percent for the coarse-level classification, and higher than 85 percent for the fine-level classification. Examples of audiovisual data segmentation and retrieval are also provided.

Using Video Modeling and Video Prompting to Teach Core Academic Content to Students with Learning Disabilities

ERIC Educational Resources Information Center

Kellems, Ryan O.; Edwards, Sean

2016-01-01

Practitioners are constantly searching for evidence-based practices that are effective in teaching academic skills to students with learning disabilities (LD). Video modeling (VM) and video prompting have become popular instructional interventions for many students across a wide range of different disability classifications, including those with…

An automated form of video image analysis applied to classification of movement disorders.

PubMed

Chang, R; Guan, L; Burne, J A

Video image analysis is able to provide quantitative data on postural and movement abnormalities and thus has an important application in neurological diagnosis and management. The conventional techniques require patients to be videotaped while wearing markers in a highly structured laboratory environment. This restricts the utility of video in routine clinical practise. We have begun development of intelligent software which aims to provide a more flexible system able to quantify human posture and movement directly from whole-body images without markers and in an unstructured environment. The steps involved are to extract complete human profiles from video frames, to fit skeletal frameworks to the profiles and derive joint angles and swing distances. By this means a given posture is reduced to a set of basic parameters that can provide input to a neural network classifier. To test the system's performance we videotaped patients with dopa-responsive Parkinsonism and age-matched normals during several gait cycles, to yield 61 patient and 49 normal postures. These postures were reduced to their basic parameters and fed to the neural network classifier in various combinations. The optimal parameter sets (consisting of both swing distances and joint angles) yielded successful classification of normals and patients with an accuracy above 90%. This result demonstrated the feasibility of the approach. The technique has the potential to guide clinicians on the relative sensitivity of specific postural/gait features in diagnosis. Future studies will aim to improve the robustness of the system in providing accurate parameter estimates from subjects wearing a range of clothing, and to further improve discrimination by incorporating more stages of the gait cycle into the analysis.

Monitoring tool usage in surgery videos using boosted convolutional and recurrent neural networks.

PubMed

Al Hajj, Hassan; Lamard, Mathieu; Conze, Pierre-Henri; Cochener, Béatrice; Quellec, Gwenolé

2018-05-09

This paper investigates the automatic monitoring of tool usage during a surgery, with potential applications in report generation, surgical training and real-time decision support. Two surgeries are considered: cataract surgery, the most common surgical procedure, and cholecystectomy, one of the most common digestive surgeries. Tool usage is monitored in videos recorded either through a microscope (cataract surgery) or an endoscope (cholecystectomy). Following state-of-the-art video analysis solutions, each frame of the video is analyzed by convolutional neural networks (CNNs) whose outputs are fed to recurrent neural networks (RNNs) in order to take temporal relationships between events into account. Novelty lies in the way those CNNs and RNNs are trained. Computational complexity prevents the end-to-end training of "CNN+RNN" systems. Therefore, CNNs are usually trained first, independently from the RNNs. This approach is clearly suboptimal for surgical tool analysis: many tools are very similar to one another, but they can generally be differentiated based on past events. CNNs should be trained to extract the most useful visual features in combination with the temporal context. A novel boosting strategy is proposed to achieve this goal: the CNN and RNN parts of the system are simultaneously enriched by progressively adding weak classifiers (either CNNs or RNNs) trained to improve the overall classification accuracy. Experiments were performed in a dataset of 50 cataract surgery videos, where the usage of 21 surgical tools was manually annotated, and a dataset of 80 cholecystectomy videos, where the usage of 7 tools was manually annotated. Very good classification performance are achieved in both datasets: tool usage could be labeled with an average area under the ROC curve of A z =0.9961 and A z =0.9939, respectively, in offline mode (using past, present and future information), and A z =0.9957 and A z =0.9936, respectively, in online mode (using past and present information only). Copyright © 2018 Elsevier B.V. All rights reserved.

Automatic polyp detection in colonoscopy videos

NASA Astrophysics Data System (ADS)

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.

Hierarchical Spatio-Temporal Probabilistic Graphical Model with Multiple Feature Fusion for Binary Facial Attribute Classification in Real-World Face Videos.

PubMed

Demirkus, Meltem; Precup, Doina; Clark, James J; Arbel, Tal

2016-06-01

Recent literature shows that facial attributes, i.e., contextual facial information, can be beneficial for improving the performance of real-world applications, such as face verification, face recognition, and image search. Examples of face attributes include gender, skin color, facial hair, etc. How to robustly obtain these facial attributes (traits) is still an open problem, especially in the presence of the challenges of real-world environments: non-uniform illumination conditions, arbitrary occlusions, motion blur and background clutter. What makes this problem even more difficult is the enormous variability presented by the same subject, due to arbitrary face scales, head poses, and facial expressions. In this paper, we focus on the problem of facial trait classification in real-world face videos. We have developed a fully automatic hierarchical and probabilistic framework that models the collective set of frame class distributions and feature spatial information over a video sequence. The experiments are conducted on a large real-world face video database that we have collected, labelled and made publicly available. The proposed method is flexible enough to be applied to any facial classification problem. Experiments on a large, real-world video database McGillFaces [1] of 18,000 video frames reveal that the proposed framework outperforms alternative approaches, by up to 16.96 and 10.13%, for the facial attributes of gender and facial hair, respectively.

Dynamic species classification of microorganisms across time, abiotic and biotic environments—A sliding window approach

PubMed Central

Griffiths, Jason I.; Fronhofer, Emanuel A.; Garnier, Aurélie; Seymour, Mathew; Altermatt, Florian; Petchey, Owen L.

2017-01-01

The development of video-based monitoring methods allows for rapid, dynamic and accurate monitoring of individuals or communities, compared to slower traditional methods, with far reaching ecological and evolutionary applications. Large amounts of data are generated using video-based methods, which can be effectively processed using machine learning (ML) algorithms into meaningful ecological information. ML uses user defined classes (e.g. species), derived from a subset (i.e. training data) of video-observed quantitative features (e.g. phenotypic variation), to infer classes in subsequent observations. However, phenotypic variation often changes due to environmental conditions, which may lead to poor classification, if environmentally induced variation in phenotypes is not accounted for. Here we describe a framework for classifying species under changing environmental conditions based on the random forest classification. A sliding window approach was developed that restricts temporal and environmentally conditions to improve the classification. We tested our approach by applying the classification framework to experimental data. The experiment used a set of six ciliate species to monitor changes in community structure and behavior over hundreds of generations, in dozens of species combinations and across a temperature gradient. Differences in biotic and abiotic conditions caused simplistic classification approaches to be unsuccessful. In contrast, the sliding window approach allowed classification to be highly successful, as phenotypic differences driven by environmental change, could be captured by the classifier. Importantly, classification using the random forest algorithm showed comparable success when validated against traditional, slower, manual identification. Our framework allows for reliable classification in dynamic environments, and may help to improve strategies for long-term monitoring of species in changing environments. Our classification pipeline can be applied in fields assessing species community dynamics, such as eco-toxicology, ecology and evolutionary ecology. PMID:28472193

Artificial Neural Network applied to lightning flashes

NASA Astrophysics Data System (ADS)

Gin, R. B.; Guedes, D.; Bianchi, R.

2013-05-01

The development of video cameras enabled cientists to study lightning discharges comportment with more precision. The main goal of this project is to create a system able to detect images of lightning discharges stored in videos and classify them using an Artificial Neural Network (ANN)using C Language and OpenCV libraries. The developed system, can be split in two different modules: detection module and classification module. The detection module uses OpenCV`s computer vision libraries and image processing techniques to detect if there are significant differences between frames in a sequence, indicating that something, still not classified, occurred. Whenever there is a significant difference between two consecutive frames, two main algorithms are used to analyze the frame image: brightness and shape algorithms. These algorithms detect both shape and brightness of the event, removing irrelevant events like birds, as well as detecting the relevant events exact position, allowing the system to track it over time. The classification module uses a neural network to classify the relevant events as horizontal or vertical lightning, save the event`s images and calculates his number of discharges. The Neural Network was implemented using the backpropagation algorithm, and was trained with 42 training images , containing 57 lightning events (one image can have more than one lightning). TheANN was tested with one to five hidden layers, with up to 50 neurons each. The best configuration achieved a success rate of 95%, with one layer containing 20 neurons (33 test images with 42 events were used in this phase). This configuration was implemented in the developed system to analyze 20 video files, containing 63 lightning discharges previously manually detected. Results showed that all the lightning discharges were detected, many irrelevant events were unconsidered, and the event's number of discharges was correctly computed. The neural network used in this project achieved a success rate of 90%. The videos used in this experiment were acquired by seven video cameras installed in São Bernardo do Campo, Brazil, that continuously recorded lightning events during the summer. The cameras were disposed in a 360 loop, recording all data at a time resolution of 33ms. During this period, several convective storms were recorded.

Proposition of a Classification of Adult Patients with Hemiparesis in Chronic Phase.

PubMed

Chantraine, Frédéric; Filipetti, Paul; Schreiber, Céline; Remacle, Angélique; Kolanowski, Elisabeth; Moissenet, Florent

2016-01-01

Patients who have developed hemiparesis as a result of a central nervous system lesion, often experience reduced walking capacity and worse gait quality. Although clinically, similar gait patterns have been observed, presently, no clinically driven classification has been validated to group these patients' gait abnormalities at the level of the hip, knee and ankle joints. This study has thus intended to put forward a new gait classification for adult patients with hemiparesis in chronic phase, and to validate its discriminatory capacity. Twenty-six patients with hemiparesis were included in this observational study. Following a clinical examination, a clinical gait analysis, complemented by a video analysis, was performed whereby participants were requested to walk spontaneously on a 10m walkway. A patient's classification was established from clinical examination data and video analysis. This classification was made up of three groups, including two sub-groups, defined with key abnormalities observed whilst walking. Statistical analysis was achieved on the basis of 25 parameters resulting from the clinical gait analysis in order to assess the discriminatory characteristic of the classification as displayed by the walking speed and kinematic parameters. Results revealed that the parameters related to the discriminant criteria of the proposed classification were all significantly different between groups and subgroups. More generally, nearly two thirds of the 25 parameters showed significant differences (p<0.05) between the groups and sub-groups. However, prior to being fully validated, this classification must still be tested on a larger number of patients, and the repeatability of inter-operator measures must be assessed. This classification enables patients to be grouped on the basis of key abnormalities observed whilst walking and has the advantage of being able to be used in clinical routines without necessitating complex apparatus. In the midterm, this classification may allow a decision-tree of therapies to be developed on the basis of the group in which the patient has been categorised.

Proposition of a Classification of Adult Patients with Hemiparesis in Chronic Phase

PubMed Central

Filipetti, Paul; Remacle, Angélique; Kolanowski, Elisabeth

2016-01-01

Background Patients who have developed hemiparesis as a result of a central nervous system lesion, often experience reduced walking capacity and worse gait quality. Although clinically, similar gait patterns have been observed, presently, no clinically driven classification has been validated to group these patients’ gait abnormalities at the level of the hip, knee and ankle joints. This study has thus intended to put forward a new gait classification for adult patients with hemiparesis in chronic phase, and to validate its discriminatory capacity. Methods and Findings Twenty-six patients with hemiparesis were included in this observational study. Following a clinical examination, a clinical gait analysis, complemented by a video analysis, was performed whereby participants were requested to walk spontaneously on a 10m walkway. A patient’s classification was established from clinical examination data and video analysis. This classification was made up of three groups, including two sub-groups, defined with key abnormalities observed whilst walking. Statistical analysis was achieved on the basis of 25 parameters resulting from the clinical gait analysis in order to assess the discriminatory characteristic of the classification as displayed by the walking speed and kinematic parameters. Results revealed that the parameters related to the discriminant criteria of the proposed classification were all significantly different between groups and subgroups. More generally, nearly two thirds of the 25 parameters showed significant differences (p<0.05) between the groups and sub-groups. However, prior to being fully validated, this classification must still be tested on a larger number of patients, and the repeatability of inter-operator measures must be assessed. Conclusions This classification enables patients to be grouped on the basis of key abnormalities observed whilst walking and has the advantage of being able to be used in clinical routines without necessitating complex apparatus. In the midterm, this classification may allow a decision-tree of therapies to be developed on the basis of the group in which the patient has been categorised. PMID:27271533

Classification of Normal and Pathological Gait in Young Children Based on Foot Pressure Data.

PubMed

Guo, Guodong; Guffey, Keegan; Chen, Wenbin; Pergami, Paola

2017-01-01

Human gait recognition, an active research topic in computer vision, is generally based on data obtained from images/videos. We applied computer vision technology to classify pathology-related changes in gait in young children using a foot-pressure database collected using the GAITRite walkway system. As foot positioning changes with children's development, we also investigated the possibility of age estimation based on this data. Our results demonstrate that the data collected by the GAITRite system can be used for normal/pathological gait classification. Combining age information and normal/pathological gait classification increases the accuracy of the classifier. This novel approach could support the development of an accurate, real-time, and economic measure of gait abnormalities in children, able to provide important feedback to clinicians regarding the effect of rehabilitation interventions, and to support targeted treatment modifications.

Composite Wavelet Filters for Enhanced Automated Target Recognition

NASA Technical Reports Server (NTRS)

Chiang, Jeffrey N.; Zhang, Yuhan; Lu, Thomas T.; Chao, Tien-Hsin

2012-01-01

Automated Target Recognition (ATR) systems aim to automate target detection, recognition, and tracking. The current project applies a JPL ATR system to low-resolution sonar and camera videos taken from unmanned vehicles. These sonar images are inherently noisy and difficult to interpret, and pictures taken underwater are unreliable due to murkiness and inconsistent lighting. The ATR system breaks target recognition into three stages: 1) Videos of both sonar and camera footage are broken into frames and preprocessed to enhance images and detect Regions of Interest (ROIs). 2) Features are extracted from these ROIs in preparation for classification. 3) ROIs are classified as true or false positives using a standard Neural Network based on the extracted features. Several preprocessing, feature extraction, and training methods are tested and discussed in this paper.

Embedded security system for multi-modal surveillance in a railway carriage

NASA Astrophysics Data System (ADS)

Zouaoui, Rhalem; Audigier, Romaric; Ambellouis, Sébastien; Capman, François; Benhadda, Hamid; Joudrier, Stéphanie; Sodoyer, David; Lamarque, Thierry

2015-10-01

Public transport security is one of the main priorities of the public authorities when fighting against crime and terrorism. In this context, there is a great demand for autonomous systems able to detect abnormal events such as violent acts aboard passenger cars and intrusions when the train is parked at the depot. To this end, we present an innovative approach which aims at providing efficient automatic event detection by fusing video and audio analytics and reducing the false alarm rate compared to classical stand-alone video detection. The multi-modal system is composed of two microphones and one camera and integrates onboard video and audio analytics and fusion capabilities. On the one hand, for detecting intrusion, the system relies on the fusion of "unusual" audio events detection with intrusion detections from video processing. The audio analysis consists in modeling the normal ambience and detecting deviation from the trained models during testing. This unsupervised approach is based on clustering of automatically extracted segments of acoustic features and statistical Gaussian Mixture Model (GMM) modeling of each cluster. The intrusion detection is based on the three-dimensional (3D) detection and tracking of individuals in the videos. On the other hand, for violent events detection, the system fuses unsupervised and supervised audio algorithms with video event detection. The supervised audio technique detects specific events such as shouts. A GMM is used to catch the formant structure of a shout signal. Video analytics use an original approach for detecting aggressive motion by focusing on erratic motion patterns specific to violent events. As data with violent events is not easily available, a normality model with structured motions from non-violent videos is learned for one-class classification. A fusion algorithm based on Dempster-Shafer's theory analyses the asynchronous detection outputs and computes the degree of belief of each probable event.

Support vector machine for automatic pain recognition

NASA Astrophysics Data System (ADS)

Monwar, Md Maruf; Rezaei, Siamak

2009-02-01

Facial expressions are a key index of emotion and the interpretation of such expressions of emotion is critical to everyday social functioning. In this paper, we present an efficient video analysis technique for recognition of a specific expression, pain, from human faces. We employ an automatic face detector which detects face from the stored video frame using skin color modeling technique. For pain recognition, location and shape features of the detected faces are computed. These features are then used as inputs to a support vector machine (SVM) for classification. We compare the results with neural network based and eigenimage based automatic pain recognition systems. The experiment results indicate that using support vector machine as classifier can certainly improve the performance of automatic pain recognition system.

Classification of a wetland area along the upper Mississippi River with aerial videography

USGS Publications Warehouse

Jennings, C.A.; Vohs, P.A.; Dewey, M.R.

1992-01-01

We evaluated the use of aerial videography for classifying wetland habitats along the upper Mississippi River and found the prompt availability of habitat feature maps to be the major advantage of the video imagery technique. We successfully produced feature maps from digitized video images that generally agreed with the known distribution and areal coverages of the major habitat types independently identified and quantified with photointerpretation techniques. However, video images were not sufficiently detailed to allow us to consistently discriminate among the classes of aquatic macrophytes present or to quantify their areal coverage. Our inability to consistently distinguish among emergent, floating, and submergent macrophytes from the feature maps may have been related to the structural complexity of the site, to our limited vegetation sampling, and to limitations in video imagery. We expect that careful site selection (i.e., the desired level of resolution is available from video imagery) and additional vegetation samples (e.g., along a transect) will allow improved assignment of spectral values to specific plant types and enhance plant classification from feature maps produced from video imagery.

Hierarchical event selection for video storyboards with a case study on snooker video visualization.

PubMed

Parry, Matthew L; Legg, Philip A; Chung, David H S; Griffiths, Iwan W; Chen, Min

2011-12-01

Video storyboard, which is a form of video visualization, summarizes the major events in a video using illustrative visualization. There are three main technical challenges in creating a video storyboard, (a) event classification, (b) event selection and (c) event illustration. Among these challenges, (a) is highly application-dependent and requires a significant amount of application specific semantics to be encoded in a system or manually specified by users. This paper focuses on challenges (b) and (c). In particular, we present a framework for hierarchical event representation, and an importance-based selection algorithm for supporting the creation of a video storyboard from a video. We consider the storyboard to be an event summarization for the whole video, whilst each individual illustration on the board is also an event summarization but for a smaller time window. We utilized a 3D visualization template for depicting and annotating events in illustrations. To demonstrate the concepts and algorithms developed, we use Snooker video visualization as a case study, because it has a concrete and agreeable set of semantic definitions for events and can make use of existing techniques of event detection and 3D reconstruction in a reliable manner. Nevertheless, most of our concepts and algorithms developed for challenges (b) and (c) can be applied to other application areas. © 2010 IEEE

The Effect of Interactive Simulations on Exercise Adherence with Overweight and Obese Adults

DTIC Science & Technology

2009-12-01

integrated video game play capabilities was developed. Unique software was written and further modified to integrate the exercise equipment/ video game ...exercise bicycle with video gaming console 16. SECURITY CLASSIFICATION OF: 17. LIMITATION OF ABSTRACT 18. NUMBER OF PAGES 19a. NAME OF... video game play on exercise adherence, exercise motivation , and self-efficacy in overweight and obese Army personnel. Despite being younger. less

A deep learning pipeline for Indian dance style classification

NASA Astrophysics Data System (ADS)

Dewan, Swati; Agarwal, Shubham; Singh, Navjyoti

2018-04-01

In this paper, we address the problem of dance style classification to classify Indian dance or any dance in general. We propose a 3-step deep learning pipeline. First, we extract 14 essential joint locations of the dancer from each video frame, this helps us to derive any body region location within the frame, we use this in the second step which forms the main part of our pipeline. Here, we divide the dancer into regions of important motion in each video frame. We then extract patches centered at these regions. Main discriminative motion is captured in these patches. We stack the features from all such patches of a frame into a single vector and form our hierarchical dance pose descriptor. Finally, in the third step, we build a high level representation of the dance video using the hierarchical descriptors and train it using a Recurrent Neural Network (RNN) for classification. Our novelty also lies in the way we use multiple representations for a single video. This helps us to: (1) Overcome the RNN limitation of learning small sequences over big sequences such as dance; (2) Extract more data from the available dataset for effective deep learning by training multiple representations. Our contributions in this paper are three-folds: (1) We provide a deep learning pipeline for classification of any form of dance; (2) We prove that a segmented representation of a dance video works well with sequence learning techniques for recognition purposes; (3) We extend and refine the ICD dataset and provide a new dataset for evaluation of dance. Our model performs comparable or better in some cases than the state-of-the-art on action recognition benchmarks.

Are YouTube seizure videos misleading? Neurologists do not always agree.

PubMed

Brna, P M; Dooley, J M; Esser, M J; Perry, M S; Gordon, K E

2013-11-01

The internet has become the first stop for the public and patients to seek health-related information. Video-sharing websites are particularly important sources of information for those seeking answers about seizures and epilepsy. Because of the widespread popularity of YouTube, we sought to explore whether a seizure diagnosis and classification could reliably be applied. All videos related to "seizures" were reviewed, and irrelevant videos were excluded. The remaining 162 nonduplicate videos were analyzed by 4 independent pediatric neurologists who classified the events as epilepsy seizures, nonepileptic seizures, or indeterminate. Videos designated as epilepsy seizures were then classified into focal, generalized, or unclassified. At least 3 of the 4 reviewers agreed that 35% of the videos showed that the events were "epilepsy seizures", at least 3 of the 4 reviewers agreed that 28% of the videos demonstrated that the events were "nonepileptic seizures", and there was good agreement that 7% of the videos showed that the event was "indeterminate". Overall, interrater agreement was moderate at k=0.57 for epilepsy seizures and k=0.43 for nonepileptic seizures. For seizure classification, reviewer agreement was greatest for "generalized seizures" (k=0.45) and intermediate for "focal seizures" (k=0.27), and there was no agreement for unclassified events (k=0.026, p=0.2). Overall, neurology reviewer agreement suggests that only approximately one-third of the videos designated as "seizures" on the most popular video-sharing website, YouTube, definitely depict a seizure. Caution should be exercised in the use of such online video media for accessing educational or self-diagnosis aids for seizures. © 2013.

Testing of a Composite Wavelet Filter to Enhance Automated Target Recognition in SONAR

NASA Technical Reports Server (NTRS)

Chiang, Jeffrey N.

2011-01-01

Automated Target Recognition (ATR) systems aim to automate target detection, recognition, and tracking. The current project applies a JPL ATR system to low resolution SONAR and camera videos taken from Unmanned Underwater Vehicles (UUVs). These SONAR images are inherently noisy and difficult to interpret, and pictures taken underwater are unreliable due to murkiness and inconsistent lighting. The ATR system breaks target recognition into three stages: 1) Videos of both SONAR and camera footage are broken into frames and preprocessed to enhance images and detect Regions of Interest (ROIs). 2) Features are extracted from these ROIs in preparation for classification. 3) ROIs are classified as true or false positives using a standard Neural Network based on the extracted features. Several preprocessing, feature extraction, and training methods are tested and discussed in this report.

Hierarchical video summarization based on context clustering

NASA Astrophysics Data System (ADS)

Tseng, Belle L.; Smith, John R.

2003-11-01

A personalized video summary is dynamically generated in our video personalization and summarization system based on user preference and usage environment. The three-tier personalization system adopts the server-middleware-client architecture in order to maintain, select, adapt, and deliver rich media content to the user. The server stores the content sources along with their corresponding MPEG-7 metadata descriptions. In this paper, the metadata includes visual semantic annotations and automatic speech transcriptions. Our personalization and summarization engine in the middleware selects the optimal set of desired video segments by matching shot annotations and sentence transcripts with user preferences. Besides finding the desired contents, the objective is to present a coherent summary. There are diverse methods for creating summaries, and we focus on the challenges of generating a hierarchical video summary based on context information. In our summarization algorithm, three inputs are used to generate the hierarchical video summary output. These inputs are (1) MPEG-7 metadata descriptions of the contents in the server, (2) user preference and usage environment declarations from the user client, and (3) context information including MPEG-7 controlled term list and classification scheme. In a video sequence, descriptions and relevance scores are assigned to each shot. Based on these shot descriptions, context clustering is performed to collect consecutively similar shots to correspond to hierarchical scene representations. The context clustering is based on the available context information, and may be derived from domain knowledge or rules engines. Finally, the selection of structured video segments to generate the hierarchical summary efficiently balances between scene representation and shot selection.

Video content analysis of surgical procedures.

PubMed

Loukas, Constantinos

2018-02-01

In addition to its therapeutic benefits, minimally invasive surgery offers the potential for video recording of the operation. The videos may be archived and used later for reasons such as cognitive training, skills assessment, and workflow analysis. Methods from the major field of video content analysis and representation are increasingly applied in the surgical domain. In this paper, we review recent developments and analyze future directions in the field of content-based video analysis of surgical operations. The review was obtained from PubMed and Google Scholar search on combinations of the following keywords: 'surgery', 'video', 'phase', 'task', 'skills', 'event', 'shot', 'analysis', 'retrieval', 'detection', 'classification', and 'recognition'. The collected articles were categorized and reviewed based on the technical goal sought, type of surgery performed, and structure of the operation. A total of 81 articles were included. The publication activity is constantly increasing; more than 50% of these articles were published in the last 3 years. Significant research has been performed for video task detection and retrieval in eye surgery. In endoscopic surgery, the research activity is more diverse: gesture/task classification, skills assessment, tool type recognition, shot/event detection and retrieval. Recent works employ deep neural networks for phase and tool recognition as well as shot detection. Content-based video analysis of surgical operations is a rapidly expanding field. Several future prospects for research exist including, inter alia, shot boundary detection, keyframe extraction, video summarization, pattern discovery, and video annotation. The development of publicly available benchmark datasets to evaluate and compare task-specific algorithms is essential.

The Reliability of Classification Decisions for the Furtado-Gallagher Computerized Observational Movement Pattern Assessment System--FG-COMPASS

ERIC Educational Resources Information Center

Furtado, Ovande, Jr.; Gallagher, Jere D.

2012-01-01

Mastery of fundamental movement skills (FMS) is an important factor in preventing weight gain and increasing physical activity. To master FMS, performance evaluation is necessary. In this study, we investigated the reliability of a new observational assessment tool. In Phase I, 110 video clips of children performing five locomotor, and six…

Method of center localization for objects containing concentric arcs

NASA Astrophysics Data System (ADS)

Kuznetsova, Elena G.; Shvets, Evgeny A.; Nikolaev, Dmitry P.

2015-02-01

This paper proposes a method for automatic center location of objects containing concentric arcs. The method utilizes structure tensor analysis and voting scheme optimized with Fast Hough Transform. Two applications of the proposed method are considered: (i) wheel tracking in video-based system for automatic vehicle classification and (ii) tree growth rings analysis on a tree cross cut image.

Acoustic Scattering Classification of Zooplankton and Microstructure

DTIC Science & Technology

2002-09-30

the scattering in different areas. In some cases, siphonophores dominated the scattering; in other cases, euphausiids were the dominant scatterers...juvenile form of siphonophores ) through the use of BIOMAPER-II acoustics and video systems. Because of their fragility, these organisms are...scattering strength, total biomass, siphonophore abundance, and water temperature, throughout the water column in a one-hour section of a transect

Context aware decision support in neurosurgical oncology based on an efficient classification of endomicroscopic data.

PubMed

Li, Yachun; Charalampaki, Patra; Liu, Yong; Yang, Guang-Zhong; Giannarou, Stamatia

2018-06-13

Probe-based confocal laser endomicroscopy (pCLE) enables in vivo, in situ tissue characterisation without changes in the surgical setting and simplifies the oncological surgical workflow. The potential of this technique in identifying residual cancer tissue and improving resection rates of brain tumours has been recently verified in pilot studies. The interpretation of endomicroscopic information is challenging, particularly for surgeons who do not themselves routinely review histopathology. Also, the diagnosis can be examiner-dependent, leading to considerable inter-observer variability. Therefore, automatic tissue characterisation with pCLE would support the surgeon in establishing diagnosis as well as guide robot-assisted intervention procedures. The aim of this work is to propose a deep learning-based framework for brain tissue characterisation for context aware diagnosis support in neurosurgical oncology. An efficient representation of the context information of pCLE data is presented by exploring state-of-the-art CNN models with different tuning configurations. A novel video classification framework based on the combination of convolutional layers with long-range temporal recursion has been proposed to estimate the probability of each tumour class. The video classification accuracy is compared for different network architectures and data representation and video segmentation methods. We demonstrate the application of the proposed deep learning framework to classify Glioblastoma and Meningioma brain tumours based on endomicroscopic data. Results show significant improvement of our proposed image classification framework over state-of-the-art feature-based methods. The use of video data further improves the classification performance, achieving accuracy equal to 99.49%. This work demonstrates that deep learning can provide an efficient representation of pCLE data and accurately classify Glioblastoma and Meningioma tumours. The performance evaluation analysis shows the potential clinical value of the technique.

Computer-Aided Diagnosis Based on Convolutional Neural Network System for Colorectal Polyp Classification: Preliminary Experience.

PubMed

Komeda, Yoriaki; Handa, Hisashi; Watanabe, Tomohiro; Nomura, Takanobu; Kitahashi, Misaki; Sakurai, Toshiharu; Okamoto, Ayana; Minami, Tomohiro; Kono, Masashi; Arizumi, Tadaaki; Takenaka, Mamoru; Hagiwara, Satoru; Matsui, Shigenaga; Nishida, Naoshi; Kashida, Hiroshi; Kudo, Masatoshi

2017-01-01

Computer-aided diagnosis (CAD) is becoming a next-generation tool for the diagnosis of human disease. CAD for colon polyps has been suggested as a particularly useful tool for trainee colonoscopists, as the use of a CAD system avoids the complications associated with endoscopic resections. In addition to conventional CAD, a convolutional neural network (CNN) system utilizing artificial intelligence (AI) has been developing rapidly over the past 5 years. We attempted to generate a unique CNN-CAD system with an AI function that studied endoscopic images extracted from movies obtained with colonoscopes used in routine examinations. Here, we report our preliminary results of this novel CNN-CAD system for the diagnosis of colon polyps. A total of 1,200 images from cases of colonoscopy performed between January 2010 and December 2016 at Kindai University Hospital were used. These images were extracted from the video of actual endoscopic examinations. Additional video images from 10 cases of unlearned processes were retrospectively assessed in a pilot study. They were simply diagnosed as either an adenomatous or nonadenomatous polyp. The number of images used by AI to learn to distinguish adenomatous from nonadenomatous was 1,200:600. These images were extracted from the videos of actual endoscopic examinations. The size of each image was adjusted to 256 × 256 pixels. A 10-hold cross-validation was carried out. The accuracy of the 10-hold cross-validation is 0.751, where the accuracy is the ratio of the number of correct answers over the number of all the answers produced by the CNN. The decisions by the CNN were correct in 7 of 10 cases. A CNN-CAD system using routine colonoscopy might be useful for the rapid diagnosis of colorectal polyp classification. Further prospective studies in an in vivo setting are required to confirm the effectiveness of a CNN-CAD system in routine colonoscopy. © 2017 S. Karger AG, Basel.

ViCoMo: visual context modeling for scene understanding in video surveillance

NASA Astrophysics Data System (ADS)

Creusen, Ivo M.; Javanbakhti, Solmaz; Loomans, Marijn J. H.; Hazelhoff, Lykele B.; Roubtsova, Nadejda; Zinger, Svitlana; de With, Peter H. N.

2013-10-01

The use of contextual information can significantly aid scene understanding of surveillance video. Just detecting people and tracking them does not provide sufficient information to detect situations that require operator attention. We propose a proof-of-concept system that uses several sources of contextual information to improve scene understanding in surveillance video. The focus is on two scenarios that represent common video surveillance situations, parking lot surveillance and crowd monitoring. In the first scenario, a pan-tilt-zoom (PTZ) camera tracking system is developed for parking lot surveillance. Context is provided by the traffic sign recognition system to localize regular and handicapped parking spot signs as well as license plates. The PTZ algorithm has the ability to selectively detect and track persons based on scene context. In the second scenario, a group analysis algorithm is introduced to detect groups of people. Contextual information is provided by traffic sign recognition and region labeling algorithms and exploited for behavior understanding. In both scenarios, decision engines are used to interpret and classify the output of the subsystems and if necessary raise operator alerts. We show that using context information enables the automated analysis of complicated scenarios that were previously not possible using conventional moving object classification techniques.

Video Traffic Analysis for Abnormal Event Detection

DOT National Transportation Integrated Search

2010-01-01

We propose the use of video imaging sensors for the detection and classification of abnormal events to be used primarily for mitigation of traffic congestion. Successful detection of such events will allow for new road guidelines; for rapid deploymen...

Video traffic analysis for abnormal event detection.

DOT National Transportation Integrated Search

2010-01-01

We propose the use of video imaging sensors for the detection and classification of abnormal events to : be used primarily for mitigation of traffic congestion. Successful detection of such events will allow for : new road guidelines; for rapid deplo...

Adaptive video-based vehicle classification technique for monitoring traffic : [executive summary].

DOT National Transportation Integrated Search

2015-08-01

Federal Highway Administration (FHWA) recommends axle-based classification standards to map : passenger vehicles, single unit trucks, and multi-unit trucks, at Automatic Traffic Recorder (ATR) stations : statewide. Many state Departments of Transport...

"SmartMonitor"--an intelligent security system for the protection of individuals and small properties with the possibility of home automation.

PubMed

Frejlichowski, Dariusz; Gościewska, Katarzyna; Forczmański, Paweł; Hofman, Radosław

2014-06-05

"SmartMonitor" is an intelligent security system based on image analysis that combines the advantages of alarm, video surveillance and home automation systems. The system is a complete solution that automatically reacts to every learned situation in a pre-specified way and has various applications, e.g., home and surrounding protection against unauthorized intrusion, crime detection or supervision over ill persons. The software is based on well-known and proven methods and algorithms for visual content analysis (VCA) that were appropriately modified and adopted to fit specific needs and create a video processing model which consists of foreground region detection and localization, candidate object extraction, object classification and tracking. In this paper, the "SmartMonitor" system is presented along with its architecture, employed methods and algorithms, and object analysis approach. Some experimental results on system operation are also provided. In the paper, focus is put on one of the aforementioned functionalities of the system, namely supervision over ill persons.

Mobile-Cloud Assisted Video Summarization Framework for Efficient Management of Remote Sensing Data Generated by Wireless Capsule Sensors

PubMed Central

Mehmood, Irfan; Sajjad, Muhammad; Baik, Sung Wook

2014-01-01

Wireless capsule endoscopy (WCE) has great advantages over traditional endoscopy because it is portable and easy to use, especially in remote monitoring health-services. However, during the WCE process, the large amount of captured video data demands a significant deal of computation to analyze and retrieve informative video frames. In order to facilitate efficient WCE data collection and browsing task, we present a resource- and bandwidth-aware WCE video summarization framework that extracts the representative keyframes of the WCE video contents by removing redundant and non-informative frames. For redundancy elimination, we use Jeffrey-divergence between color histograms and inter-frame Boolean series-based correlation of color channels. To remove non-informative frames, multi-fractal texture features are extracted to assist the classification using an ensemble-based classifier. Owing to the limited WCE resources, it is impossible for the WCE system to perform computationally intensive video summarization tasks. To resolve computational challenges, mobile-cloud architecture is incorporated, which provides resizable computing capacities by adaptively offloading video summarization tasks between the client and the cloud server. The qualitative and quantitative results are encouraging and show that the proposed framework saves information transmission cost and bandwidth, as well as the valuable time of data analysts in browsing remote sensing data. PMID:25225874

Mobile-cloud assisted video summarization framework for efficient management of remote sensing data generated by wireless capsule sensors.

PubMed

Mehmood, Irfan; Sajjad, Muhammad; Baik, Sung Wook

2014-09-15

Wireless capsule endoscopy (WCE) has great advantages over traditional endoscopy because it is portable and easy to use, especially in remote monitoring health-services. However, during the WCE process, the large amount of captured video data demands a significant deal of computation to analyze and retrieve informative video frames. In order to facilitate efficient WCE data collection and browsing task, we present a resource- and bandwidth-aware WCE video summarization framework that extracts the representative keyframes of the WCE video contents by removing redundant and non-informative frames. For redundancy elimination, we use Jeffrey-divergence between color histograms and inter-frame Boolean series-based correlation of color channels. To remove non-informative frames, multi-fractal texture features are extracted to assist the classification using an ensemble-based classifier. Owing to the limited WCE resources, it is impossible for the WCE system to perform computationally intensive video summarization tasks. To resolve computational challenges, mobile-cloud architecture is incorporated, which provides resizable computing capacities by adaptively offloading video summarization tasks between the client and the cloud server. The qualitative and quantitative results are encouraging and show that the proposed framework saves information transmission cost and bandwidth, as well as the valuable time of data analysts in browsing remote sensing data.

Issues and advances in research methods on video games and cognitive abilities.

PubMed

Sobczyk, Bart; Dobrowolski, Paweł; Skorko, Maciek; Michalak, Jakub; Brzezicka, Aneta

2015-01-01

The impact of video game playing on cognitive abilities has been the focus of numerous studies over the last 10 years. Some cross-sectional comparisons indicate the cognitive advantages of video game players (VGPs) over non-players (NVGPs) and the benefits of video game trainings, while others fail to replicate these findings. Though there is an ongoing discussion over methodological practices and their impact on observable effects, some elementary issues, such as the representativeness of recruited VGP groups and lack of genre differentiation have not yet been widely addressed. In this article we present objective and declarative gameplay time data gathered from large samples in order to illustrate how playtime is distributed over VGP populations. The implications of this data are then discussed in the context of previous studies in the field. We also argue in favor of differentiating video games based on their genre when recruiting study samples, as this form of classification reflects the core mechanics that they utilize and therefore provides a measure of insight into what cognitive functions are likely to be engaged most. Additionally, we present the Covert Video Game Experience Questionnaire as an example of how this sort of classification can be applied during the recruitment process.

A habituation based approach for detection of visual changes in surveillance camera

NASA Astrophysics Data System (ADS)

Sha'abani, M. N. A. H.; Adan, N. F.; Sabani, M. S. M.; Abdullah, F.; Nadira, J. H. S.; Yasin, M. S. M.

2017-09-01

This paper investigates a habituation based approach in detecting visual changes using video surveillance systems in a passive environment. Various techniques have been introduced for dynamic environment such as motion detection, object classification and behaviour analysis. However, in a passive environment, most of the scenes recorded by the surveillance system are normal. Therefore, implementing a complex analysis all the time in the passive environment resulting on computationally expensive, especially when using a high video resolution. Thus, a mechanism of attention is required, where the system only responds to an abnormal event. This paper proposed a novelty detection mechanism in detecting visual changes and a habituation based approach to measure the level of novelty. The objective of the paper is to investigate the feasibility of the habituation based approach in detecting visual changes. Experiment results show that the approach are able to accurately detect the presence of novelty as deviations from the learned knowledge.

National Plant Diagnostic Network, Taxonomic training videos: Introduction to Aphids - Part 1

USDA-ARS?s Scientific Manuscript database

Training is a critical part of aphid (Hemiptera: Aphididae) identification. This video provides visual instruction on important subject areas for aphid examination and identification. Aphid topics such as classification, morphology, plant disease transmission, and references are discussed. This dis...

Ovarian Tumor-Stroma Interactions in an In Vivo Orthotopic Model

DTIC Science & Technology

2011-08-01

cancer cells to the novel environment. We have devised an Intravital Video Microscopy approach to this problem in which MOVCAR cells labeled with green...Ovarian cancer, gene expression, metastasis, intravital video microscopy 16. SECURITY CLASSIFICATION OF: 17. LIMITATION OF ABSTRACT 18. NUMBER...placed in a dorsal skin-fold chamber for Intravital Video Microscopy (IVM). The minced pseudo-organ tissue revascularizes and recapitulates some of the

Secure Recognition of Voice-Less Commands Using Videos

NASA Astrophysics Data System (ADS)

Yau, Wai Chee; Kumar, Dinesh Kant; Weghorn, Hans

Interest in voice recognition technologies for internet applications is growing due to the flexibility of speech-based communication. The major drawback with the use of sound for internet access with computers is that the commands will be audible to other people in the vicinity. This paper examines a secure and voice-less method for recognition of speech-based commands using video without evaluating sound signals. The proposed approach represents mouth movements in the video data using 2D spatio-temporal templates (STT). Zernike moments (ZM) are computed from STT and fed into support vector machines (SVM) to be classified into one of the utterances. The experimental results demonstrate that the proposed technique produces a high accuracy of 98% in a phoneme classification task. The proposed technique is demonstrated to be invariant to global variations of illumination level. Such a system is useful for securely interpreting user commands for internet applications on mobile devices.

Real-time interactive 3D computer stereography for recreational applications

NASA Astrophysics Data System (ADS)

Miyazawa, Atsushi; Ishii, Motonaga; Okuzawa, Kazunori; Sakamoto, Ryuuichi

2008-02-01

With the increasing calculation costs of 3D computer stereography, low-cost, high-speed implementation of the latter requires effective distribution of computing resources. In this paper, we attempt to re-classify 3D display technologies on the basis of humans' 3D perception, in order to determine what level of presence or reality is required in recreational video game systems. We then discuss the design and implementation of stereography systems in two categories of the new classification.

Visual Semantic Based 3D Video Retrieval System Using HDFS.

PubMed

Kumar, C Ranjith; Suguna, S

2016-08-01

This paper brings out a neoteric frame of reference for visual semantic based 3d video search and retrieval applications. Newfangled 3D retrieval application spotlight on shape analysis like object matching, classification and retrieval not only sticking up entirely with video retrieval. In this ambit, we delve into 3D-CBVR (Content Based Video Retrieval) concept for the first time. For this purpose, we intent to hitch on BOVW and Mapreduce in 3D framework. Instead of conventional shape based local descriptors, we tried to coalesce shape, color and texture for feature extraction. For this purpose, we have used combination of geometric & topological features for shape and 3D co-occurrence matrix for color and texture. After thriving extraction of local descriptors, TB-PCT (Threshold Based- Predictive Clustering Tree) algorithm is used to generate visual codebook and histogram is produced. Further, matching is performed using soft weighting scheme with L 2 distance function. As a final step, retrieved results are ranked according to the Index value and acknowledged to the user as a feedback .In order to handle prodigious amount of data and Efficacious retrieval, we have incorporated HDFS in our Intellection. Using 3D video dataset, we future the performance of our proposed system which can pan out that the proposed work gives meticulous result and also reduce the time intricacy.

EEG-based recognition of video-induced emotions: selecting subject-independent feature set.

PubMed

Kortelainen, Jukka; Seppänen, Tapio

2013-01-01

Emotions are fundamental for everyday life affecting our communication, learning, perception, and decision making. Including emotions into the human-computer interaction (HCI) could be seen as a significant step forward offering a great potential for developing advanced future technologies. While the electrical activity of the brain is affected by emotions, offers electroencephalogram (EEG) an interesting channel to improve the HCI. In this paper, the selection of subject-independent feature set for EEG-based emotion recognition is studied. We investigate the effect of different feature sets in classifying person's arousal and valence while watching videos with emotional content. The classification performance is optimized by applying a sequential forward floating search algorithm for feature selection. The best classification rate (65.1% for arousal and 63.0% for valence) is obtained with a feature set containing power spectral features from the frequency band of 1-32 Hz. The proposed approach substantially improves the classification rate reported in the literature. In future, further analysis of the video-induced EEG changes including the topographical differences in the spectral features is needed.

User Experience Evaluations in Rehabilitation Video Games for Children: A Systematic Mapping of the Literature.

PubMed

Rico-Olarte, Carolina; López, Diego M; Blobel, Bernd; Kepplinger, Sara

2017-01-01

In recent years, the interest in user experience (UX) evaluation methods for assessing technology solutions, especially in health systems for children with special needs like cognitive disabilities, has increased. Conduct a systematic mapping study to provide an overview in the field of UX evaluations in rehabilitation video games for children. The definition of research questions, the search for primary studies and the extraction of those studies by inclusion and exclusion criteria lead to the mapping of primary papers according to a classification scheme. Main findings from this study include the detection of the target population of the selected studies, the recognition of two different ways of evaluating UX: (i) user evaluation and (ii) system evaluation, and UX measurements and devices used. This systematic mapping specifies the research gaps identified for future research works in the area.

Studying Upper-Limb Amputee Prosthesis Use to Inform Device Design

DTIC Science & Technology

2016-10-01

study of the resulting videos led to a new prosthetics-use taxonomy that is generalizable to various levels of amputation and terminal devices. The...taxonomy was applied to classification of the recorded videos via custom tagging software with midi controller interface. The software creates...a motion capture studio and video cameras to record accurate and detailed upper body motion during a series of standardized tasks. These tasks are

Automated video-based assessment of surgical skills for training and evaluation in medical schools.

PubMed

Zia, Aneeq; Sharma, Yachna; Bettadapura, Vinay; Sarin, Eric L; Ploetz, Thomas; Clements, Mark A; Essa, Irfan

2016-09-01

Routine evaluation of basic surgical skills in medical schools requires considerable time and effort from supervising faculty. For each surgical trainee, a supervisor has to observe the trainees in person. Alternatively, supervisors may use training videos, which reduces some of the logistical overhead. All these approaches however are still incredibly time consuming and involve human bias. In this paper, we present an automated system for surgical skills assessment by analyzing video data of surgical activities. We compare different techniques for video-based surgical skill evaluation. We use techniques that capture the motion information at a coarser granularity using symbols or words, extract motion dynamics using textural patterns in a frame kernel matrix, and analyze fine-grained motion information using frequency analysis. We were successfully able to classify surgeons into different skill levels with high accuracy. Our results indicate that fine-grained analysis of motion dynamics via frequency analysis is most effective in capturing the skill relevant information in surgical videos. Our evaluations show that frequency features perform better than motion texture features, which in-turn perform better than symbol-/word-based features. Put succinctly, skill classification accuracy is positively correlated with motion granularity as demonstrated by our results on two challenging video datasets.

Issues and advances in research methods on video games and cognitive abilities

PubMed Central

Sobczyk, Bart; Dobrowolski, Paweł; Skorko, Maciek; Michalak, Jakub; Brzezicka, Aneta

2015-01-01

The impact of video game playing on cognitive abilities has been the focus of numerous studies over the last 10 years. Some cross-sectional comparisons indicate the cognitive advantages of video game players (VGPs) over non-players (NVGPs) and the benefits of video game trainings, while others fail to replicate these findings. Though there is an ongoing discussion over methodological practices and their impact on observable effects, some elementary issues, such as the representativeness of recruited VGP groups and lack of genre differentiation have not yet been widely addressed. In this article we present objective and declarative gameplay time data gathered from large samples in order to illustrate how playtime is distributed over VGP populations. The implications of this data are then discussed in the context of previous studies in the field. We also argue in favor of differentiating video games based on their genre when recruiting study samples, as this form of classification reflects the core mechanics that they utilize and therefore provides a measure of insight into what cognitive functions are likely to be engaged most. Additionally, we present the Covert Video Game Experience Questionnaire as an example of how this sort of classification can be applied during the recruitment process. PMID:26483717

Large-scale machine learning and evaluation platform for real-time traffic surveillance

NASA Astrophysics Data System (ADS)

Eichel, Justin A.; Mishra, Akshaya; Miller, Nicholas; Jankovic, Nicholas; Thomas, Mohan A.; Abbott, Tyler; Swanson, Douglas; Keller, Joel

2016-09-01

In traffic engineering, vehicle detectors are trained on limited datasets, resulting in poor accuracy when deployed in real-world surveillance applications. Annotating large-scale high-quality datasets is challenging. Typically, these datasets have limited diversity; they do not reflect the real-world operating environment. There is a need for a large-scale, cloud-based positive and negative mining process and a large-scale learning and evaluation system for the application of automatic traffic measurements and classification. The proposed positive and negative mining process addresses the quality of crowd sourced ground truth data through machine learning review and human feedback mechanisms. The proposed learning and evaluation system uses a distributed cloud computing framework to handle data-scaling issues associated with large numbers of samples and a high-dimensional feature space. The system is trained using AdaBoost on 1,000,000 Haar-like features extracted from 70,000 annotated video frames. The trained real-time vehicle detector achieves an accuracy of at least 95% for 1/2 and about 78% for 19/20 of the time when tested on ˜7,500,000 video frames. At the end of 2016, the dataset is expected to have over 1 billion annotated video frames.

Studying fish near ocean energy devices using underwater video

DOE Office of Scientific and Technical Information (OSTI.GOV)

Matzner, Shari; Hull, Ryan E.; Harker-Klimes, Genevra EL

The effects of energy devices on fish populations are not well-understood, and studying the interactions of fish with tidal and instream turbines is challenging. To address this problem, we have evaluated algorithms to automatically detect fish in underwater video and propose a semi-automated method for ocean and river energy device ecological monitoring. The key contributions of this work are the demonstration of a background subtraction algorithm (ViBE) that detected 87% of human-identified fish events and is suitable for use in a real-time system to reduce data volume, and the demonstration of a statistical model to classify detections as fish ormore » not fish that achieved a correct classification rate of 85% overall and 92% for detections larger than 5 pixels. Specific recommendations for underwater video acquisition to better facilitate automated processing are given. The recommendations will help energy developers put effective monitoring systems in place, and could lead to a standard approach that simplifies the monitoring effort and advances the scientific understanding of the ecological impacts of ocean and river energy devices.« less

Virtual Reality as an Educational and Training Tool for Medicine.

PubMed

Izard, Santiago González; Juanes, Juan A; García Peñalvo, Francisco J; Estella, Jesús Mª Gonçalvez; Ledesma, Mª José Sánchez; Ruisoto, Pablo

2018-02-01

Until very recently, we considered Virtual Reality as something that was very close, but it was still science fiction. However, today Virtual Reality is being integrated into many different areas of our lives, from videogames to different industrial use cases and, of course, it is starting to be used in medicine. There are two great general classifications for Virtual Reality. Firstly, we find a Virtual Reality in which we visualize a world completely created by computer, three-dimensional and where we can appreciate that the world we are visualizing is not real, at least for the moment as rendered images are improving very fast. Secondly, there is a Virtual Reality that basically consists of a reflection of our reality. This type of Virtual Reality is created using spherical or 360 images and videos, so we lose three-dimensional visualization capacity (until the 3D cameras are more developed), but on the other hand we gain in terms of realism in the images. We could also mention a third classification that merges the previous two, where virtual elements created by computer coexist with 360 images and videos. In this article we will show two systems that we have developed where each of them can be framed within one of the previous classifications, identifying the technologies used for their implementation as well as the advantages of each one. We will also analize how these systems can improve the current methodologies used for medical training. The implications of these developments as tools for teaching, learning and training are discussed.

Video segmentation and camera motion characterization using compressed data

NASA Astrophysics Data System (ADS)

Milanese, Ruggero; Deguillaume, Frederic; Jacot-Descombes, Alain

1997-10-01

We address the problem of automatically extracting visual indexes from videos, in order to provide sophisticated access methods to the contents of a video server. We focus on tow tasks, namely the decomposition of a video clip into uniform segments, and the characterization of each shot by camera motion parameters. For the first task we use a Bayesian classification approach to detecting scene cuts by analyzing motion vectors. For the second task a least- squares fitting procedure determines the pan/tilt/zoom camera parameters. In order to guarantee the highest processing speed, all techniques process and analyze directly MPEG-1 motion vectors, without need for video decompression. Experimental results are reported for a database of news video clips.

Pornography classification: The hidden clues in video space-time.

PubMed

Moreira, Daniel; Avila, Sandra; Perez, Mauricio; Moraes, Daniel; Testoni, Vanessa; Valle, Eduardo; Goldenstein, Siome; Rocha, Anderson

2016-11-01

As web technologies and social networks become part of the general public's life, the problem of automatically detecting pornography is into every parent's mind - nobody feels completely safe when their children go online. In this paper, we focus on video-pornography classification, a hard problem in which traditional methods often employ still-image techniques - labeling frames individually prior to a global decision. Frame-based approaches, however, ignore significant cogent information brought by motion. Here, we introduce a space-temporal interest point detector and descriptor called Temporal Robust Features (TRoF). TRoF was custom-tailored for efficient (low processing time and memory footprint) and effective (high classification accuracy and low false negative rate) motion description, particularly suited to the task at hand. We aggregate local information extracted by TRoF into a mid-level representation using Fisher Vectors, the state-of-the-art model of Bags of Visual Words (BoVW). We evaluate our original strategy, contrasting it both to commercial pornography detection solutions, and to BoVW solutions based upon other space-temporal features from the scientific literature. The performance is assessed using the Pornography-2k dataset, a new challenging pornographic benchmark, comprising 2000 web videos and 140h of video footage. The dataset is also a contribution of this work and is very assorted, including both professional and amateur content, and it depicts several genres of pornography, from cartoon to live action, with diverse behavior and ethnicity. The best approach, based on a dense application of TRoF, yields a classification error reduction of almost 79% when compared to the best commercial classifier. A sparse description relying on TRoF detector is also noteworthy, for yielding a classification error reduction of over 69%, with 19× less memory footprint than the dense solution, and yet can also be implemented to meet real-time requirements. Copyright © 2016 Elsevier Ireland Ltd. All rights reserved.

A system for verifying models and classification maps by extraction of information from a variety of data sources

NASA Technical Reports Server (NTRS)

Norikane, L.; Freeman, A.; Way, J.; Okonek, S.; Casey, R.

1992-01-01

Recent updates to a geographical information system (GIS) called VICAR (Video Image Communication and Retrieval)/IBIS are described. The system is designed to handle data from many different formats (vector, raster, tabular) and many different sources (models, radar images, ground truth surveys, optical images). All the data are referenced to a single georeference plane, and average or typical values for parameters defined within a polygonal region are stored in a tabular file, called an info file. The info file format allows tracking of data in time, maintenance of links between component data sets and the georeference image, conversion of pixel values to `actual' values (e.g., radar cross-section, luminance, temperature), graph plotting, data manipulation, generation of training vectors for classification algorithms, and comparison between actual measurements and model predictions (with ground truth data as input).

The learning curve for narrow-band imaging in the diagnosis of precancerous gastric lesions by using Web-based video.

PubMed

Dias-Silva, Diogo; Pimentel-Nunes, Pedro; Magalhães, Joana; Magalhães, Ricardo; Veloso, Nuno; Ferreira, Carlos; Figueiredo, Pedro; Moutinho, Pedro; Dinis-Ribeiro, Mário

2014-06-01

A simplified narrow-band imaging (NBI) endoscopy classification of gastric precancerous and cancerous lesions was derived and validated in a multicenter study. This classification comes with the need for dissemination through adequate training. To address the learning curve of this classification by endoscopists with differing expertise and to assess the feasibility of a YouTube-based learning program to disseminate it. Prospective study. Five centers. Six gastroenterologists (3 trainees, 3 fully trained endoscopists [FTs]). Twenty tests provided through a Web-based program containing 10 randomly ordered NBI videos of gastric mucosa were taken. Feedback was sent 7 days after every test submission. Measures of accuracy of the NBI classification throughout the time. From the first to the last 50 videos, a learning curve was observed with a 10% increase in global accuracy, for both trainees (from 64% to 74%) and FTs (from 56% to 65%). After 200 videos, sensitivity and specificity of 80% and higher for intestinal metaplasia were observed in half the participants, and a specificity for dysplasia greater than 95%, along with a relevant likelihood ratio for a positive result of 7 to 28 and likelihood ratio for a negative result of 0.21 to 0.82, were achieved by all of the participants. No constant learning curve was observed for the identification of Helicobacter pylori gastritis and sensitivity to dysplasia. The trainees had better results in all of the parameters, except specificity for dysplasia, compared with the FTs. Globally, participants agreed that the program's structure was adequate, except on the feedback, which should have consisted of a more detailed explanation of each answer. No formal sample size estimate. A Web-based learning program could be used to teach and disseminate classifications in the endoscopy field. In this study, an NBI classification for gastric mucosal features seems to be easily learned for the identification of gastric preneoplastic lesions. Copyright © 2014 American Society for Gastrointestinal Endoscopy. Published by Mosby, Inc. All rights reserved.

Retrieval evaluation and distance learning from perceived similarity between endomicroscopy videos.

PubMed

André, Barbara; Vercauteren, Tom; Buchner, Anna M; Wallace, Michael B; Ayache, Nicholas

2011-01-01

Evaluating content-based retrieval (CBR) is challenging because it requires an adequate ground-truth. When the available groundtruth is limited to textual metadata such as pathological classes, retrieval results can only be evaluated indirectly, for example in terms of classification performance. In this study we first present a tool to generate perceived similarity ground-truth that enables direct evaluation of endomicroscopic video retrieval. This tool uses a four-points Likert scale and collects subjective pairwise similarities perceived by multiple expert observers. We then evaluate against the generated ground-truth a previously developed dense bag-of-visual-words method for endomicroscopic video retrieval. Confirming the results of previous indirect evaluation based on classification, our direct evaluation shows that this method significantly outperforms several other state-of-the-art CBR methods. In a second step, we propose to improve the CBR method by learning an adjusted similarity metric from the perceived similarity ground-truth. By minimizing a margin-based cost function that differentiates similar and dissimilar video pairs, we learn a weight vector applied to the visual word signatures of videos. Using cross-validation, we demonstrate that the learned similarity distance is significantly better correlated with the perceived similarity than the original visual-word-based distance.

Development of a Kinect Software Tool to Classify Movements during Active Video Gaming.

PubMed

Rosenberg, Michael; Thornton, Ashleigh L; Lay, Brendan S; Ward, Brodie; Nathan, David; Hunt, Daniel; Braham, Rebecca

2016-01-01

While it has been established that using full body motion to play active video games results in increased levels of energy expenditure, there is little information on the classification of human movement during active video game play in relationship to fundamental movement skills. The aim of this study was to validate software utilising Kinect sensor motion capture technology to recognise fundamental movement skills (FMS), during active video game play. Two human assessors rated jumping and side-stepping and these assessments were compared to the Kinect Action Recognition Tool (KART), to establish a level of agreement and determine the number of movements completed during five minutes of active video game play, for 43 children (m = 12 years 7 months ± 1 year 6 months). During five minutes of active video game play, inter-rater reliability, when examining the two human raters, was found to be higher for the jump (r = 0.94, p < .01) than the sidestep (r = 0.87, p < .01), although both were excellent. Excellent reliability was also found between human raters and the KART system for the jump (r = 0.84, p, .01) and moderate reliability for sidestep (r = 0.6983, p < .01) during game play, demonstrating that both humans and KART had higher agreement for jumps than sidesteps in the game play condition. The results of the study provide confidence that the Kinect sensor can be used to count the number of jumps and sidestep during five minutes of active video game play with a similar level of accuracy as human raters. However, in contrast to humans, the KART system required a fraction of the time to analyse and tabulate the results.

Development of a Kinect Software Tool to Classify Movements during Active Video Gaming

PubMed Central

Rosenberg, Michael; Lay, Brendan S.; Ward, Brodie; Nathan, David; Hunt, Daniel; Braham, Rebecca

2016-01-01

While it has been established that using full body motion to play active video games results in increased levels of energy expenditure, there is little information on the classification of human movement during active video game play in relationship to fundamental movement skills. The aim of this study was to validate software utilising Kinect sensor motion capture technology to recognise fundamental movement skills (FMS), during active video game play. Two human assessors rated jumping and side-stepping and these assessments were compared to the Kinect Action Recognition Tool (KART), to establish a level of agreement and determine the number of movements completed during five minutes of active video game play, for 43 children (m = 12 years 7 months ± 1 year 6 months). During five minutes of active video game play, inter-rater reliability, when examining the two human raters, was found to be higher for the jump (r = 0.94, p < .01) than the sidestep (r = 0.87, p < .01), although both were excellent. Excellent reliability was also found between human raters and the KART system for the jump (r = 0.84, p, .01) and moderate reliability for sidestep (r = 0.6983, p < .01) during game play, demonstrating that both humans and KART had higher agreement for jumps than sidesteps in the game play condition. The results of the study provide confidence that the Kinect sensor can be used to count the number of jumps and sidestep during five minutes of active video game play with a similar level of accuracy as human raters. However, in contrast to humans, the KART system required a fraction of the time to analyse and tabulate the results. PMID:27442437

Video Segmentation Descriptors for Event Recognition

DTIC Science & Technology

2014-12-08

Velastin, 3D Extended Histogram of Oriented Gradients (3DHOG) for Classification of Road Users in Urban Scenes , BMVC, 2009. [3] M.-Y. Chen and A. Hauptmann...computed on 3D volume outputted by the hierarchical segmentation . Each video is described as follows. Each supertube is temporally divided in n-frame...strength of these descriptors is their adaptability to the scene variations since they are grounded on a video segmentation . This makes them naturally robust

“SmartMonitor” — An Intelligent Security System for the Protection of Individuals and Small Properties with the Possibility of Home Automation

PubMed Central

Frejlichowski, Dariusz; Gościewska, Katarzyna; Forczmański, Paweł; Hofman, Radosław

2014-01-01

“SmartMonitor” is an intelligent security system based on image analysis that combines the advantages of alarm, video surveillance and home automation systems. The system is a complete solution that automatically reacts to every learned situation in a pre-specified way and has various applications, e.g., home and surrounding protection against unauthorized intrusion, crime detection or supervision over ill persons. The software is based on well-known and proven methods and algorithms for visual content analysis (VCA) that were appropriately modified and adopted to fit specific needs and create a video processing model which consists of foreground region detection and localization, candidate object extraction, object classification and tracking. In this paper, the “SmartMonitor” system is presented along with its architecture, employed methods and algorithms, and object analysis approach. Some experimental results on system operation are also provided. In the paper, focus is put on one of the aforementioned functionalities of the system, namely supervision over ill persons. PMID:24905854

Vehicle detection in aerial surveillance using dynamic Bayesian networks.

PubMed

Cheng, Hsu-Yung; Weng, Chih-Chia; Chen, Yi-Ying

2012-04-01

We present an automatic vehicle detection system for aerial surveillance in this paper. In this system, we escape from the stereotype and existing frameworks of vehicle detection in aerial surveillance, which are either region based or sliding window based. We design a pixelwise classification method for vehicle detection. The novelty lies in the fact that, in spite of performing pixelwise classification, relations among neighboring pixels in a region are preserved in the feature extraction process. We consider features including vehicle colors and local features. For vehicle color extraction, we utilize a color transform to separate vehicle colors and nonvehicle colors effectively. For edge detection, we apply moment preserving to adjust the thresholds of the Canny edge detector automatically, which increases the adaptability and the accuracy for detection in various aerial images. Afterward, a dynamic Bayesian network (DBN) is constructed for the classification purpose. We convert regional local features into quantitative observations that can be referenced when applying pixelwise classification via DBN. Experiments were conducted on a wide variety of aerial videos. The results demonstrate flexibility and good generalization abilities of the proposed method on a challenging data set with aerial surveillance images taken at different heights and under different camera angles.

Gender Recognition from Human-Body Images Using Visible-Light and Thermal Camera Videos Based on a Convolutional Neural Network for Image Feature Extraction

PubMed Central

Nguyen, Dat Tien; Kim, Ki Wan; Hong, Hyung Gil; Koo, Ja Hyung; Kim, Min Cheol; Park, Kang Ryoung

2017-01-01

Extracting powerful image features plays an important role in computer vision systems. Many methods have previously been proposed to extract image features for various computer vision applications, such as the scale-invariant feature transform (SIFT), speed-up robust feature (SURF), local binary patterns (LBP), histogram of oriented gradients (HOG), and weighted HOG. Recently, the convolutional neural network (CNN) method for image feature extraction and classification in computer vision has been used in various applications. In this research, we propose a new gender recognition method for recognizing males and females in observation scenes of surveillance systems based on feature extraction from visible-light and thermal camera videos through CNN. Experimental results confirm the superiority of our proposed method over state-of-the-art recognition methods for the gender recognition problem using human body images. PMID:28335510

Gender Recognition from Human-Body Images Using Visible-Light and Thermal Camera Videos Based on a Convolutional Neural Network for Image Feature Extraction.

PubMed

Nguyen, Dat Tien; Kim, Ki Wan; Hong, Hyung Gil; Koo, Ja Hyung; Kim, Min Cheol; Park, Kang Ryoung

2017-03-20

Extracting powerful image features plays an important role in computer vision systems. Many methods have previously been proposed to extract image features for various computer vision applications, such as the scale-invariant feature transform (SIFT), speed-up robust feature (SURF), local binary patterns (LBP), histogram of oriented gradients (HOG), and weighted HOG. Recently, the convolutional neural network (CNN) method for image feature extraction and classification in computer vision has been used in various applications. In this research, we propose a new gender recognition method for recognizing males and females in observation scenes of surveillance systems based on feature extraction from visible-light and thermal camera videos through CNN. Experimental results confirm the superiority of our proposed method over state-of-the-art recognition methods for the gender recognition problem using human body images.

Affective State Level Recognition in Naturalistic Facial and Vocal Expressions.

PubMed

Meng, Hongying; Bianchi-Berthouze, Nadia

2014-03-01

Naturalistic affective expressions change at a rate much slower than the typical rate at which video or audio is recorded. This increases the probability that consecutive recorded instants of expressions represent the same affective content. In this paper, we exploit such a relationship to improve the recognition performance of continuous naturalistic affective expressions. Using datasets of naturalistic affective expressions (AVEC 2011 audio and video dataset, PAINFUL video dataset) continuously labeled over time and over different dimensions, we analyze the transitions between levels of those dimensions (e.g., transitions in pain intensity level). We use an information theory approach to show that the transitions occur very slowly and hence suggest modeling them as first-order Markov models. The dimension levels are considered to be the hidden states in the Hidden Markov Model (HMM) framework. Their discrete transition and emission matrices are trained by using the labels provided with the training set. The recognition problem is converted into a best path-finding problem to obtain the best hidden states sequence in HMMs. This is a key difference from previous use of HMMs as classifiers. Modeling of the transitions between dimension levels is integrated in a multistage approach, where the first level performs a mapping between the affective expression features and a soft decision value (e.g., an affective dimension level), and further classification stages are modeled as HMMs that refine that mapping by taking into account the temporal relationships between the output decision labels. The experimental results for each of the unimodal datasets show overall performance to be significantly above that of a standard classification system that does not take into account temporal relationships. In particular, the results on the AVEC 2011 audio dataset outperform all other systems presented at the international competition.

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

PubMed Central

Thounaojam, Dalton Meitei; Khelchandra, Thongam; Singh, Kh. Manglem; 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. PMID:27127500

Pancreatic abnormalities detected by endoscopic ultrasound (EUS) in patients without clinical signs of pancreatic disease: any difference between standard and Rosemont classification scoring?

PubMed

Petrone, Maria Chiara; Terracciano, Fulvia; Perri, Francesco; Carrara, Silvia; Cavestro, Giulia Martina; Mariani, Alberto; Testoni, Pier Alberto; Arcidiacono, Paolo Giorgio

2014-01-01

The prevalence of nine EUS features of chronic pancreatitis (CP) according to the standard Wiersema classification has been investigated in 489 patients undergoing EUS for an indication not related to pancreatico-biliary disease. We showed that 82 subjects (16.8%) had at least one ductular or parenchymal abnormality. Among them, 18 (3.7% of study population) had ≥3 Wiersema criteria suggestive of CP. Recently, a new classification (Rosemont) of EUS findings consistent, suggestive or indeterminate for CP has been proposed. To stratify healthy subjects into different subgroups on the basis of EUS features of CP according to the Wiersema and Rosemont classifications and to evaluate the agreement in the diagnosis of CP with the two scoring systems. Weighted kappa statistics was computed to evaluate the strength of agreement between the two scoring systems. Univariate and multivariate analysis between any EUS abnormality and habits were performed. Eighty-two EUS videos were reviewed. Using the Wiersema classification, 18 subjects showed ≥3 EUS features suggestive of CP. The EUS diagnosis of CP in these 18 subjects was considered as consistent in only one patient, according to Rosemont classification. Weighted Kappa statistics was 0.34 showing that the strength of agreement was 'fair'. Alcohol use and smoking were identified as risk factors for having pancreatic abnormalities on EUS. The prevalence of EUS features consistent or suggestive of CP in healthy subjects according to the Rosemont classification is lower than that assessed by Wiersema criteria. In that regard the Rosemont classification seems to be more accurate in excluding clinically relevant CP. Overall agreement between the two classifications is fair. Copyright © 2014 IAP and EPC. Published by Elsevier B.V. All rights reserved.

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

NASA Astrophysics Data System (ADS)

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.

GISentinel: a software platform for automatic ulcer detection on capsule endoscopy videos

NASA Astrophysics Data System (ADS)

Yi, Steven; Jiao, Heng; Meng, Fan; Leighton, Jonathon A.; Shabana, Pasha; Rentz, Lauri

2014-03-01

In this paper, we present a novel and clinically valuable software platform for automatic ulcer detection on gastrointestinal (GI) tract from Capsule Endoscopy (CE) videos. Typical CE videos take about 8 hours. They have to be reviewed manually by physicians to detect and locate diseases such as ulcers and bleedings. The process is time consuming. Moreover, because of the long-time manual review, it is easy to lead to miss-finding. Working with our collaborators, we were focusing on developing a software platform called GISentinel, which can fully automated GI tract ulcer detection and classification. This software includes 3 parts: the frequency based Log-Gabor filter regions of interest (ROI) extraction, the unique feature selection and validation method (e.g. illumination invariant feature, color independent features, and symmetrical texture features), and the cascade SVM classification for handling "ulcer vs. non-ulcer" cases. After the experiments, this SW gave descent results. In frame-wise, the ulcer detection rate is 69.65% (319/458). In instance-wise, the ulcer detection rate is 82.35%(28/34).The false alarm rate is 16.43% (34/207). This work is a part of our innovative 2D/3D based GI tract disease detection software platform. The final goal of this SW is to find and classification of major GI tract diseases intelligently, such as bleeding, ulcer, and polyp from the CE videos. This paper will mainly describe the automatic ulcer detection functional module.

A Sieving ANN for Emotion-Based Movie Clip Classification

NASA Astrophysics Data System (ADS)

Watanapa, Saowaluk C.; Thipakorn, Bundit; Charoenkitkarn, Nipon

Effective classification and analysis of semantic contents are very important for the content-based indexing and retrieval of video database. Our research attempts to classify movie clips into three groups of commonly elicited emotions, namely excitement, joy and sadness, based on a set of abstract-level semantic features extracted from the film sequence. In particular, these features consist of six visual and audio measures grounded on the artistic film theories. A unique sieving-structured neural network is proposed to be the classifying model due to its robustness. The performance of the proposed model is tested with 101 movie clips excerpted from 24 award-winning and well-known Hollywood feature films. The experimental result of 97.8% correct classification rate, measured against the collected human-judges, indicates the great potential of using abstract-level semantic features as an engineered tool for the application of video-content retrieval/indexing.

Fractal measures of video-recorded trajectories can classify motor subtypes in Parkinson's Disease

NASA Astrophysics Data System (ADS)

Figueiredo, Thiago C.; Vivas, Jamile; Peña, Norberto; Miranda, José G. V.

2016-11-01

Parkinson's Disease is one of the most prevalent neurodegenerative diseases in the world and affects millions of individuals worldwide. The clinical criteria for classification of motor subtypes in Parkinson's Disease are subjective and may be misleading when symptoms are not clearly identifiable. A video recording protocol was used to measure hand tremor of 14 individuals with Parkinson's Disease and 7 healthy subjects. A method for motor subtype classification was proposed based on the spectral distribution of the movement and compared with the existing clinical criteria. Box-counting dimension and Hurst Exponent calculated from the trajectories were used as the relevant measures for the statistical tests. The classification based on the power-spectrum is shown to be well suited to separate patients with and without tremor from healthy subjects and could provide clinicians with a tool to aid in the diagnosis of patients in an early stage of the disease.

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

PubMed

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.

Affective Video Retrieval: Violence Detection in Hollywood Movies by Large-Scale Segmental Feature Extraction

PubMed Central

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. PMID:24391704

The Camera-Based Assessment Survey System (C-BASS): A towed camera platform for reef fish abundance surveys and benthic habitat characterization in the Gulf of Mexico

NASA Astrophysics Data System (ADS)

Lembke, Chad; Grasty, Sarah; Silverman, Alex; Broadbent, Heather; Butcher, Steven; Murawski, Steven

2017-12-01

An ongoing challenge for fisheries management is to provide cost-effective and timely estimates of habitat stratified fish densities. Traditional approaches use modified commercial fishing gear (such as trawls and baited hooks) that have biases in species selectivity and may also be inappropriate for deployment in some habitat types. Underwater visual and optical approaches offer the promise of more precise and less biased assessments of relative fish abundance, as well as direct estimates of absolute fish abundance. A number of video-based approaches have been developed and the technology for data acquisition, calibration, and synthesis has been developing rapidly. Beginning in 2012, our group of engineers and researchers at the University of South Florida has been working towards the goal of completing large scale, video-based surveys in the eastern Gulf of Mexico. This paper discusses design considerations and development of a towed camera system for collection of video-based data on commercially and recreationally important reef fishes and benthic habitat on the West Florida Shelf. Factors considered during development included potential habitat types to be assessed, sea-floor bathymetry, vessel support requirements, personnel requirements, and cost-effectiveness of system components. This regional-specific effort has resulted in a towed platform called the Camera-Based Assessment Survey System, or C-BASS, which has proven capable of surveying tens of kilometers of video transects per day and has the ability to cost-effective population estimates of reef fishes and coincident benthic habitat classification.

Games for Training: Leveraging Commercial Off the Shelf Multiplayer Gaming Software for Infantry Squad Collective Training

DTIC Science & Technology

2005-09-01

squad training, team training, dismounted training, video games , computer games, multiplayer games. 16. PRICE CODE 17. SECURITY CLASSIFICATION OF...Multiplayer - mode of play for computer and video games in which multiple people can play the same game at the same time (Wikipedia, 2005) D...that “improvements in 3-D image generation on the PC and the speed of the internet” have increased the military’s interest in the use of video games as

Pedestrian detection in video surveillance using fully convolutional YOLO neural network

NASA Astrophysics Data System (ADS)

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.

Video and accelerometer-based motion analysis for automated surgical skills assessment.

PubMed

Zia, Aneeq; Sharma, Yachna; Bettadapura, Vinay; Sarin, Eric L; Essa, Irfan

2018-03-01

Basic surgical skills of suturing and knot tying are an essential part of medical training. Having an automated system for surgical skills assessment could help save experts time and improve training efficiency. There have been some recent attempts at automated surgical skills assessment using either video analysis or acceleration data. In this paper, we present a novel approach for automated assessment of OSATS-like surgical skills and provide an analysis of different features on multi-modal data (video and accelerometer data). We conduct a large study for basic surgical skill assessment on a dataset that contained video and accelerometer data for suturing and knot-tying tasks. We introduce "entropy-based" features-approximate entropy and cross-approximate entropy, which quantify the amount of predictability and regularity of fluctuations in time series data. The proposed features are compared to existing methods of Sequential Motion Texture, Discrete Cosine Transform and Discrete Fourier Transform, for surgical skills assessment. We report average performance of different features across all applicable OSATS-like criteria for suturing and knot-tying tasks. Our analysis shows that the proposed entropy-based features outperform previous state-of-the-art methods using video data, achieving average classification accuracies of 95.1 and 92.2% for suturing and knot tying, respectively. For accelerometer data, our method performs better for suturing achieving 86.8% average accuracy. We also show that fusion of video and acceleration features can improve overall performance for skill assessment. Automated surgical skills assessment can be achieved with high accuracy using the proposed entropy features. Such a system can significantly improve the efficiency of surgical training in medical schools and teaching hospitals.

Combining high-speed SVM learning with CNN feature encoding for real-time target recognition in high-definition video for ISR missions

NASA Astrophysics Data System (ADS)

Kroll, Christine; von der Werth, Monika; Leuck, Holger; Stahl, Christoph; Schertler, Klaus

2017-05-01

For Intelligence, Surveillance, Reconnaissance (ISR) missions of manned and unmanned air systems typical electrooptical payloads provide high-definition video data which has to be exploited with respect to relevant ground targets in real-time by automatic/assisted target recognition software. Airbus Defence and Space is developing required technologies for real-time sensor exploitation since years and has combined the latest advances of Deep Convolutional Neural Networks (CNN) with a proprietary high-speed Support Vector Machine (SVM) learning method into a powerful object recognition system with impressive results on relevant high-definition video scenes compared to conventional target recognition approaches. This paper describes the principal requirements for real-time target recognition in high-definition video for ISR missions and the Airbus approach of combining an invariant feature extraction using pre-trained CNNs and the high-speed training and classification ability of a novel frequency-domain SVM training method. The frequency-domain approach allows for a highly optimized implementation for General Purpose Computation on a Graphics Processing Unit (GPGPU) and also an efficient training of large training samples. The selected CNN which is pre-trained only once on domain-extrinsic data reveals a highly invariant feature extraction. This allows for a significantly reduced adaptation and training of the target recognition method for new target classes and mission scenarios. A comprehensive training and test dataset was defined and prepared using relevant high-definition airborne video sequences. The assessment concept is explained and performance results are given using the established precision-recall diagrams, average precision and runtime figures on representative test data. A comparison to legacy target recognition approaches shows the impressive performance increase by the proposed CNN+SVM machine-learning approach and the capability of real-time high-definition video exploitation.

A Semi-supervised Heat Kernel Pagerank MBO Algorithm for Data Classification

DTIC Science & Technology

2016-07-01

financial predictions, etc. and is finding growing use in text mining studies. In this paper, we present an efficient algorithm for classification of high...video data, set of images, hyperspectral data, medical data, text data, etc. Moreover, the framework provides a way to analyze data whose different...also be incorporated. For text classification, one can use tfidf (term frequency inverse document frequency) to form feature vectors for each document

A framework for the recognition of high-level surgical tasks from video images for cataract surgeries

PubMed Central

Lalys, Florent; Riffaud, Laurent; Bouget, David; Jannin, Pierre

2012-01-01

The need for a better integration of the new generation of Computer-Assisted-Surgical (CAS) systems has been recently emphasized. One necessity to achieve this objective is to retrieve data from the Operating Room (OR) with different sensors, then to derive models from these data. Recently, the use of videos from cameras in the OR has demonstrated its efficiency. In this paper, we propose a framework to assist in the development of systems for the automatic recognition of high level surgical tasks using microscope videos analysis. We validated its use on cataract procedures. The idea is to combine state-of-the-art computer vision techniques with time series analysis. The first step of the framework consisted in the definition of several visual cues for extracting semantic information, therefore characterizing each frame of the video. Five different pieces of image-based classifiers were therefore implemented. A step of pupil segmentation was also applied for dedicated visual cue detection. Time series classification algorithms were then applied to model time-varying data. Dynamic Time Warping (DTW) and Hidden Markov Models (HMM) were tested. This association combined the advantages of all methods for better understanding of the problem. The framework was finally validated through various studies. Six binary visual cues were chosen along with 12 phases to detect, obtaining accuracies of 94%. PMID:22203700

Optimal path planning for video-guided smart munitions via multitarget tracking

NASA Astrophysics Data System (ADS)

Borkowski, Jeffrey M.; Vasquez, Juan R.

2006-05-01

An advent in the development of smart munitions entails autonomously modifying target selection during flight in order to maximize the value of the target being destroyed. A unique guidance law can be constructed that exploits both attribute and kinematic data obtained from an onboard video sensor. An optimal path planning algorithm has been developed with the goals of obstacle avoidance and maximizing the value of the target impacted by the munition. Target identification and classification provides a basis for target value which is used in conjunction with multi-target tracks to determine an optimal waypoint for the munition. A dynamically feasible trajectory is computed to provide constraints on the waypoint selection. Results demonstrate the ability of the autonomous system to avoid moving obstacles and revise target selection in flight.

Large-Scale Machine Learning for Classification and Search

ERIC Educational Resources Information Center

Liu, Wei

2012-01-01

With the rapid development of the Internet, nowadays tremendous amounts of data including images and videos, up to millions or billions, can be collected for training machine learning models. Inspired by this trend, this thesis is dedicated to developing large-scale machine learning techniques for the purpose of making classification and nearest…

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

NASA Astrophysics Data System (ADS)

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.

Towards human behavior recognition based on spatio temporal features and support vector machines

NASA Astrophysics Data System (ADS)

Ghabri, Sawsen; Ouarda, Wael; Alimi, Adel M.

2017-03-01

Security and surveillance are vital issues in today's world. The recent acts of terrorism have highlighted the urgent need for efficient surveillance. There is indeed a need for an automated system for video surveillance which can detect identity and activity of person. In this article, we propose a new paradigm to recognize an aggressive human behavior such as boxing action. Our proposed system for human activity detection includes the use of a fusion between Spatio Temporal Interest Point (STIP) and Histogram of Oriented Gradient (HoG) features. The novel feature called Spatio Temporal Histogram Oriented Gradient (STHOG). To evaluate the robustness of our proposed paradigm with a local application of HoG technique on STIP points, we made experiments on KTH human action dataset based on Multi Class Support Vector Machines classification. The proposed scheme outperforms basic descriptors like HoG and STIP to achieve 82.26% us an accuracy value of classification rate.

Computer vision-based classification of hand grip variations in neurorehabilitation.

PubMed

Zariffa, José; Steeves, John D

2011-01-01

The complexity of hand function is such that most existing upper limb rehabilitation robotic devices use only simplified hand interfaces. This is in contrast to the importance of the hand in regaining function after neurological injury. Computer vision technology has been used to identify hand posture in the field of Human Computer Interaction, but this approach has not been translated to the rehabilitation context. We describe a computer vision-based classifier that can be used to discriminate rehabilitation-relevant hand postures, and could be integrated into a virtual reality-based upper limb rehabilitation system. The proposed system was tested on a set of video recordings from able-bodied individuals performing cylindrical grasps, lateral key grips, and tip-to-tip pinches. The overall classification success rate was 91.2%, and was above 98% for 6 out of the 10 subjects. © 2011 IEEE

Local feature saliency classifier for real-time intrusion monitoring

NASA Astrophysics Data System (ADS)

Buch, Norbert; Velastin, Sergio A.

2014-07-01

We propose a texture saliency classifier to detect people in a video frame by identifying salient texture regions. The image is classified into foreground and background in real time. No temporal image information is used during the classification. The system is used for the task of detecting people entering a sterile zone, which is a common scenario for visual surveillance. Testing is performed on the Imagery Library for Intelligent Detection Systems sterile zone benchmark dataset of the United Kingdom's Home Office. The basic classifier is extended by fusing its output with simple motion information, which significantly outperforms standard motion tracking. A lower detection time can be achieved by combining texture classification with Kalman filtering. The fusion approach running at 10 fps gives the highest result of F1=0.92 for the 24-h test dataset. The paper concludes with a detailed analysis of the computation time required for the different parts of the algorithm.

Computationally efficient target classification in multispectral image data with Deep Neural Networks

NASA Astrophysics Data System (ADS)

Cavigelli, Lukas; Bernath, Dominic; Magno, Michele; Benini, Luca

2016-10-01

Detecting and classifying targets in video streams from surveillance cameras is a cumbersome, error-prone and expensive task. Often, the incurred costs are prohibitive for real-time monitoring. This leads to data being stored locally or transmitted to a central storage site for post-incident examination. The required communication links and archiving of the video data are still expensive and this setup excludes preemptive actions to respond to imminent threats. An effective way to overcome these limitations is to build a smart camera that analyzes the data on-site, close to the sensor, and transmits alerts when relevant video sequences are detected. Deep neural networks (DNNs) have come to outperform humans in visual classifications tasks and are also performing exceptionally well on other computer vision tasks. The concept of DNNs and Convolutional Networks (ConvNets) can easily be extended to make use of higher-dimensional input data such as multispectral data. We explore this opportunity in terms of achievable accuracy and required computational effort. To analyze the precision of DNNs for scene labeling in an urban surveillance scenario we have created a dataset with 8 classes obtained in a field experiment. We combine an RGB camera with a 25-channel VIS-NIR snapshot sensor to assess the potential of multispectral image data for target classification. We evaluate several new DNNs, showing that the spectral information fused together with the RGB frames can be used to improve the accuracy of the system or to achieve similar accuracy with a 3x smaller computation effort. We achieve a very high per-pixel accuracy of 99.1%. Even for scarcely occurring, but particularly interesting classes, such as cars, 75% of the pixels are labeled correctly with errors occurring only around the border of the objects. This high accuracy was obtained with a training set of only 30 labeled images, paving the way for fast adaptation to various application scenarios.

Automated intelligent video surveillance system for ships

NASA Astrophysics Data System (ADS)

Wei, Hai; Nguyen, Hieu; Ramu, Prakash; Raju, Chaitanya; Liu, Xiaoqing; Yadegar, Jacob

2009-05-01

To protect naval and commercial ships from attack by terrorists and pirates, it is important to have automatic surveillance systems able to detect, identify, track and alert the crew on small watercrafts that might pursue malicious intentions, while ruling out non-threat entities. Radar systems have limitations on the minimum detectable range and lack high-level classification power. In this paper, we present an innovative Automated Intelligent Video Surveillance System for Ships (AIVS3) as a vision-based solution for ship security. Capitalizing on advanced computer vision algorithms and practical machine learning methodologies, the developed AIVS3 is not only capable of efficiently and robustly detecting, classifying, and tracking various maritime targets, but also able to fuse heterogeneous target information to interpret scene activities, associate targets with levels of threat, and issue the corresponding alerts/recommendations to the man-in- the-loop (MITL). AIVS3 has been tested in various maritime scenarios and shown accurate and effective threat detection performance. By reducing the reliance on human eyes to monitor cluttered scenes, AIVS3 will save the manpower while increasing the accuracy in detection and identification of asymmetric attacks for ship protection.

Modulation Classification of Satellite Communication Signals Using Cumulants and Neural Networks

NASA Technical Reports Server (NTRS)

Smith, Aaron; Evans, Michael; Downey, Joseph

2017-01-01

National Aeronautics and Space Administration (NASA)'s future communication architecture is evaluating cognitive technologies and increased system intelligence. These technologies are expected to reduce the operational complexity of the network, increase science data return, and reduce interference to self and others. In order to increase situational awareness, signal classification algorithms could be applied to identify users and distinguish sources of interference. A significant amount of previous work has been done in the area of automatic signal classification for military and commercial applications. As a preliminary step, we seek to develop a system with the ability to discern signals typically encountered in satellite communication. Proposed is an automatic modulation classifier which utilizes higher order statistics (cumulants) and an estimate of the signal-to-noise ratio. These features are extracted from baseband symbols and then processed by a neural network for classification. The modulation types considered are phase-shift keying (PSK), amplitude and phase-shift keying (APSK),and quadrature amplitude modulation (QAM). Physical layer properties specific to the Digital Video Broadcasting - Satellite- Second Generation (DVB-S2) standard, such as pilots and variable ring ratios, are also considered. This paper will provide simulation results of a candidate modulation classifier, and performance will be evaluated over a range of signal-to-noise ratios, frequency offsets, and nonlinear amplifier distortions.

Robust representation and recognition of facial emotions using extreme sparse learning.

PubMed

Shojaeilangari, Seyedehsamaneh; Yau, Wei-Yun; Nandakumar, Karthik; Li, Jun; Teoh, Eam Khwang

2015-07-01

Recognition of natural emotions from human faces is an interesting topic with a wide range of potential applications, such as human-computer interaction, automated tutoring systems, image and video retrieval, smart environments, and driver warning systems. Traditionally, facial emotion recognition systems have been evaluated on laboratory controlled data, which is not representative of the environment faced in real-world applications. To robustly recognize the facial emotions in real-world natural situations, this paper proposes an approach called extreme sparse learning, which has the ability to jointly learn a dictionary (set of basis) and a nonlinear classification model. The proposed approach combines the discriminative power of extreme learning machine with the reconstruction property of sparse representation to enable accurate classification when presented with noisy signals and imperfect data recorded in natural settings. In addition, this paper presents a new local spatio-temporal descriptor that is distinctive and pose-invariant. The proposed framework is able to achieve the state-of-the-art recognition accuracy on both acted and spontaneous facial emotion databases.

Research Instruments

NASA Technical Reports Server (NTRS)

1992-01-01

The GENETI-SCANNER, newest product of Perceptive Scientific Instruments, Inc. (PSI), rapidly scans slides, locates, digitizes, measures and classifies specific objects and events in research and diagnostic applications. Founded by former NASA employees, PSI's primary product line is based on NASA image processing technology. The instruments karyotype - a process employed in analysis and classification of chromosomes - using a video camera mounted on a microscope. Images are digitized, enabling chromosome image enhancement. The system enables karyotyping to be done significantly faster, increasing productivity and lowering costs. Product is no longer being manufactured.

Video occupant detection and classification

DOEpatents

Krumm, John C.

1999-01-01

A system for determining when it is not safe to arm a vehicle airbag by storing representations of known situations as observed by a camera at a passenger seat; and comparing a representation of a camera output of the current situation to the stored representations to determine the known situation most closely represented by the current situation. In the preferred embodiment, the stored representations include the presence or absence of a person or infant seat in the front passenger seat of an automobile.

A bio-inspired system for spatio-temporal recognition in static and video imagery

NASA Astrophysics Data System (ADS)

Khosla, Deepak; Moore, Christopher K.; Chelian, Suhas

2007-04-01

This paper presents a bio-inspired method for spatio-temporal recognition in static and video imagery. It builds upon and extends our previous work on a bio-inspired Visual Attention and object Recognition System (VARS). The VARS approach locates and recognizes objects in a single frame. This work presents two extensions of VARS. The first extension is a Scene Recognition Engine (SCE) that learns to recognize spatial relationships between objects that compose a particular scene category in static imagery. This could be used for recognizing the category of a scene, e.g., office vs. kitchen scene. The second extension is the Event Recognition Engine (ERE) that recognizes spatio-temporal sequences or events in sequences. This extension uses a working memory model to recognize events and behaviors in video imagery by maintaining and recognizing ordered spatio-temporal sequences. The working memory model is based on an ARTSTORE1 neural network that combines an ART-based neural network with a cascade of sustained temporal order recurrent (STORE)1 neural networks. A series of Default ARTMAP classifiers ascribes event labels to these sequences. Our preliminary studies have shown that this extension is robust to variations in an object's motion profile. We evaluated the performance of the SCE and ERE on real datasets. The SCE module was tested on a visual scene classification task using the LabelMe2 dataset. The ERE was tested on real world video footage of vehicles and pedestrians in a street scene. Our system is able to recognize the events in this footage involving vehicles and pedestrians.

Complete Scene Recovery and Terrain Classification in Textured Terrain Meshes

PubMed Central

Song, Wei; Cho, Kyungeun; Um, Kyhyun; Won, Chee Sun; Sim, Sungdae

2012-01-01

Terrain classification allows a mobile robot to create an annotated map of its local environment from the three-dimensional (3D) and two-dimensional (2D) datasets collected by its array of sensors, including a GPS receiver, gyroscope, video camera, and range sensor. However, parts of objects that are outside the measurement range of the range sensor will not be detected. To overcome this problem, this paper describes an edge estimation method for complete scene recovery and complete terrain reconstruction. Here, the Gibbs-Markov random field is used to segment the ground from 2D videos and 3D point clouds. Further, a masking method is proposed to classify buildings and trees in a terrain mesh. PMID:23112653

Nailfold Capillaroscopy in Rheumatic Diseases: Which Parameters Should Be Evaluated?

PubMed Central

Etehad Tavakol, Mahnaz; Erlandsson, Björn-Erik

2015-01-01

Video nailfold capillaroscopy (NFC), considered as an extension of the widefield technique, allows a more accurate measuring and storing of capillary data and a better defining, analyzing, and quantifying of capillary abnormalities. Capillaroscopic study is often performed on the patients suspected of having microcirculation problems such as Raynaud's phenomenon as the main indication for nailfold capillaroscopy. Capillaroscopic findings based on microcirculation studies can provide useful information in the fields of pathophysiology, differential diagnosis, and monitoring therapy. Nailfold capillaroscopy provides a vital assessment in clinical practices and research; for example, its reputation in the early diagnosis of systemic sclerosis is well established and it is also used as a classification criterion in this regard. This review focuses on the manner of performing video nailfold capillaroscopy and on a common approach for measuring capillary dimensions in fingers and toes. PMID:26421308

Nailfold Capillaroscopy in Rheumatic Diseases: Which Parameters Should Be Evaluated?

PubMed

Etehad Tavakol, Mahnaz; Fatemi, Alimohammad; Karbalaie, Abdolamir; Emrani, Zahra; Erlandsson, Björn-Erik

2015-01-01

Video nailfold capillaroscopy (NFC), considered as an extension of the widefield technique, allows a more accurate measuring and storing of capillary data and a better defining, analyzing, and quantifying of capillary abnormalities. Capillaroscopic study is often performed on the patients suspected of having microcirculation problems such as Raynaud's phenomenon as the main indication for nailfold capillaroscopy. Capillaroscopic findings based on microcirculation studies can provide useful information in the fields of pathophysiology, differential diagnosis, and monitoring therapy. Nailfold capillaroscopy provides a vital assessment in clinical practices and research; for example, its reputation in the early diagnosis of systemic sclerosis is well established and it is also used as a classification criterion in this regard. This review focuses on the manner of performing video nailfold capillaroscopy and on a common approach for measuring capillary dimensions in fingers and toes.

Automatic optical detection and classification of marine animals around MHK converters using machine vision

DOE Office of Scientific and Technical Information (OSTI.GOV)

Brunton, Steven

Optical systems provide valuable information for evaluating interactions and associations between organisms and MHK energy converters and for capturing potentially rare encounters between marine organisms and MHK device. The deluge of optical data from cabled monitoring packages makes expert review time-consuming and expensive. We propose algorithms and a processing framework to automatically extract events of interest from underwater video. The open-source software framework consists of background subtraction, filtering, feature extraction and hierarchical classification algorithms. This principle classification pipeline was validated on real-world data collected with an experimental underwater monitoring package. An event detection rate of 100% was achieved using robustmore » principal components analysis (RPCA), Fourier feature extraction and a support vector machine (SVM) binary classifier. The detected events were then further classified into more complex classes – algae | invertebrate | vertebrate, one species | multiple species of fish, and interest rank. Greater than 80% accuracy was achieved using a combination of machine learning techniques.« less

Validation of Accelerometer Cut-Points in Children With Cerebral Palsy Aged 4 to 5 Years.

PubMed

Keawutan, Piyapa; Bell, Kristie L; Oftedal, Stina; Davies, Peter S W; Boyd, Roslyn N

2016-01-01

To derive and validate triaxial accelerometer cut-points in children with cerebral palsy (CP) and compare these with previously established cut-points in children with typical development. Eighty-four children with CP aged 4 to 5 years wore the ActiGraph during a play-based gross motor function measure assessment that was video-taped for direct observation. Receiver operating characteristic and Bland-Altman plots were used for analyses. The ActiGraph had good classification accuracy in Gross Motor Function Classification System (GMFCS) levels III and V and fair classification accuracy in GMFCS levels I, II, and IV. These results support the use of the previously established cut-points for sedentary time of 820 counts per minute in children with CP aged 4 to 5 years across all functional abilities. The cut-point provides an objective measure of sedentary and active time in children with CP. The cut-point is applicable to group data but not for individual children.

Image-classification-based global dimming algorithm for LED backlights in LCDs

NASA Astrophysics Data System (ADS)

Qibin, Feng; Huijie, He; Dong, Han; Lei, Zhang; Guoqiang, Lv

2015-07-01

Backlight dimming can help LCDs reduce power consumption and improve CR. With fixed parameters, dimming algorithm cannot achieve satisfied effects for all kinds of images. The paper introduces an image-classification-based global dimming algorithm. The proposed classification method especially for backlight dimming is based on luminance and CR of input images. The parameters for backlight dimming level and pixel compensation are adaptive with image classifications. The simulation results show that the classification based dimming algorithm presents 86.13% power reduction improvement compared with dimming without classification, with almost same display quality. The prototype is developed. There are no perceived distortions when playing videos. The practical average power reduction of the prototype TV is 18.72%, compared with common TV without dimming.

A computer vision framework for finger-tapping evaluation in Parkinson's disease.

PubMed

Khan, Taha; Nyholm, Dag; Westin, Jerker; Dougherty, Mark

2014-01-01

The rapid finger-tapping test (RFT) is an important method for clinical evaluation of movement disorders, including Parkinson's disease (PD). In clinical practice, the naked-eye evaluation of RFT results in a coarse judgment of symptom scores. We introduce a novel computer-vision (CV) method for quantification of tapping symptoms through motion analysis of index-fingers. The method is unique as it utilizes facial features to calibrate tapping amplitude for normalization of distance variation between the camera and subject. The study involved 387 video footages of RFT recorded from 13 patients diagnosed with advanced PD. Tapping performance in these videos was rated by two clinicians between the symptom severity levels ('0: normal' to '3: severe') using the unified Parkinson's disease rating scale motor examination of finger-tapping (UPDRS-FT). Another set of recordings in this study consisted of 84 videos of RFT recorded from 6 healthy controls. These videos were processed by a CV algorithm that tracks the index-finger motion between the video-frames to produce a tapping time-series. Different features were computed from this time series to estimate speed, amplitude, rhythm and fatigue in tapping. The features were trained in a support vector machine (1) to categorize the patient group between UPDRS-FT symptom severity levels, and (2) to discriminate between PD patients and healthy controls. A new representative feature of tapping rhythm, 'cross-correlation between the normalized peaks' showed strong Guttman correlation (μ2=-0.80) with the clinical ratings. The classification of tapping features using the support vector machine classifier and 10-fold cross validation categorized the patient samples between UPDRS-FT levels with an accuracy of 88%. The same classification scheme discriminated between RFT samples of healthy controls and PD patients with an accuracy of 95%. The work supports the feasibility of the approach, which is presumed suitable for PD monitoring in the home environment. The system offers advantages over other technologies (e.g. magnetic sensors, accelerometers, etc.) previously developed for objective assessment of tapping symptoms. Copyright © 2013 Elsevier B.V. All rights reserved.

Real-time automatic inspection under adverse conditions

NASA Astrophysics Data System (ADS)

Carvalho, Fernando D.; Correia, Fernando C.; Freitas, Jose C. A.; Rodrigues, Fernando C.

1991-03-01

This paper presents the results of a R&D Program supported by a grant from the Ministry of Defense, devoted to the development of an inteffigent camera for surveillance in the open air. The effects of shadows, clouds and winds were problems to be solved without generating false alarm events. The system is based on a video CCD camera which generates a video CCIR signal. The signal is then processed in modular hardware which detects the changes in the scene and processes the image, in order to enhance the intruder image and path. Windows may be defined over the image in order to increase the information obtained about the intruder and a first approach to the classification of the type of intruder may be achieved. The paper describes the hardware used in the system, as well as the software, used for the installation of the camera and the software developed for the microprocessor which is responsible for the generation of the alarm signals. The paper also presents some results of surveillance tasks in the open air executed by the system with real time performance.

Opinion Mining for Educational Video Lectures.

PubMed

Kravvaris, Dimitrios; Kermanidis, Katia Lida

2017-01-01

The search for relevant educational videos is a time consuming process for the users. Furthermore, the increasing demand for educational videos intensifies the problem and calls for the users to utilize whichever information is offered by the hosting web pages, and choose the most appropriate one. This research focuses on the classification of user views, based on the comments on educational videos, into positive or negative ones. The aim is to give users a picture of the positive and negative comments that have been recorded, so as to provide a qualitative view of the final selection at their disposal. The present paper's innovation is the automatic identification of the most important words of the verbal content of the video lectures and the filtering of the comments based on them, thus limiting the comments to the ones that have a substantial semantic connection with the video content.

A scheme for racquet sports video analysis with the combination of audio-visual information

NASA Astrophysics Data System (ADS)

Xing, Liyuan; Ye, Qixiang; Zhang, Weigang; Huang, Qingming; Yu, Hua

2005-07-01

As a very important category in sports video, racquet sports video, e.g. table tennis, tennis and badminton, has been paid little attention in the past years. Considering the characteristics of this kind of sports video, we propose a new scheme for structure indexing and highlight generating based on the combination of audio and visual information. Firstly, a supervised classification method is employed to detect important audio symbols including impact (ball hit), audience cheers, commentator speech, etc. Meanwhile an unsupervised algorithm is proposed to group video shots into various clusters. Then, by taking advantage of temporal relationship between audio and visual signals, we can specify the scene clusters with semantic labels including rally scenes and break scenes. Thirdly, a refinement procedure is developed to reduce false rally scenes by further audio analysis. Finally, an exciting model is proposed to rank the detected rally scenes from which many exciting video clips such as game (match) points can be correctly retrieved. Experiments on two types of representative racquet sports video, table tennis video and tennis video, demonstrate encouraging results.

Validity of an activity monitor in young people with cerebral palsy gross motor function classification system level I.

PubMed

O' Donoghue, Deirdre; Kennedy, Norelee

2014-11-01

The activPAL™ activity monitor has potential for use in youth with Cerebral Palsy (CP) as it has demonstrated acceptable validity for the assessment of sedentary and physical activity in other populations. This study determined the validity of the activPAL™ activity monitor for the measurement of sitting, standing, walking time, transitions and step count for both legs in young people with hemiplegic and asymmetric diplegic CP. Seventeen participants with CP Gross Motor Function Classification System level I completed two video recorded test protocols that involved wearing an activPAL™ activity monitor on alternate legs. Agreement between observed video recorded data and activPAL™ activity monitor data was assessed using the Bland and Altman (BA) method and intraclass correlation coefficients (ICC 3,1). There was perfect agreement for transitions and high agreement for sitting (BA mean differences (MD): -1.8 and -1.8 s; ICCs: 0.49 and 0.95) standing (MD: 0.8 and 0.1 s; ICCs: 0.59 and 0.98) walking (MD: 1 and 1.1 s; ICCs: 0.99 and 0.94) timings and low agreement for step count (MD: 4.1 and 2.8 steps; ICCs: 0.96 and 0.95) for both legs. This study found clinically acceptable agreement with direct observation for all activPAL™ activity monitor functions, except for step count measurement with respect to the range of measurement values obtained for both legs in this study population.

Evaluation of Video Image Analysis (VIA) technology to predict meat yield of sheep carcasses on-line under UK abattoir conditions.

PubMed

Rius-Vilarrasa, E; Bünger, L; Maltin, C; Matthews, K R; Roehe, R

2009-05-01

The Meat and Livestock Commission's (MLC) EUROP classification based scheme and Video Image Analysis (VIA) system were compared in their ability to predict weights of primal carcass joints. A total of 443 commercial lamb carcasses under 12 months of age and mixed gender were selected by their cold carcass weight (CCW), conformation and fat scores. Lamb carcasses were classified for conformation and fatness, scanned by the VIA system and dissected into primal joints of leg, chump, loin, breast and shoulder. After adjustment for CCW, the estimation of primal joints using MLC EUROP scores showed high coefficients of determination (R(2)) in the range of 0.82-0.99. The use of VIA always resulted in equal or higher R(2). The precision measured as root mean square error (RMSE) was 27% (leg), 13% (chump), 1% (loin), 11% (breast), 5% (shoulders) and 13% (total primals) higher using VIA than MLC carcass information. Adjustment for slaughter day and gender effects indicated that estimations of primal joints using MLC EUROP scores were more sensitive to these factors than using VIA. This was consistent with an increase in stability of the prediction model of 28%, 11%, 2%, 12%, 6% and 14% for leg, chump, loin, breast and shoulder and total primals, respectively, using VIA compared to MLC EUROP scores. Consequently, VIA was capable of improving the prediction of primal meat yields compared to the current MLC EUROP carcass classification scheme used in the UK abattoirs.

The Impact of Computed Tomography on Decision Making in Tibial Plateau Fractures.

PubMed

Castiglia, Marcello Teixeira; Nogueira-Barbosa, Marcello Henrique; Messias, Andre Marcio Vieira; Salim, Rodrigo; Fogagnolo, Fabricio; Schatzker, Joseph; Kfuri, Mauricio

2018-02-14

Schatzker introduced one of the most used classification systems for tibial plateau fractures, based on plain radiographs. Computed tomography brought to attention the importance of coronal plane-oriented fractures. The goal of our study was to determine if the addition of computed tomography would affect the decision making of surgeons who usually use the Schatzker classification to assess tibial plateau fractures. Image studies of 70 patients who sustained tibial plateau fractures were uploaded to a dedicated homepage. Every patient was linked to a folder which contained two radiographic projections (anteroposterior and lateral), three interactive videos of computed tomography (axial, sagittal, and coronal), and eight pictures depicting tridimensional reconstructions of the tibial plateau. Ten attending orthopaedic surgeons, who were blinded to the cases, were granted access to the homepage and assessed each set of images in two different rounds, separated to each other by an interval of 2 weeks. Each case was evaluated in three steps, where surgeons had access, respectively to radiographs, two-dimensional videos of computed tomography, and three-dimensional reconstruction images. After every step, surgeons were asked to present how would they classify the case using the Schatzker system and which surgical approaches would be appropriate. We evaluated the inter- and intraobserver reliability of the Schatzker classification using the Kappa concordance coefficient, as well as the impact of computed tomography in the decision making regarding the surgical approach for each case, by using the chi-square test and likelihood ratio. The interobserver concordance kappa coefficients after each assessment step were, respectively, 0.58, 0.62, and 0.64. For the intraobserver analysis, the coefficients were, respectively, 0.76, 0.75, and 0.78. Computed tomography changed the surgical approach selection for the types II, V, and VI of Schatzker ( p  < 0.01). The addition of computed tomography scans to plain radiographs improved the interobserver reliability of Schatzker classification. Computed tomography had a statistically significant impact in the selection of surgical approaches for the lateral tibial plateau. Thieme Medical Publishers 333 Seventh Avenue, New York, NY 10001, USA.

Convolutional Architecture Exploration for Action Recognition and Image Classification

DTIC Science & Technology

2015-01-01

that has 200 videos taken in 720x480 resolution of 9 different sporting activities: diving, golf , swinging , kicking, lifting, horseback riding, running...sporting activities: diving, golf swinging , kicking, lifting, horseback riding, running, skateboarding, swinging (various gymnastics), and walking. In this...Testing Videos Diving 13 3 Golf Swinging 21 4 Horseback Riding 11 3 Kicking 21 4 Lifting 12 3 Running 12 3 Skateboarding 12 3 Swinging (Gymnastics) 28

Multi-agent Negotiation Mechanisms for Statistical Target Classification in Wireless Multimedia Sensor Networks

PubMed Central

Wang, Xue; Bi, Dao-wei; Ding, Liang; Wang, Sheng

2007-01-01

The recent availability of low cost and miniaturized hardware has allowed wireless sensor networks (WSNs) to retrieve audio and video data in real world applications, which has fostered the development of wireless multimedia sensor networks (WMSNs). Resource constraints and challenging multimedia data volume make development of efficient algorithms to perform in-network processing of multimedia contents imperative. This paper proposes solving problems in the domain of WMSNs from the perspective of multi-agent systems. The multi-agent framework enables flexible network configuration and efficient collaborative in-network processing. The focus is placed on target classification in WMSNs where audio information is retrieved by microphones. To deal with the uncertainties related to audio information retrieval, the statistical approaches of power spectral density estimates, principal component analysis and Gaussian process classification are employed. A multi-agent negotiation mechanism is specially developed to efficiently utilize limited resources and simultaneously enhance classification accuracy and reliability. The negotiation is composed of two phases, where an auction based approach is first exploited to allocate the classification task among the agents and then individual agent decisions are combined by the committee decision mechanism. Simulation experiments with real world data are conducted and the results show that the proposed statistical approaches and negotiation mechanism not only reduce memory and computation requirements in WMSNs but also significantly enhance classification accuracy and reliability. PMID:28903223

Video compression of coronary angiograms based on discrete wavelet transform with block classification.

PubMed

Ho, B T; Tsai, M J; Wei, J; Ma, M; Saipetch, P

1996-01-01

A new method of video compression for angiographic images has been developed to achieve high compression ratio (~20:1) while eliminating block artifacts which leads to loss of diagnostic accuracy. This method adopts motion picture experts group's (MPEGs) motion compensated prediction to takes advantage of frame to frame correlation. However, in contrast to MPEG, the error images arising from mismatches in the motion estimation are encoded by discrete wavelet transform (DWT) rather than block discrete cosine transform (DCT). Furthermore, the authors developed a classification scheme which label each block in an image as intra, error, or background type and encode it accordingly. This hybrid coding can significantly improve the compression efficiency in certain eases. This method can be generalized for any dynamic image sequences applications sensitive to block artifacts.

Ambulant adults with spastic cerebral palsy: the validity of lower limb joint angle measurements from sagittal video recordings.

PubMed

Larsen, Kerstin L; Maanum, Grethe; Frøslie, Kathrine F; Jahnsen, Reidun

2012-02-01

In the development of a clinical program for ambulant adults with cerebral palsy (CP), we investigated the validity of joint angles measured from sagittal video recordings and explored if movements in the transversal plane identified with three-dimensional gait analysis (3DGA) affected the validity of sagittal video joint angle measurements. Ten observers, and 10 persons with spastic CP (19-63 years), Gross Motor Function Classification System I-II, participated in the study. Concurrent criterion validity between video joint angle measurements and 3DGA was assessed by Bland-Altman plots with mean differences and 95% limits of agreement (LoA). Pearson's correlation coefficients (r) and scatter plots were used supplementary. Transversal kinematics ≥2 SD from our reference band were defined as increased movement in the transversal plane. The overall mean differences in degrees between joint angles measured by 3DGA and video recordings (3°, 5° and -7° for the hip, knee and ankle respectively) and corresponding LoA (18°, 10° and 15° for the hip, knee and ankle, respectively) demonstrated substantial discrepancies between the two methods. The correlations ranged from low (r=0.39) to moderate (r=0.68). Discrepancy between the two measurements was seen both among persons with and without the presence of deviating transversal kinematics. Quantifying lower limb joint angles from sagittal video recordings in ambulant adults with spastic CP demonstrated low validity, and should be conducted with caution. This gives implications for selecting evaluation method of gait. Copyright © 2011 Elsevier B.V. All rights reserved.

Semantic Information Extraction of Lanes Based on Onboard Camera Videos

NASA Astrophysics Data System (ADS)

Tang, L.; Deng, T.; Ren, C.

2018-04-01

In the field of autonomous driving, semantic information of lanes is very important. This paper proposes a method of automatic detection of lanes and extraction of semantic information from onboard camera videos. The proposed method firstly detects the edges of lanes by the grayscale gradient direction, and improves the Probabilistic Hough transform to fit them; then, it uses the vanishing point principle to calculate the lane geometrical position, and uses lane characteristics to extract lane semantic information by the classification of decision trees. In the experiment, 216 road video images captured by a camera mounted onboard a moving vehicle were used to detect lanes and extract lane semantic information. The results show that the proposed method can accurately identify lane semantics from video images.

A Study of Feature Combination for Vehicle Detection Based on Image Processing

PubMed Central

2014-01-01

Video analytics play a critical role in most recent traffic monitoring and driver assistance systems. In this context, the correct detection and classification of surrounding vehicles through image analysis has been the focus of extensive research in the last years. Most of the pieces of work reported for image-based vehicle verification make use of supervised classification approaches and resort to techniques, such as histograms of oriented gradients (HOG), principal component analysis (PCA), and Gabor filters, among others. Unfortunately, existing approaches are lacking in two respects: first, comparison between methods using a common body of work has not been addressed; second, no study of the combination potentiality of popular features for vehicle classification has been reported. In this study the performance of the different techniques is first reviewed and compared using a common public database. Then, the combination capabilities of these techniques are explored and a methodology is presented for the fusion of classifiers built upon them, taking into account also the vehicle pose. The study unveils the limitations of single-feature based classification and makes clear that fusion of classifiers is highly beneficial for vehicle verification. PMID:24672299

Developing and Integrating Advanced Movement Features Improves Automated Classification of Ciliate Species

PubMed Central

Soleymani, Ali; Pennekamp, Frank; Petchey, Owen L.; Weibel, Robert

2015-01-01

Recent advances in tracking technologies such as GPS or video tracking systems describe the movement paths of individuals in unprecedented details and are increasingly used in different fields, including ecology. However, extracting information from raw movement data requires advanced analysis techniques, for instance to infer behaviors expressed during a certain period of the recorded trajectory, or gender or species identity in case data is obtained from remote tracking. In this paper, we address how different movement features affect the ability to automatically classify the species identity, using a dataset of unicellular microbes (i.e., ciliates). Previously, morphological attributes and simple movement metrics, such as speed, were used for classifying ciliate species. Here, we demonstrate that adding advanced movement features, in particular such based on discrete wavelet transform, to morphological features can improve classification. These results may have practical applications in automated monitoring of waste water facilities as well as environmental monitoring of aquatic systems. PMID:26680591

Action recognition in depth video from RGB perspective: A knowledge transfer manner

NASA Astrophysics Data System (ADS)

Chen, Jun; Xiao, Yang; Cao, Zhiguo; Fang, Zhiwen

2018-03-01

Different video modal for human action recognition has becoming a highly promising trend in the video analysis. In this paper, we propose a method for human action recognition from RGB video to Depth video using domain adaptation, where we use learned feature from RGB videos to do action recognition for depth videos. More specifically, we make three steps for solving this problem in this paper. First, different from image, video is more complex as it has both spatial and temporal information, in order to better encode this information, dynamic image method is used to represent each RGB or Depth video to one image, based on this, most methods for extracting feature in image can be used in video. Secondly, as video can be represented as image, so standard CNN model can be used for training and testing for videos, beside, CNN model can be also used for feature extracting as its powerful feature expressing ability. Thirdly, as RGB videos and Depth videos are belong to two different domains, in order to make two different feature domains has more similarity, domain adaptation is firstly used for solving this problem between RGB and Depth video, based on this, the learned feature from RGB video model can be directly used for Depth video classification. We evaluate the proposed method on one complex RGB-D action dataset (NTU RGB-D), and our method can have more than 2% accuracy improvement using domain adaptation from RGB to Depth action recognition.

Video-based depression detection using local Curvelet binary patterns in pairwise orthogonal planes.

PubMed

Pampouchidou, Anastasia; Marias, Kostas; Tsiknakis, Manolis; Simos, Panagiotis; Fan Yang; Lemaitre, Guillaume; Meriaudeau, Fabrice

2016-08-01

Depression is an increasingly prevalent mood disorder. This is the reason why the field of computer-based depression assessment has been gaining the attention of the research community during the past couple of years. The present work proposes two algorithms for depression detection, one Frame-based and the second Video-based, both employing Curvelet transform and Local Binary Patterns. The main advantage of these methods is that they have significantly lower computational requirements, as the extracted features are of very low dimensionality. This is achieved by modifying the previously proposed algorithm which considers Three-Orthogonal-Planes, to only Pairwise-Orthogonal-Planes. Performance of the algorithms was tested on the benchmark dataset provided by the Audio/Visual Emotion Challenge 2014, with the person-specific system achieving 97.6% classification accuracy, and the person-independed one yielding promising preliminary results of 74.5% accuracy. The paper concludes with open issues, proposed solutions, and future plans.

Vehicle Detection with Occlusion Handling, Tracking, and OC-SVM Classification: A High Performance Vision-Based System

PubMed Central

Velazquez-Pupo, Roxana; Sierra-Romero, Alberto; Torres-Roman, Deni; Shkvarko, Yuriy V.; Romero-Delgado, Misael

2018-01-01

This paper presents a high performance vision-based system with a single static camera for traffic surveillance, for moving vehicle detection with occlusion handling, tracking, counting, and One Class Support Vector Machine (OC-SVM) classification. In this approach, moving objects are first segmented from the background using the adaptive Gaussian Mixture Model (GMM). After that, several geometric features are extracted, such as vehicle area, height, width, centroid, and bounding box. As occlusion is present, an algorithm was implemented to reduce it. The tracking is performed with adaptive Kalman filter. Finally, the selected geometric features: estimated area, height, and width are used by different classifiers in order to sort vehicles into three classes: small, midsize, and large. Extensive experimental results in eight real traffic videos with more than 4000 ground truth vehicles have shown that the improved system can run in real time under an occlusion index of 0.312 and classify vehicles with a global detection rate or recall, precision, and F-measure of up to 98.190%, and an F-measure of up to 99.051% for midsize vehicles. PMID:29382078

Dollar Summary of Federal Supply Classification and Service Category by Company, FY84, Part 5 (6630-7530).

DTIC Science & Technology

1984-01-01

INSTR CONN ARMY OPTICAL INSTRUMENTS 83 CONTRAVES GOERZ CORPORATION PENN ARMY OPTICAL INSTRUMENTS 1.830 CORDIN COMPANY UTAH ARMY OPTICAL INSTRUMENTS 99 D A...CO ALABAMA ARMY OPTICAL INSTRUMENTS 27𔄃 INTERACTIVE VIDEO DISC CALIFORNIA NAVY OPTICAL INSTRUMENTS 30 INTERNATIONAL SCTFC INST CALIFORNIA ARMY...PHOTOGRAPHIC SETS KITS AND OUTFITS 41 CALIFORNIA VIDEO SALES INC CALIFORNIA ARMY PHOTOGRAPHIC SETS KITS AND OUTFITS 31 CONTRAVES GOERZ CORPORATION

Sensor Management for Tactical Surveillance Operations

DTIC Science & Technology

2007-11-01

active and passive sonar for submarine and tor- pedo detection, and mine avoidance. [range, bearing] range 1.8 km to 55 km Active or Passive AN/SLQ-501...finding (DF) unit [bearing, classification] maximum range 1100 km Passive Cameras (day- light/ night- vision) ( video & still) Record optical and...infrared still images or motion video of events for near-real time assessment or long term analysis and archiving. Range is limited by the image resolution

Test and Evaluation of Video Teleconferencing at 56 kbps.

DTIC Science & Technology

1985-03-01

ll-ll - - llI .. -.. . .- - UNCLASSIFIED SECURITY CLASSIFICATION OF THIS PAGE (Whan Date BAnterd _________________ jREPORT DOCUMENTATION PAGE BFR...NUMBER OFPAGES Washington, D.C. 20305-2010 123 1.MONITORING AGENCY NAME h ADDRESS(tI different from Controlling Office) IS. SECURITY CLASS. (of this... SECURITY CL ASSIICA1 ION OF TH13 PAGEWM~ DOOM EfntenE) -7- 7. NCS TECHNICAL INFORMATION BULLETIN 85-3 TEST AND EVALUATION OF VIDEO TELECONFERENCING AT 56

Fifth Disease

MedlinePlus

... because it was fifth in a list of historical classifications of common skin rash illnesses in children. ... Audio/Video file Apple Quicktime file RealPlayer file Text file Zip Archive file SAS file ePub file ...

A distributed approach for optimizing cascaded classifier topologies in real-time stream mining systems.

PubMed

Foo, Brian; van der Schaar, Mihaela

2010-11-01

In this paper, we discuss distributed optimization techniques for configuring classifiers in a real-time, informationally-distributed stream mining system. Due to the large volume of streaming data, stream mining systems must often cope with overload, which can lead to poor performance and intolerable processing delay for real-time applications. Furthermore, optimizing over an entire system of classifiers is a difficult task since changing the filtering process at one classifier can impact both the feature values of data arriving at classifiers further downstream and thus, the classification performance achieved by an ensemble of classifiers, as well as the end-to-end processing delay. To address this problem, this paper makes three main contributions: 1) Based on classification and queuing theoretic models, we propose a utility metric that captures both the performance and the delay of a binary filtering classifier system. 2) We introduce a low-complexity framework for estimating the system utility by observing, estimating, and/or exchanging parameters between the inter-related classifiers deployed across the system. 3) We provide distributed algorithms to reconfigure the system, and analyze the algorithms based on their convergence properties, optimality, information exchange overhead, and rate of adaptation to non-stationary data sources. We provide results using different video classifier systems.

Video mining using combinations of unsupervised and supervised learning techniques

NASA Astrophysics Data System (ADS)

Divakaran, Ajay; Miyahara, Koji; Peker, Kadir A.; Radhakrishnan, Regunathan; Xiong, Ziyou

2003-12-01

We discuss the meaning and significance of the video mining problem, and present our work on some aspects of video mining. A simple definition of video mining is unsupervised discovery of patterns in audio-visual content. Such purely unsupervised discovery is readily applicable to video surveillance as well as to consumer video browsing applications. We interpret video mining as content-adaptive or "blind" content processing, in which the first stage is content characterization and the second stage is event discovery based on the characterization obtained in stage 1. We discuss the target applications and find that using a purely unsupervised approach are too computationally complex to be implemented on our product platform. We then describe various combinations of unsupervised and supervised learning techniques that help discover patterns that are useful to the end-user of the application. We target consumer video browsing applications such as commercial message detection, sports highlights extraction etc. We employ both audio and video features. We find that supervised audio classification combined with unsupervised unusual event discovery enables accurate supervised detection of desired events. Our techniques are computationally simple and robust to common variations in production styles etc.

Classification of Birds and Bats Using Flight Tracks

DOE Office of Scientific and Technical Information (OSTI.GOV)

Cullinan, Valerie I.; Matzner, Shari; Duberstein, Corey A.

Classification of birds and bats that use areas targeted for offshore wind farm development and the inference of their behavior is essential to evaluating the potential effects of development. The current approach to assessing the number and distribution of birds at sea involves transect surveys using trained individuals in boats or airplanes or using high-resolution imagery. These approaches are costly and have safety concerns. Based on a limited annotated library extracted from a single-camera thermal video, we provide a framework for building models that classify birds and bats and their associated behaviors. As an example, we developed a discriminant modelmore » for theoretical flight paths and applied it to data (N = 64 tracks) extracted from 5-min video clips. The agreement between model- and observer-classified path types was initially only 41%, but it increased to 73% when small-scale jitter was censored and path types were combined. Classification of 46 tracks of bats, swallows, gulls, and terns on average was 82% accurate, based on a jackknife cross-validation. Model classification of bats and terns (N = 4 and 2, respectively) was 94% and 91% correct, respectively; however, the variance associated with the tracks from these targets is poorly estimated. Model classification of gulls and swallows (N ≥ 18) was on average 73% and 85% correct, respectively. The models developed here should be considered preliminary because they are based on a small data set both in terms of the numbers of species and the identified flight tracks. Future classification models would be greatly improved by including a measure of distance between the camera and the target.« less

The Simple Video Coder: A free tool for efficiently coding social video data.

PubMed

Barto, Daniel; Bird, Clark W; Hamilton, Derek A; Fink, Brandi C

2017-08-01

Videotaping of experimental sessions is a common practice across many disciplines of psychology, ranging from clinical therapy, to developmental science, to animal research. Audio-visual data are a rich source of information that can be easily recorded; however, analysis of the recordings presents a major obstacle to project completion. Coding behavior is time-consuming and often requires ad-hoc training of a student coder. In addition, existing software is either prohibitively expensive or cumbersome, which leaves researchers with inadequate tools to quickly process video data. We offer the Simple Video Coder-free, open-source software for behavior coding that is flexible in accommodating different experimental designs, is intuitive for students to use, and produces outcome measures of event timing, frequency, and duration. Finally, the software also offers extraction tools to splice video into coded segments suitable for training future human coders or for use as input for pattern classification algorithms.

Logo recognition in video by line profile classification

NASA Astrophysics Data System (ADS)

den Hollander, Richard J. M.; Hanjalic, Alan

2003-12-01

We present an extension to earlier work on recognizing logos in video stills. The logo instances considered here are rigid planar objects observed at a distance in the scene, so the possible perspective transformation can be approximated by an affine transformation. For this reason we can classify the logos by matching (invariant) line profiles. We enhance our previous method by considering multiple line profiles instead of a single profile of the logo. The positions of the lines are based on maxima in the Hough transform space of the segmented logo foreground image. Experiments are performed on MPEG1 sport video sequences to show the performance of the proposed method.

Optimal frame-by-frame result combination strategy for OCR in video stream

NASA Astrophysics Data System (ADS)

Bulatov, Konstantin; Lynchenko, Aleksander; Krivtsov, Valeriy

2018-04-01

This paper describes the problem of combining classification results of multiple observations of one object. This task can be regarded as a particular case of a decision-making using a combination of experts votes with calculated weights. The accuracy of various methods of combining the classification results depending on different models of input data is investigated on the example of frame-by-frame character recognition in a video stream. Experimentally it is shown that the strategy of choosing a single most competent expert in case of input data without irrelevant observations has an advantage (in this case irrelevant means with character localization and segmentation errors). At the same time this work demonstrates the advantage of combining several most competent experts according to multiplication rule or voting if irrelevant samples are present in the input data.

Remote sensing application to regional activities

NASA Technical Reports Server (NTRS)

Shahrokhi, F.; Jones, N. L.; Sharber, L. A.

1976-01-01

Two agencies within the State of Tennessee were identified whereby the transfer of aerospace technology, namely remote sensing, could be applied to their stated problem areas. Their stated problem areas are wetland and land classification and strip mining studies. In both studies, LANDSAT data was analyzed with the UTSI video-input analog/digital automatic analysis and classification facility. In the West Tennessee area three land-use classifications could be distinguished; cropland, wetland, and forest. In the East Tennessee study area, measurements were submitted to statistical tests which verified the significant differences due to natural terrain, stripped areas, various stages of reclamation, water, etc. Classifications for both studies were output in the form of maps of symbols and varying shades of gray.

Automated detection of feeding strikes by larval fish using continuous high-speed digital video: a novel method to extract quantitative data from fast, sparse kinematic events.

PubMed

Shamur, Eyal; Zilka, Miri; Hassner, Tal; China, Victor; Liberzon, Alex; Holzman, Roi

2016-06-01

Using videography to extract quantitative data on animal movement and kinematics constitutes a major tool in biomechanics and behavioral ecology. Advanced recording technologies now enable acquisition of long video sequences encompassing sparse and unpredictable events. Although such events may be ecologically important, analysis of sparse data can be extremely time-consuming and potentially biased; data quality is often strongly dependent on the training level of the observer and subject to contamination by observer-dependent biases. These constraints often limit our ability to study animal performance and fitness. Using long videos of foraging fish larvae, we provide a framework for the automated detection of prey acquisition strikes, a behavior that is infrequent yet critical for larval survival. We compared the performance of four video descriptors and their combinations against manually identified feeding events. For our data, the best single descriptor provided a classification accuracy of 77-95% and detection accuracy of 88-98%, depending on fish species and size. Using a combination of descriptors improved the accuracy of classification by ∼2%, but did not improve detection accuracy. Our results indicate that the effort required by an expert to manually label videos can be greatly reduced to examining only the potential feeding detections in order to filter false detections. Thus, using automated descriptors reduces the amount of manual work needed to identify events of interest from weeks to hours, enabling the assembly of an unbiased large dataset of ecologically relevant behaviors. © 2016. Published by The Company of Biologists Ltd.

Video rate color region segmentation for mobile robotic applications

NASA Astrophysics Data System (ADS)

de Cabrol, Aymeric; Bonnin, Patrick J.; Hugel, Vincent; Blazevic, Pierre; Chetto, Maryline

2005-08-01

Color Region may be an interesting image feature to extract for visual tasks in robotics, such as navigation and obstacle avoidance. But, whereas numerous methods are used for vision systems embedded on robots, only a few use this segmentation mainly because of the processing duration. In this paper, we propose a new real-time (ie. video rate) color region segmentation followed by a robust color classification and a merging of regions, dedicated to various applications such as RoboCup four-legged league or an industrial conveyor wheeled robot. Performances of this algorithm and confrontation with other methods, in terms of result quality and temporal performances are provided. For better quality results, the obtained speed up is between 2 and 4. For same quality results, the it is up to 10. We present also the outlines of the Dynamic Vision System of the CLEOPATRE Project - for which this segmentation has been developed - and the Clear Box Methodology which allowed us to create the new color region segmentation from the evaluation and the knowledge of other well known segmentations.

Computer-aided diagnosis (CAD) for colonoscopy

NASA Astrophysics Data System (ADS)

Gu, Jia; Poirson, Allen

2007-03-01

Colorectal cancer is the second leading cause of cancer deaths, and ranks third for new cancer cases and cancer mortality for both men and women. However, its death rate can be dramatically reduced by appropriate treatment when early detection is available. The purpose of colonoscopy is to identify and assess the severity of lesions, which may be flat or protruding. Due to the subjective nature of the examination, colonoscopic proficiency is highly variable and dependent upon the colonoscopist's knowledge and experience. An automated image processing system providing an objective, rapid, and inexpensive analysis of video from a standard colonoscope could provide a valuable tool for screening and diagnosis. In this paper, we present the design, functionality and preliminary results of its Computer-Aided-Diagnosis (CAD) system for colonoscopy - ColonoCAD TM. ColonoCAD is a complex multi-sensor, multi-data and multi-algorithm image processing system, incorporating data management and visualization, video quality assessment and enhancement, calibration, multiple view based reconstruction, feature extraction and classification. As this is a new field in medical image processing, our hope is that this paper will provide the framework to encourage and facilitate collaboration and discussion between industry, academia, and medical practitioners.

Object detection in cinematographic video sequences for automatic indexing

NASA Astrophysics Data System (ADS)

Stauder, Jurgen; Chupeau, Bertrand; Oisel, Lionel

2003-06-01

This paper presents an object detection framework applied to cinematographic post-processing of video sequences. Post-processing is done after production and before editing. At the beginning of each shot of a video, a slate (also called clapperboard) is shown. The slate contains notably an electronic audio timecode that is necessary for audio-visual synchronization. This paper presents an object detection framework to detect slates in video sequences for automatic indexing and post-processing. It is based on five steps. The first two steps aim to reduce drastically the video data to be analyzed. They ensure high recall rate but have low precision. The first step detects images at the beginning of a shot possibly showing up a slate while the second step searches in these images for candidates regions with color distribution similar to slates. The objective is to not miss any slate while eliminating long parts of video without slate appearance. The third and fourth steps are statistical classification and pattern matching to detected and precisely locate slates in candidate regions. These steps ensure high recall rate and high precision. The objective is to detect slates with very little false alarms to minimize interactive corrections. In a last step, electronic timecodes are read from slates to automize audio-visual synchronization. The presented slate detector has a recall rate of 89% and a precision of 97,5%. By temporal integration, much more than 89% of shots in dailies are detected. By timecode coherence analysis, the precision can be raised too. Issues for future work are to accelerate the system to be faster than real-time and to extend the framework for several slate types.

Inter-rater reliability and aspects of validity of the parent-infant relationship global assessment scale (PIR-GAS)

PubMed Central

2013-01-01

Background The Parent-Infant Relationship Global Assessment Scale (PIR-GAS) signifies a conceptually relevant development in the multi-axial, developmentally sensitive classification system DC:0-3R for preschool children. However, information about the reliability and validity of the PIR-GAS is rare. A review of the available empirical studies suggests that in research, PIR-GAS ratings can be based on a ten-minute videotaped interaction sequence. The qualification of raters may be very heterogeneous across studies. Methods To test whether the use of the PIR-GAS still allows for a reliable assessment of the parent-infant relationship, our study compared a PIR-GAS ratings based on a full-information procedure across multiple settings with ratings based on a ten-minute video by two doctoral candidates of medicine. For each mother-child dyad at a family day hospital (N = 48), we obtained two video ratings and one full-information rating at admission to therapy and at discharge. This pre-post design allowed for a replication of our findings across the two measurement points. We focused on the inter-rater reliability between the video coders, as well as between the video and full-information procedure, including mean differences and correlations between the raters. Additionally, we examined aspects of the validity of video and full-information ratings based on their correlation with measures of child and maternal psychopathology. Results Our results showed that a ten-minute video and full-information PIR-GAS ratings were not interchangeable. Most results at admission could be replicated by the data obtained at discharge. We concluded that a higher degree of standardization of the assessment procedure should increase the reliability of the PIR-GAS, and a more thorough theoretical foundation of the manual should increase its validity. PMID:23705962

Classification and simulation of stereoscopic artifacts in mobile 3DTV content

NASA Astrophysics Data System (ADS)

Boev, Atanas; Hollosi, Danilo; Gotchev, Atanas; Egiazarian, Karen

2009-02-01

We identify, categorize and simulate artifacts which might occur during delivery stereoscopic video to mobile devices. We consider the stages of 3D video delivery dataflow: content creation, conversion to the desired format (multiview or source-plus-depth), coding/decoding, transmission, and visualization on 3D display. Human 3D vision works by assessing various depth cues - accommodation, binocular depth cues, pictorial cues and motion parallax. As a consequence any artifact which modifies these cues impairs the quality of a 3D scene. The perceptibility of each artifact can be estimated through subjective tests. The material for such tests needs to contain various artifacts with different amounts of impairment. We present a system for simulation of these artifacts. The artifacts are organized in groups with similar origins, and each group is simulated by a block in a simulation channel. The channel introduces the following groups of artifacts: sensor limitations, geometric distortions caused by camera optics, spatial and temporal misalignments between video channels, spatial and temporal artifacts caused by coding, transmission losses, and visualization artifacts. For the case of source-plus-depth representation, artifacts caused by format conversion are added as well.

Sleep violence--forensic science implications: polygraphic and video documentation.

PubMed

Mahowald, M W; Bundlie, S R; Hurwitz, T D; Schenck, C H

1990-03-01

During the past century, infrequent, anecdotal reports of sleep-related violence with forensic science implications have appeared. Recent rapid developments in the field of sleep-disorders medicine have resulted in greater understanding of a variety of sleep-related behaviors, and formal sleep-behavior monitoring techniques have permitted their documentation and classification. Sleep-related violence can be associated with a number of diagnosable and treatable sleep disorders, including (1) night terrors/sleepwalking, (2) nocturnal seizures, (3) rapid eye movement (REM) sleep-behavior disorder, (4) sleep drunkenness, and (5) psychogenic dissociative states occurring during the sleep period. Potentially violent automatized behavior, without consciousness, can and does occur during sleep. The violence resulting from these disorders may be misinterpreted as purposeful suicide, assault, or even homicide. Sleep-related violence must be added to the list of automatisms. A classification system of both waking and sleep-related automatic behavior is proposed, with recommendations for assessment of such behavior.

Photometric stereo endoscopy.

PubMed

Parot, Vicente; Lim, Daryl; González, Germán; Traverso, Giovanni; Nishioka, Norman S; Vakoc, Benjamin J; Durr, Nicholas J

2013-07-01

While color video endoscopy has enabled wide-field examination of the gastrointestinal tract, it often misses or incorrectly classifies lesions. Many of these missed lesions exhibit characteristic three-dimensional surface topographies. An endoscopic system that adds topographical measurements to conventional color imagery could therefore increase lesion detection and improve classification accuracy. We introduce photometric stereo endoscopy (PSE), a technique which allows high spatial frequency components of surface topography to be acquired simultaneously with conventional two-dimensional color imagery. We implement this technique in an endoscopic form factor and demonstrate that it can acquire the topography of small features with complex geometries and heterogeneous optical properties. PSE imaging of ex vivo human gastrointestinal tissue shows that surface topography measurements enable differentiation of abnormal shapes from surrounding normal tissue. Together, these results confirm that the topographical measurements can be obtained with relatively simple hardware in an endoscopic form factor, and suggest the potential of PSE to improve lesion detection and classification in gastrointestinal imaging.

Census Videos

Science.gov Websites

United States Census Bureau Topics Population Latest Information Age and Sex Ancestry Children Mobility Population Estimates Population Projections Race Veterans Economy Latest Information Portal Other Economic Programs Business Latest Information Business Characteristics Classification Codes

An algorithm for power line detection and warning based on a millimeter-wave radar video.

PubMed

Ma, Qirong; Goshi, Darren S; Shih, Yi-Chi; Sun, Ming-Ting

2011-12-01

Power-line-strike accident is a major safety threat for low-flying aircrafts such as helicopters, thus an automatic warning system to power lines is highly desirable. In this paper we propose an algorithm for detecting power lines from radar videos from an active millimeter-wave sensor. Hough Transform is employed to detect candidate lines. The major challenge is that the radar videos are very noisy due to ground return. The noise points could fall on the same line which results in signal peaks after Hough Transform similar to the actual cable lines. To differentiate the cable lines from the noise lines, we train a Support Vector Machine to perform the classification. We exploit the Bragg pattern, which is due to the diffraction of electromagnetic wave on the periodic surface of power lines. We propose a set of features to represent the Bragg pattern for the classifier. We also propose a slice-processing algorithm which supports parallel processing, and improves the detection of cables in a cluttered background. Lastly, an adaptive algorithm is proposed to integrate the detection results from individual frames into a reliable video detection decision, in which temporal correlation of the cable pattern across frames is used to make the detection more robust. Extensive experiments with real-world data validated the effectiveness of our cable detection algorithm. © 2011 IEEE

Sensor network based vehicle classification and license plate identification system

DOE Office of Scientific and Technical Information (OSTI.GOV)

Frigo, Janette Rose; Brennan, Sean M; Rosten, Edward J

Typically, for energy efficiency and scalability purposes, sensor networks have been used in the context of environmental and traffic monitoring applications in which operations at the sensor level are not computationally intensive. But increasingly, sensor network applications require data and compute intensive sensors such video cameras and microphones. In this paper, we describe the design and implementation of two such systems: a vehicle classifier based on acoustic signals and a license plate identification system using a camera. The systems are implemented in an energy-efficient manner to the extent possible using commercially available hardware, the Mica motes and the Stargate platform.more » Our experience in designing these systems leads us to consider an alternate more flexible, modular, low-power mote architecture that uses a combination of FPGAs, specialized embedded processing units and sensor data acquisition systems.« less

Physiological reactivity to nonideographic virtual reality stimuli in veterans with and without PTSD.

PubMed

Webb, Andrea K; Vincent, Ashley L; Jin, Alvin B; Pollack, Mark H

2015-02-01

Post-traumatic stress disorder (PTSD) currently is diagnosed via clinical interview in which subjective self reports of traumatic events and associated experiences are discussed with a mental health professional. The reliability and validity of diagnoses can be improved with the use of objective physiological measures. In this study, physiological activity was recorded from 58 male veterans (PTSD Diagnosis n = 16; Trauma Exposed/No PTSD Diagnosis: n = 23; No Trauma/No PTSD Diagnosis: n = 19) with and without PTSD and combat trauma exposure in response to emotionally evocative non-idiographic virtual reality stimuli. Statistically significant differences among the Control, Trauma, and PTSD groups were present during the viewing of two virtual reality videos. Skin conductance and interbeat interval features were extracted for each of ten video events (five events of increasing severity per video). These features were submitted to three stepwise discriminant function analyses to assess classification accuracy for Control versus Trauma, Control versus PTSD, and Trauma versus PTSD pairings of participant groups. Leave-one-out cross-validation classification accuracy was between 71 and 94%. These results are promising and suggest the utility of objective physiological measures in assisting with PTSD diagnosis.

Change Detection Algorithms for Surveillance in Visual IoT: A Comparative Study

NASA Astrophysics Data System (ADS)

Akram, Beenish Ayesha; Zafar, Amna; Akbar, Ali Hammad; Wajid, Bilal; Chaudhry, Shafique Ahmad

2018-01-01

The VIoT (Visual Internet of Things) connects virtual information world with real world objects using sensors and pervasive computing. For video surveillance in VIoT, ChD (Change Detection) is a critical component. ChD algorithms identify regions of change in multiple images of the same scene recorded at different time intervals for video surveillance. This paper presents performance comparison of histogram thresholding and classification ChD algorithms using quantitative measures for video surveillance in VIoT based on salient features of datasets. The thresholding algorithms Otsu, Kapur, Rosin and classification methods k-means, EM (Expectation Maximization) were simulated in MATLAB using diverse datasets. For performance evaluation, the quantitative measures used include OSR (Overall Success Rate), YC (Yule's Coefficient) and JC (Jaccard's Coefficient), execution time and memory consumption. Experimental results showed that Kapur's algorithm performed better for both indoor and outdoor environments with illumination changes, shadowing and medium to fast moving objects. However, it reflected degraded performance for small object size with minor changes. Otsu algorithm showed better results for indoor environments with slow to medium changes and nomadic object mobility. k-means showed good results in indoor environment with small object size producing slow change, no shadowing and scarce illumination changes.

The ability of video image analysis to predict lean meat yield and EUROP score of lamb carcasses.

PubMed

Einarsson, E; Eythórsdóttir, E; Smith, C R; Jónmundsson, J V

2014-07-01

A total of 862 lamb carcasses that were evaluated by both the VIAscan® and the current EUROP classification system were deboned and the actual yield was measured. Models were derived for predicting lean meat yield of the legs (Leg%), loin (Loin%) and shoulder (Shldr%) using the best VIAscan® variables selected by stepwise regression analysis of a calibration data set (n=603). The equations were tested on validation data set (n=259). The results showed that the VIAscan® predicted lean meat yield in the leg, loin and shoulder with an R 2 of 0.60, 0.31 and 0.47, respectively, whereas the current EUROP system predicted lean yield with an R 2 of 0.57, 0.32 and 0.37, respectively, for the three carcass parts. The VIAscan® also predicted the EUROP score of the trial carcasses, using a model derived from an earlier trial. The EUROP classification from VIAscan® and the current system were compared for their ability to explain the variation in lean yield of the whole carcass (LMY%) and trimmed fat (FAT%). The predicted EUROP scores from the VIAscan® explained 36% of the variation in LMY% and 60% of the variation in FAT%, compared with the current EUROP system that explained 49% and 72%, respectively. The EUROP classification obtained by the VIAscan® was tested against a panel of three expert classifiers (n=696). The VIAscan® classification agreed with 82% of conformation and 73% of the fat classes assigned by a panel of expert classifiers. It was concluded that VIAscan® provides a technology that can directly predict LMY% of lamb carcasses with more accuracy than the current EUROP classification system. The VIAscan® is also capable of classifying lamb carcasses into EUROP classes with an accuracy that fulfils minimum demands for the Icelandic sheep industry. Although the VIAscan® prediction of the Loin% is low, it is comparable to the current EUROP system, and should not hinder the adoption of the technology to estimate the yield of Icelandic lambs as it delivered a more accurate prediction for the Leg%, Shldr% and overall LMY% with negligible prediction bias.

Evaluation of a Single-Beam Sonar System to Map Seagrass at Two Sites in Northern Puget Sound, Washington

USGS Publications Warehouse

Stevens, Andrew W.; Lacy, Jessica R.; Finlayson, David P.; Gelfenbaum, Guy

2008-01-01

Seagrass at two sites in northern Puget Sound, Possession Point and nearby Browns Bay, was mapped using both a single-beam sonar and underwater video camera. The acoustic and underwater video data were compared to evaluate the accuracy of acoustic estimates of seagrass cover. The accuracy of the acoustic method was calculated for three classifications of seagrass observed in underwater video: bare (no seagrass), patchy seagrass, and continuous seagrass. Acoustic and underwater video methods agreed in 92 percent and 74 percent of observations made in bare and continuous areas, respectively. However, in patchy seagrass, the agreement between acoustic and underwater video was poor (43 percent). The poor agreement between the two methods in areas with patchy seagrass is likely because the two instruments were not precisely colocated. The distribution of seagrass at the two sites differed both in overall percent vegetated and in the distribution of percent cover versus depth. On the basis of acoustic data, seagrass inhabited 0.29 km2 (19 percent of total area) at Possession Point and 0.043 km2 (5 percent of total area) at the Browns Bay study site. The depth distribution at the two sites was markedly different. Whereas the majority of seagrass at Possession Point occurred between -0.5 and -1.5 m MLLW, most seagrass at Browns Bay occurred at a greater depth, between -2.25 and -3.5 m MLLW. Further investigation of the anthropogenic and natural factors causing these differences in distribution is needed.

Mapping coral and sponge habitats on a shelf-depth environment using multibeam sonar and ROV video observations: Learmonth Bank, northern British Columbia, Canada

NASA Astrophysics Data System (ADS)

Neves, Bárbara M.; Du Preez, Cherisse; Edinger, Evan

2014-01-01

Efforts to locate and map deep-water coral and sponge habitats are essential for the effective management and conservation of these vulnerable marine ecosystems. Here we test the applicability of a simple multibeam sonar classification method developed for fjord environments to map the distribution of shelf-depth substrates and gorgonian coral- and sponge-dominated biotopes. The studied area is a shelf-depth feature Learmonth Bank, northern British Columbia, Canada and the method was applied aiming to map primarily non-reef forming coral and sponge biotopes. Aside from producing high-resolution maps (5 m2 raster grid), biotope-substrate associations were also investigated. A multibeam sonar survey yielded bathymetry, acoustic backscatter strength and slope. From benthic video transects recorded by remotely operated vehicles (ROVs) six primary substrate types and twelve biotope categories were identified, defined by the primary sediment and dominant biological structure, respectively. Substrate and biotope maps were produced using a supervised classification mostly based on the inter-quartile range of the acoustic variables for each substrate type and biotope. Twenty-five percent of the video observations were randomly reserved for testing the classification accuracy. The dominant biotope-defining corals were red tree coral Primnoa pacifica and small styasterids, of which Stylaster parageus was common. Demosponges and hexactinellid sponges were frequently observed but no sponge reefs were observed. The substrate classification readily distinguished fine sediment, Sand and Bedrock from the other substrate types, but had greater difficulty distinguishing Bedrock from Boulders and Cobble. The biotope classification accurately identified Gardens (dense aggregations of sponges and corals) and Primnoa-dominated biotopes (67% accuracy), but most other biotopes had lower accuracies. There was a significant correspondence between Learmonth's biotopes and substrate types, with many biotopes strongly associated with only a single substrate type. This strong correspondence allowed substrate types to function as a surrogate for helping to map biotope distributions. Our results add new information on the distribution of corals and sponges at Learmonth Bank, which can be used to guide management at this location.

Classification and Weakly Supervised Pain Localization using Multiple Segment Representation.

PubMed

Sikka, Karan; Dhall, Abhinav; Bartlett, Marian Stewart

2014-10-01

Automatic pain recognition from videos is a vital clinical application and, owing to its spontaneous nature, poses interesting challenges to automatic facial expression recognition (AFER) research. Previous pain vs no-pain systems have highlighted two major challenges: (1) ground truth is provided for the sequence, but the presence or absence of the target expression for a given frame is unknown, and (2) the time point and the duration of the pain expression event(s) in each video are unknown. To address these issues we propose a novel framework (referred to as MS-MIL) where each sequence is represented as a bag containing multiple segments, and multiple instance learning (MIL) is employed to handle this weakly labeled data in the form of sequence level ground-truth. These segments are generated via multiple clustering of a sequence or running a multi-scale temporal scanning window, and are represented using a state-of-the-art Bag of Words (BoW) representation. This work extends the idea of detecting facial expressions through 'concept frames' to 'concept segments' and argues through extensive experiments that algorithms such as MIL are needed to reap the benefits of such representation. The key advantages of our approach are: (1) joint detection and localization of painful frames using only sequence-level ground-truth, (2) incorporation of temporal dynamics by representing the data not as individual frames but as segments, and (3) extraction of multiple segments, which is well suited to signals with uncertain temporal location and duration in the video. Extensive experiments on UNBC-McMaster Shoulder Pain dataset highlight the effectiveness of the approach by achieving competitive results on both tasks of pain classification and localization in videos. We also empirically evaluate the contributions of different components of MS-MIL. The paper also includes the visualization of discriminative facial patches, important for pain detection, as discovered by our algorithm and relates them to Action Units that have been associated with pain expression. We conclude the paper by demonstrating that MS-MIL yields a significant improvement on another spontaneous facial expression dataset, the FEEDTUM dataset.

Role-Playing and Real-Time Strategy Games Associated with Greater Probability of Internet Gaming Disorder.

PubMed

Eichenbaum, Adam; Kattner, Florian; Bradford, Daniel; Gentile, Douglas A; Green, C Shawn

2015-08-01

Research indicates that a small subset of those who routinely play video games show signs of pathological habits, with side effects ranging from mild (e.g., being late) to quite severe (e.g., losing a job). However, it is still not clear whether individual types, or genres, of games are most strongly associated with Internet gaming disorder (IGD). A sample of 4,744 University of Wisconsin-Madison undergraduates (Mage=18.9 years; SD=1.9 years; 60.5% female) completed questionnaires on general video game playing habits and on symptoms of IGD. Consistent with previous reports: 5.9-10.8% (depending on classification criteria) of individuals who played video games show signs of pathological play. Furthermore, real-time strategy and role-playing video games were more strongly associated with pathological play, compared with action and other games (e.g., phone games). The current investigation adds support to the idea that not all video games are equal. Instead, certain genres of video games, specifically real-time strategy and role-playing/fantasy games, are disproportionately associated with IGD symptoms.

Diagnosing central lesions of the triangular fibrocartilage as traumatic or degenerative: a review of clinical accuracy.

PubMed

Löw, S; Erne, H; Pillukat, T; Mühldorfer-Fodor, M; Unglaub, F; Spies, C K

2017-05-01

This study examined the reliability of surgeons' estimations as to whether central lesions of the triangular fibrocartilage complex were traumatic or degenerative. A total of 50 consecutive central triangular fibrocartilage complex lesions were independently rated by ten experienced wrist surgeons viewing high-quality arthroscopy videos. The videos were reassessed after intervals of 3 months; at the second assessment surgeons were given the patient's history, radiographs and both, each in a randomized order. Finally, the surgeons assessed the histories and radiographs without the videos. Kappa statistics revealed fair interrater agreement when the histories were added to the videos. The other four modalities demonstrated moderate agreement, with lower Kappa values for the assessment without videos. Intra-rater reliability showed fair agreement for three surgeons, moderate agreement for two surgeons and substantial agreement for five surgeons. It appears that classification of central triangular fibrocartilage complex lesions depends on the information provided upon viewing the triangular fibrocartilage complex at arthroscopy. II.

Map Classification In Image Data

DTIC Science & Technology

2015-09-25

showing the signicant portion of image and video data transfers via Youtube , Facebook, and Flickr as primary platforms from Infographic (2015) digital...reserves • hydrography: lakes, rivers, streams, swamps, coastal flats • relief: mountains, valleys, slopes, depressions • vegetation: wooded and cleared

Interobserver reliability of video recording in the diagnosis of nocturnal frontal lobe seizures.

PubMed

Vignatelli, Luca; Bisulli, Francesca; Provini, Federica; Naldi, Ilaria; Pittau, Francesca; Zaniboni, Anna; Montagna, Pasquale; Tinuper, Paolo

2007-08-01

Nocturnal frontal lobe seizures (NFLS) show one or all of the following semeiological patterns: (1) paroxysmal arousals (PA: brief and sudden recurrent motor paroxysmal behavior); (2) hyperkinetic seizures (HS: motor attacks with complex dyskinetic features); (3) asymmetric bilateral tonic seizures (ATS: motor attacks with dystonic features); (4) epileptic nocturnal wanderings (ENW: stereotyped, prolonged ambulatory behavior). To estimate the interobserver reliability (IR) of video-recording diagnosis in patients with suspected NFLS among sleep medicine experts, epileptologists, and trainees in sleep medicine. Sixty-six patients with suspected NFLS were included. All underwent nocturnal video-polysomnographic recording. Six doctors (three experts and three trainees) independently classified each case as "NFLS ascertained" (according to the above specified subtypes: PA, HS, ATS, ENW) or "NFLS excluded". IR was calculated by means of Kappa statistics, and interpreted according to the standard classification (0.0-0.20 = slight agreement; 0.21-0.40 = fair; 0.41-0.60 = moderate; 0.61-0.80 = substantial; 0.81-1.00 = almost perfect). The observed raw agreement ranged from 63% to 79% between each pair of raters; the IR ranged from "moderate" (kappa = 0.50) to "substantial" (kappa = 0.72). A major source of variance was the disagreement in distinguishing between PA and nonepileptic arousals, without differences in the level of agreement between experts and trainees. Among sleep experts and trainees, IR of diagnosis of NFLS, based on videotaped observation of sleep phenomena, is not satisfactory. Explicit video-polysomnographic criteria for the classification of paroxysmal sleep motor phenomena are needed.

Creating a behavioural classification module for acceleration data: using a captive surrogate for difficult to observe species.

PubMed

Campbell, Hamish A; Gao, Lianli; Bidder, Owen R; Hunter, Jane; Franklin, Craig E

2013-12-15

Distinguishing specific behavioural modes from data collected by animal-borne tri-axial accelerometers can be a time-consuming and subjective process. Data synthesis can be further inhibited when the tri-axial acceleration data cannot be paired with the corresponding behavioural mode through direct observation. Here, we explored the use of a tame surrogate (domestic dog) to build a behavioural classification module, and then used that module to accurately identify and quantify behavioural modes within acceleration collected from other individuals/species. Tri-axial acceleration data were recorded from a domestic dog whilst it was commanded to walk, run, sit, stand and lie-down. Through video synchronisation, each tri-axial acceleration sample was annotated with its associated behavioural mode; the feature vectors were extracted and used to build the classification module through the application of support vector machines (SVMs). This behavioural classification module was then used to identify and quantify the same behavioural modes in acceleration collected from a range of other species (alligator, badger, cheetah, dingo, echidna, kangaroo and wombat). Evaluation of the module performance, using a binary classification system, showed there was a high capacity (>90%) for behaviour recognition between individuals of the same species. Furthermore, a positive correlation existed between SVM capacity and the similarity of the individual's spinal length-to-height above the ground ratio (SL:SH) to that of the surrogate. The study describes how to build a behavioural classification module and highlights the value of using a surrogate for studying cryptic, rare or endangered species.

Water quality real-time monitoring system via biological detection based on video analysis

NASA Astrophysics Data System (ADS)

Xin, Chen; Fei, Yuan

2017-11-01

With the development of society, water pollution has become the most serious problem in China. Therefore, real-time water quality monitoring is an important part of human activities and water pollution prevention. In this paper, the behavior of zebrafish was monitored by computer vision. Firstly, the moving target was extracted by the method of saliency detection, and tracked by fitting the ellipse model. Then the motion parameters were extracted by optical flow method, and the data were monitored in real time by means of Hinkley warning and threshold warning. We achieved classification warning through a number of dimensions by comprehensive toxicity index. The experimental results show that the system can achieve more accurate real-time monitoring.

A Neural-Network-Based Semi-Automated Geospatial Classification Tool

NASA Astrophysics Data System (ADS)

Hale, R. G.; Herzfeld, U. C.

2014-12-01

North America's largest glacier system, the Bering Bagley Glacier System (BBGS) in Alaska, surged in 2011-2013, as shown by rapid mass transfer, elevation change, and heavy crevassing. Little is known about the physics controlling surge glaciers' semi-cyclic patterns; therefore, it is crucial to collect and analyze as much data as possible so that predictive models can be made. In addition, physical signs frozen in ice in the form of crevasses may help serve as a warning for future surges. The BBGS surge provided an opportunity to develop an automated classification tool for crevasse classification based on imagery collected from small aircraft. The classification allows one to link image classification to geophysical processes associated with ice deformation. The tool uses an approach that employs geostatistical functions and a feed-forward perceptron with error back-propagation. The connectionist-geostatistical approach uses directional experimental (discrete) variograms to parameterize images into a form that the Neural Network (NN) can recognize. In an application to preform analysis on airborne video graphic data from the surge of the BBGS, an NN was able to distinguish 18 different crevasse classes with 95 percent or higher accuracy, for over 3,000 images. Recognizing that each surge wave results in different crevasse types and that environmental conditions affect the appearance in imagery, we designed the tool's semi-automated pre-training algorithm to be adaptable. The tool can be optimized to specific settings and variables of image analysis: (airborne and satellite imagery, different camera types, observation altitude, number and types of classes, and resolution). The generalization of the classification tool brings three important advantages: (1) multiple types of problems in geophysics can be studied, (2) the training process is sufficiently formalized to allow non-experts in neural nets to perform the training process, and (3) the time required to manually pre-sort imagery into classes is greatly reduced.

Sexual content in video games: an analysis of the Entertainment Software Rating Board classification from 1994 to 2013.

PubMed

Vidaña-Pérez, Dèsirée; Braverman-Bronstein, Ariela; Basto-Abreu, Ana; Barrientos-Gutierrez, Inti; Hilscher, Rainer; Barrientos-Gutierrez, Tonatiuh

2018-01-11

Background: Video games are widely used by children and adolescents and have become a significant source of exposure to sexual content. Despite evidence of the important role of media in the development of sexual attitudes and behaviours, little attention has been paid to monitor sexual content in video games. Methods: Data was obtained about sexual content and rating for 23722 video games from 1994 to 2013 from the Entertainment Software Rating Board database; release dates and information on the top 100 selling video games was also obtained. A yearly prevalence of sexual content according to rating categories was calculated. Trends and comparisons were estimated using Joinpoint regression. Results: Sexual content was present in 13% of the video games. Games rated 'Mature' had the highest prevalence of sexual content (34.5%) followed by 'Teen' (30.7%) and 'E10+' (21.3%). Over time, sexual content decreased in the 'Everyone' category, 'E10+' maintained a low prevalence and 'Teen' and 'Mature' showed a marked increase. Both top and non-top video games showed constant increases, with top selling video games having 10.1% more sexual content across the period of study. Conclusion: Over the last 20 years, the prevalence of sexual content has increased in video games with a 'Teen' or 'Mature' rating. Further studies are needed to quantify the potential association between sexual content in video games and sexual behaviour in children and adolescents.

Video-rate hyperspectral two-photon fluorescence microscopy for in vivo imaging

NASA Astrophysics Data System (ADS)

Deng, Fengyuan; Ding, Changqin; Martin, Jerald C.; Scarborough, Nicole M.; Song, Zhengtian; Eakins, Gregory S.; Simpson, Garth J.

2018-02-01

Fluorescence hyperspectral imaging is a powerful tool for in vivo biological studies. The ability to recover the full spectra of the fluorophores allows accurate classification of different structures and study of the dynamic behaviors during various biological processes. However, most existing methods require significant instrument modifications and/or suffer from image acquisition rates too low for compatibility with in vivo imaging. In the present work, a fast (up to 18 frames per second) hyperspectral two-photon fluorescence microscopy approach was demonstrated. Utilizing the beamscanning hardware inherent in conventional multi-photon microscopy, the angle dependence of the generated fluorescence signal as a function beam's position allowed the system to probe of a different potion of the spectrum at every single scanning line. An iterative algorithm to classify the fluorophores recovered spectra with up to 2,400 channels using a custom high-speed 16-channel photon multiplier tube array. Several dynamic samples including live fluorescent labeled C. elegans were imaged at video rate. Fluorescence spectra recovered using no a priori spectral information agreed well with those obtained by fluorimetry. This system required minimal changes to most existing beam-scanning multi-photon fluorescence microscopes, already accessible in many research facilities.

Robust skin color-based moving object detection for video surveillance

NASA Astrophysics Data System (ADS)

Kaliraj, Kalirajan; Manimaran, Sudha

2016-07-01

Robust skin color-based moving object detection for video surveillance is proposed. The objective of the proposed algorithm is to detect and track the target under complex situations. The proposed framework comprises four stages, which include preprocessing, skin color-based feature detection, feature classification, and target localization and tracking. In the preprocessing stage, the input image frame is smoothed using averaging filter and transformed into YCrCb color space. In skin color detection, skin color regions are detected using Otsu's method of global thresholding. In the feature classification, histograms of both skin and nonskin regions are constructed and the features are classified into foregrounds and backgrounds based on Bayesian skin color classifier. The foreground skin regions are localized by a connected component labeling process. Finally, the localized foreground skin regions are confirmed as a target by verifying the region properties, and nontarget regions are rejected using the Euler method. At last, the target is tracked by enclosing the bounding box around the target region in all video frames. The experiment was conducted on various publicly available data sets and the performance was evaluated with baseline methods. It evidently shows that the proposed algorithm works well against slowly varying illumination, target rotations, scaling, fast, and abrupt motion changes.

Physiological reactivity to nonideographic virtual reality stimuli in veterans with and without PTSD

PubMed Central

Webb, Andrea K; Vincent, Ashley L; Jin, Alvin B; Pollack, Mark H

2015-01-01

Background Post-traumatic stress disorder (PTSD) currently is diagnosed via clinical interview in which subjective self reports of traumatic events and associated experiences are discussed with a mental health professional. The reliability and validity of diagnoses can be improved with the use of objective physiological measures. Methods In this study, physiological activity was recorded from 58 male veterans (PTSD Diagnosis n = 16; Trauma Exposed/No PTSD Diagnosis: n = 23; No Trauma/No PTSD Diagnosis: n = 19) with and without PTSD and combat trauma exposure in response to emotionally evocative non-idiographic virtual reality stimuli. Results Statistically significant differences among the Control, Trauma, and PTSD groups were present during the viewing of two virtual reality videos. Skin conductance and interbeat interval features were extracted for each of ten video events (five events of increasing severity per video). These features were submitted to three stepwise discriminant function analyses to assess classification accuracy for Control versus Trauma, Control versus PTSD, and Trauma versus PTSD pairings of participant groups. Leave-one-out cross-validation classification accuracy was between 71 and 94%. Conclusions These results are promising and suggest the utility of objective physiological measures in assisting with PTSD diagnosis. PMID:25642387

King County Nearshore Habitat Mapping Data Report: Picnic Point to Shilshole Bay Marina

DOE Office of Scientific and Technical Information (OSTI.GOV)

Woodruff, Dana L.; Farley, Paul J.; Borde, Amy B.

2000-12-31

The objective of this study is to provide accurate, georeferenced maps of benthic habitats to assist in the siting of a new wastewater treatment plant outfall and the assessment of habitats of endangered, threatened, and economically important species. The mapping was conducted in the fall of 1999 using two complementary techniques: side-scan sonar and underwater videography. Products derived from these techniques include geographic information system (GIS) compatible polygon data of substrate type and vegetation cover, including eelgrass and kelp. Additional GIS overlays include underwater video track line data of total macroalgae, selected macroalgal species, fish, and macroinvertebrates. The combined toolsmore » of geo-referenced side-scan sonar and underwater video is a powerful technique for assessing and mapping of nearshore habitat in Puget Sound. Side-scan sonar offers the ability to map eelgrass with high spatial accuracy and resolution, and provides information on patch size, shape, and coverage. It also provides information on substrate change and location of specific targets (e.g., piers, docks, pilings, large boulders, debris piles). The addition of underwater video is a complementary tool providing both groundtruthing for the sonar and additional information on macro fauna and flora. As a groundtruthing technique, the video was able to confirm differences between substrate types, as well as detect subtle spatial changes in substrate. It also verified information related to eelgrass, including the density classification categories and the type of substrate associated with eelgrass, which could not be determined easily with side- scan sonar. Video is also a powerful tool for mapping the location of macroalgae, (including kelp and Ulva), fish and macroinvertebrates. The ability to geo-locate these resources in their functional habitat provides an added layer of information and analytical potential.« less

AdOn HDP-HMM: An Adaptive Online Model for Segmentation and Classification of Sequential Data.

PubMed

Bargi, Ava; Xu, Richard Yi Da; Piccardi, Massimo

2017-09-21

Recent years have witnessed an increasing need for the automated classification of sequential data, such as activities of daily living, social media interactions, financial series, and others. With the continuous flow of new data, it is critical to classify the observations on-the-fly and without being limited by a predetermined number of classes. In addition, a model should be able to update its parameters in response to a possible evolution in the distributions of the classes. This compelling problem, however, does not seem to have been adequately addressed in the literature, since most studies focus on offline classification over predefined class sets. In this paper, we present a principled solution for this problem based on an adaptive online system leveraging Markov switching models and hierarchical Dirichlet process priors. This adaptive online approach is capable of classifying the sequential data over an unlimited number of classes while meeting the memory and delay constraints typical of streaming contexts. In this paper, we introduce an adaptive ''learning rate'' that is responsible for balancing the extent to which the model retains its previous parameters or adapts to new observations. Experimental results on stationary and evolving synthetic data and two video data sets, TUM Assistive Kitchen and collated Weizmann, show a remarkable performance in terms of segmentation and classification, particularly for sequences from evolutionary distributions and/or those containing previously unseen classes.

Photometric stereo endoscopy

PubMed Central

Parot, Vicente; Lim, Daryl; González, Germán; Traverso, Giovanni; Nishioka, Norman S.; Vakoc, Benjamin J.

2013-01-01

Abstract. While color video endoscopy has enabled wide-field examination of the gastrointestinal tract, it often misses or incorrectly classifies lesions. Many of these missed lesions exhibit characteristic three-dimensional surface topographies. An endoscopic system that adds topographical measurements to conventional color imagery could therefore increase lesion detection and improve classification accuracy. We introduce photometric stereo endoscopy (PSE), a technique which allows high spatial frequency components of surface topography to be acquired simultaneously with conventional two-dimensional color imagery. We implement this technique in an endoscopic form factor and demonstrate that it can acquire the topography of small features with complex geometries and heterogeneous optical properties. PSE imaging of ex vivo human gastrointestinal tissue shows that surface topography measurements enable differentiation of abnormal shapes from surrounding normal tissue. Together, these results confirm that the topographical measurements can be obtained with relatively simple hardware in an endoscopic form factor, and suggest the potential of PSE to improve lesion detection and classification in gastrointestinal imaging. PMID:23864015

Participation of surgical residents in operations: challenging a common classification.

PubMed

Bezemer, Jeff; Cope, Alexandra; Faiz, Omar; Kneebone, Roger

2012-09-01

One important form of surgical training for residents is their participation in actual operations, for instance as an assistant or supervised surgeon. The aim of this study was to explore what participation in operations entails and how it might be described and analyzed. A qualitative study was undertaken in a major teaching hospital in London. A total of 122 general surgical operations were observed. A subsample of 14 laparoscopic cholecystectomies involving one or more residents was analyzed in detail. Audio and video recordings of eight operations were transcribed and analyzed linguistically. The degree of participation of trainees frequently shifted as the operation progressed to the next stage. Participation also varied within each stage. When trainees operated under supervision, the supervisors constantly adjusted their degree of control over the resident's operative maneuvers. Classifications such as "assistant" and "supervised surgeon" describing a trainee's overall participation in an operation potentially misrepresent the varying involvement of resident and supervisor. Video recordings provide a useful alternative for documenting and analyzing actual participation in operations.

Driver face recognition as a security and safety feature

NASA Astrophysics Data System (ADS)

Vetter, Volker; Giefing, Gerd-Juergen; Mai, Rudolf; Weisser, Hubert

1995-09-01

We present a driver face recognition system for comfortable access control and individual settings of automobiles. The primary goals are the prevention of car thefts and heavy accidents caused by unauthorized use (joy-riders), as well as the increase of safety through optimal settings, e.g. of the mirrors and the seat position. The person sitting on the driver's seat is observed automatically by a small video camera in the dashboard. All he has to do is to behave cooperatively, i.e. to look into the camera. A classification system validates his access. Only after a positive identification, the car can be used and the driver-specific environment (e.g. seat position, mirrors, etc.) may be set up to ensure the driver's comfort and safety. The driver identification system has been integrated in a Volkswagen research car. Recognition results are presented.

Multiview fusion for activity recognition using deep neural networks

NASA Astrophysics Data System (ADS)

Kavi, Rahul; Kulathumani, Vinod; Rohit, Fnu; Kecojevic, Vlad

2016-07-01

Convolutional neural networks (ConvNets) coupled with long short term memory (LSTM) networks have been recently shown to be effective for video classification as they combine the automatic feature extraction capabilities of a neural network with additional memory in the temporal domain. This paper shows how multiview fusion can be applied to such a ConvNet LSTM architecture. Two different fusion techniques are presented. The system is first evaluated in the context of a driver activity recognition system using data collected in a multicamera driving simulator. These results show significant improvement in accuracy with multiview fusion and also show that deep learning performs better than a traditional approach using spatiotemporal features even without requiring any background subtraction. The system is also validated on another publicly available multiview action recognition dataset that has 12 action classes and 8 camera views.

Problem-based learning using patient-simulated videos showing daily life for a comprehensive clinical approach

PubMed Central

Ohira, Yoshiyuki; Uehara, Takanori; Noda, Kazutaka; Suzuki, Shingo; Shikino, Kiyoshi; Kajiwara, Hideki; Kondo, Takeshi; Hirota, Yusuke; Ikusaka, Masatomi

2017-01-01

Objectives We examined whether problem-based learning tutorials using patient-simulated videos showing daily life are more practical for clinical learning, compared with traditional paper-based problem-based learning, for the consideration rate of psychosocial issues and the recall rate for experienced learning. Methods Twenty-two groups with 120 fifth-year students were each assigned paper-based problem-based learning and video-based problem-based learning using patient-simulated videos. We compared target achievement rates in questionnaires using the Wilcoxon signed-rank test and discussion contents diversity using the Mann-Whitney U test. A follow-up survey used a chi-square test to measure students’ recall of cases in three categories: video, paper, and non-experienced. Results Video-based problem-based learning displayed significantly higher achievement rates for imagining authentic patients (p=0.001), incorporating a comprehensive approach including psychosocial aspects (p<0.001), and satisfaction with sessions (p=0.001). No significant differences existed in the discussion contents diversity regarding the International Classification of Primary Care Second Edition codes and chapter types or in the rate of psychological codes. In a follow-up survey comparing video and paper groups to non-experienced groups, the rates were higher for video (χ2=24.319, p<0.001) and paper (χ2=11.134, p=0.001). Although the video rate tended to be higher than the paper rate, no significant difference was found between the two. Conclusions Patient-simulated videos showing daily life facilitate imagining true patients and support a comprehensive approach that fosters better memory. The clinical patient-simulated video method is more practical and clinical problem-based tutorials can be implemented if we create patient-simulated videos for each symptom as teaching materials.  PMID:28245193

Problem-based learning using patient-simulated videos showing daily life for a comprehensive clinical approach.

PubMed

Ikegami, Akiko; Ohira, Yoshiyuki; Uehara, Takanori; Noda, Kazutaka; Suzuki, Shingo; Shikino, Kiyoshi; Kajiwara, Hideki; Kondo, Takeshi; Hirota, Yusuke; Ikusaka, Masatomi

2017-02-27

We examined whether problem-based learning tutorials using patient-simulated videos showing daily life are more practical for clinical learning, compared with traditional paper-based problem-based learning, for the consideration rate of psychosocial issues and the recall rate for experienced learning. Twenty-two groups with 120 fifth-year students were each assigned paper-based problem-based learning and video-based problem-based learning using patient-simulated videos. We compared target achievement rates in questionnaires using the Wilcoxon signed-rank test and discussion contents diversity using the Mann-Whitney U test. A follow-up survey used a chi-square test to measure students' recall of cases in three categories: video, paper, and non-experienced. Video-based problem-based learning displayed significantly higher achievement rates for imagining authentic patients (p=0.001), incorporating a comprehensive approach including psychosocial aspects (p<0.001), and satisfaction with sessions (p=0.001). No significant differences existed in the discussion contents diversity regarding the International Classification of Primary Care Second Edition codes and chapter types or in the rate of psychological codes. In a follow-up survey comparing video and paper groups to non-experienced groups, the rates were higher for video (χ 2 =24.319, p<0.001) and paper (χ 2 =11.134, p=0.001). Although the video rate tended to be higher than the paper rate, no significant difference was found between the two. Patient-simulated videos showing daily life facilitate imagining true patients and support a comprehensive approach that fosters better memory. The clinical patient-simulated video method is more practical and clinical problem-based tutorials can be implemented if we create patient-simulated videos for each symptom as teaching materials.

Draper Laboratory small autonomous aerial vehicle

NASA Astrophysics Data System (ADS)

DeBitetto, Paul A.; Johnson, Eric N.; Bosse, Michael C.; Trott, Christian A.

1997-06-01

The Charles Stark Draper Laboratory, Inc. and students from Massachusetts Institute of Technology and Boston University have cooperated to develop an autonomous aerial vehicle that won the 1996 International Aerial Robotics Competition. This paper describes the approach, system architecture and subsystem designs for the entry. This entry represents a combination of many technology areas: navigation, guidance, control, vision processing, human factors, packaging, power, real-time software, and others. The aerial vehicle, an autonomous helicopter, performs navigation and control functions using multiple sensors: differential GPS, inertial measurement unit, sonar altimeter, and a flux compass. The aerial transmits video imagery to the ground. A ground based vision processor converts the image data into target position and classification estimates. The system was designed, built, and flown in less than one year and has provided many lessons about autonomous vehicle systems, several of which are discussed. In an appendix, our current research in augmenting the navigation system with vision- based estimates is presented.

Is Contrast Enhanced Ultrasonography a useful tool in a beginner's hand? How much can a Computer Assisted Diagnosis prototype help in characterizing the malignancy of focal liver lesions?

PubMed

Moga, Tudor Voicu; Popescu, Alina; Sporea, Ioan; Danila, Mirela; David, Ciprian; Gui, Vasile; Iacob, Nicoleta; Miclaus, Gratian; Sirli, Roxana

2017-08-23

Contrast enhanced ultrasound (CEUS) improved the characterization of focal liver lesions (FLLs), but is an operatordependent method. The goal of this paper was to test a computer assisted diagnosis (CAD) prototype and to see its benefit in assisting a beginner in the evaluation of FLLs. Our cohort included 97 good quality CEUS videos[34% hepatocellular carcinomas (HCC), 12.3% hypervascular metastases (HiperM), 11.3% hypovascular metastases (HipoM), 24.7% hemangiomas (HMG), 17.5% focal nodular hyperplasia (FNH)] that were used to develop a CAD prototype based on an algorithm that tested a binary decision based classifier. Two young medical doctors (1 year CEUS experience), two experts and the CAD prototype, reevaluated 50 FLLs CEUS videos (diagnosis of benign vs. malignant) first blinded to clinical data, in order to evaluate the diagnostic gap beginner vs. expert. The CAD classifier managed a 75.2% overall (benign vs. malignant) correct classification rate. The overall classification rates for the evaluators, before and after clinical data were: first beginner-78%; 94%; second beginner-82%; 96%; first expert-94%; 100%; second expert-96%; 98%. For both beginners, the malignant vs. benign diagnosis significantly improved after knowing the clinical data (p=0.005; p=0,008). The expert was better than the beginner (p=0.04) and better than the CAD (p=0.001). CAD in addition to the beginner can reach the expert diagnosis. The most frequent lesions misdiagnosed at CEUS were FNH and HCC. The CAD prototype is a good comparing tool for a beginner operator that can be developed to assist the diagnosis. In order to increase the classification rate, the CAD system for FLL in CEUS must integrate the clinical data.

Automated connectionist-geostatistical classification as an approach to identify sea ice and land ice types, properties and provinces

NASA Astrophysics Data System (ADS)

Goetz-Weiss, L. R.; Herzfeld, U. C.; Trantow, T.; Hunke, E. C.; Maslanik, J. A.; Crocker, R. I.

2016-12-01

An important problem in model-data comparison is the identification of parameters that can be extracted from observational data as well as used in numerical models, which are typically based on idealized physical processes. Here, we present a suite of approaches to characterization and classification of sea ice and land ice types, properties and provinces based on several types of remote-sensing data. Applications will be given to not only illustrate the approach, but employ it in model evaluation and understanding of physical processes. (1) In a geostatistical characterization, spatial sea-ice properties in the Chukchi and Beaufort Sea and in Elsoon Lagoon are derived from analysis of RADARSAT and ERS-2 SAR data. (2) The analysis is taken further by utilizing multi-parameter feature vectors as inputs for unsupervised and supervised statistical classification, which facilitates classification of different sea-ice types. (3) Characteristic sea-ice parameters, as resultant from the classification, can then be applied in model evaluation, as demonstrated for the ridging scheme of the Los Alamos sea ice model, CICE, using high-resolution altimeter and image data collected from unmanned aircraft over Fram Strait during the Characterization of Arctic Sea Ice Experiment (CASIE). The characteristic parameters chosen in this application are directly related to deformation processes, which also underly the ridging scheme. (4) The method that is capable of the most complex classification tasks is the connectionist-geostatistical classification method. This approach has been developed to identify currently up to 18 different crevasse types in order to map progression of the surge through the complex Bering-Bagley Glacier System, Alaska, in 2011-2014. The analysis utilizes airborne altimeter data and video image data and satellite image data. Results of the crevasse classification are compare to fracture modeling and found to match.

Track Everything: Limiting Prior Knowledge in Online Multi-Object Recognition.

PubMed

Wong, Sebastien C; Stamatescu, Victor; Gatt, Adam; Kearney, David; Lee, Ivan; McDonnell, Mark D

2017-10-01

This paper addresses the problem of online tracking and classification of multiple objects in an image sequence. Our proposed solution is to first track all objects in the scene without relying on object-specific prior knowledge, which in other systems can take the form of hand-crafted features or user-based track initialization. We then classify the tracked objects with a fast-learning image classifier, that is based on a shallow convolutional neural network architecture and demonstrate that object recognition improves when this is combined with object state information from the tracking algorithm. We argue that by transferring the use of prior knowledge from the detection and tracking stages to the classification stage, we can design a robust, general purpose object recognition system with the ability to detect and track a variety of object types. We describe our biologically inspired implementation, which adaptively learns the shape and motion of tracked objects, and apply it to the Neovision2 Tower benchmark data set, which contains multiple object types. An experimental evaluation demonstrates that our approach is competitive with the state-of-the-art video object recognition systems that do make use of object-specific prior knowledge in detection and tracking, while providing additional practical advantages by virtue of its generality.

A Psychometric Measure of Working Memory Capacity for Configured Body Movement

PubMed Central

Wu, Ying Choon; Coulson, Seana

2014-01-01

Working memory (WM) models have traditionally assumed at least two domain-specific storage systems for verbal and visuo-spatial information. We review data that suggest the existence of an additional slave system devoted to the temporary storage of body movements, and present a novel instrument for its assessment: the movement span task. The movement span task assesses individuals' ability to remember and reproduce meaningless configurations of the body. During the encoding phase of a trial, participants watch short videos of meaningless movements presented in sets varying in size from one to five items. Immediately after encoding, they are prompted to reenact as many items as possible. The movement span task was administered to 90 participants along with standard tests of verbal WM, visuo-spatial WM, and a gesture classification test in which participants judged whether a speaker's gestures were congruent or incongruent with his accompanying speech. Performance on the gesture classification task was not related to standard measures of verbal or visuo-spatial working memory capacity, but was predicted by scores on the movement span task. Results suggest the movement span task can serve as an assessment of individual differences in WM capacity for body-centric information. PMID:24465437

DANoC: An Efficient Algorithm and Hardware Codesign of Deep Neural Networks on Chip.

PubMed

Zhou, Xichuan; Li, Shengli; Tang, Fang; Hu, Shengdong; Lin, Zhi; Zhang, Lei

2017-07-18

Deep neural networks (NNs) are the state-of-the-art models for understanding the content of images and videos. However, implementing deep NNs in embedded systems is a challenging task, e.g., a typical deep belief network could exhaust gigabytes of memory and result in bandwidth and computational bottlenecks. To address this challenge, this paper presents an algorithm and hardware codesign for efficient deep neural computation. A hardware-oriented deep learning algorithm, named the deep adaptive network, is proposed to explore the sparsity of neural connections. By adaptively removing the majority of neural connections and robustly representing the reserved connections using binary integers, the proposed algorithm could save up to 99.9% memory utility and computational resources without undermining classification accuracy. An efficient sparse-mapping-memory-based hardware architecture is proposed to fully take advantage of the algorithmic optimization. Different from traditional Von Neumann architecture, the deep-adaptive network on chip (DANoC) brings communication and computation in close proximity to avoid power-hungry parameter transfers between on-board memory and on-chip computational units. Experiments over different image classification benchmarks show that the DANoC system achieves competitively high accuracy and efficiency comparing with the state-of-the-art approaches.

An embedded system for face classification in infrared video using sparse representation

NASA Astrophysics Data System (ADS)

Saavedra M., Antonio; Pezoa, Jorge E.; Zarkesh-Ha, Payman; Figueroa, Miguel

2017-09-01

We propose a platform for robust face recognition in Infrared (IR) images using Compressive Sensing (CS). In line with CS theory, the classification problem is solved using a sparse representation framework, where test images are modeled by means of a linear combination of the training set. Because the training set constitutes an over-complete dictionary, we identify new images by finding their sparsest representation based on the training set, using standard l1-minimization algorithms. Unlike conventional face-recognition algorithms, we feature extraction is performed using random projections with a precomputed binary matrix, as proposed in the CS literature. This random sampling reduces the effects of noise and occlusions such as facial hair, eyeglasses, and disguises, which are notoriously challenging in IR images. Thus, the performance of our framework is robust to these noise and occlusion factors, achieving an average accuracy of approximately 90% when the UCHThermalFace database is used for training and testing purposes. We implemented our framework on a high-performance embedded digital system, where the computation of the sparse representation of IR images was performed by a dedicated hardware using a deeply pipelined architecture on an Field-Programmable Gate Array (FPGA).

Distinguish self- and hetero-perceived stress through behavioral imaging and physiological features.

PubMed

Spodenkiewicz, Michel; Aigrain, Jonathan; Bourvis, Nadège; Dubuisson, Séverine; Chetouani, Mohamed; Cohen, David

2018-03-02

Stress reactivity is a complex phenomenon associated to multiple and multimodal expressions. Response to stressors has an obvious survival function and may be seen as an internal regulation to adapt to threat or danger. The intensity of this internal response can be assessed as the self-perception of the stress response. In species with social organization, this response also serves a communicative function, so-called hetero-perception. Our study presents multimodal stress detection assessment - a new methodology combining behavioral imaging and physiological monitoring for analyzing stress from these two perspectives. The system is based on automatic extraction of 39 behavioral (2D+3D video recording) and 62 physiological (Nexus-10 recording) features during a socially evaluated mental arithmetic test. The analysis with machine learning techniques for automatic classification using Support Vector Machine (SVM) show that self-perception and hetero-perception of social stress are both close but different phenomena: self-perception was significantly correlated with hetero-perception but significantly differed from it. Also, assessing stress with SVM through multimodality gave excellent classification results (F1 score values: 0.9±0.012 for hetero-perception and 0.87±0.021 for self-perception). In the best selected feature subsets, we found some common behavioral and physiological features that allow classification of both self- and hetero-perceived stress. However, we also found the contributing features for automatic classifications had opposite distributions: self-perception classification was mainly based on physiological features and hetero-perception was mainly based on behavioral features. Copyright © 2017. Published by Elsevier Inc.

Audio-based queries for video retrieval over Java enabled mobile devices

NASA Astrophysics Data System (ADS)

Ahmad, Iftikhar; Cheikh, Faouzi Alaya; Kiranyaz, Serkan; Gabbouj, Moncef

2006-02-01

In this paper we propose a generic framework for efficient retrieval of audiovisual media based on its audio content. This framework is implemented in a client-server architecture where the client application is developed in Java to be platform independent whereas the server application is implemented for the PC platform. The client application adapts to the characteristics of the mobile device where it runs such as screen size and commands. The entire framework is designed to take advantage of the high-level segmentation and classification of audio content to improve speed and accuracy of audio-based media retrieval. Therefore, the primary objective of this framework is to provide an adaptive basis for performing efficient video retrieval operations based on the audio content and types (i.e. speech, music, fuzzy and silence). Experimental results approve that such an audio based video retrieval scheme can be used from mobile devices to search and retrieve video clips efficiently over wireless networks.

47 CFR 76.1503 - Carriage of video programming providers on open video systems.

Code of Federal Regulations, 2011 CFR

2011-10-01

... 47 Telecommunication 4 2011-10-01 2011-10-01 false Carriage of video programming providers on open video systems. 76.1503 Section 76.1503 Telecommunication FEDERAL COMMUNICATIONS COMMISSION (CONTINUED) BROADCAST RADIO SERVICES MULTICHANNEL VIDEO AND CABLE TELEVISION SERVICE Open Video Systems § 76.1503...

47 CFR 76.1503 - Carriage of video programming providers on open video systems.

Code of Federal Regulations, 2012 CFR

2012-10-01

... 47 Telecommunication 4 2012-10-01 2012-10-01 false Carriage of video programming providers on open video systems. 76.1503 Section 76.1503 Telecommunication FEDERAL COMMUNICATIONS COMMISSION (CONTINUED) BROADCAST RADIO SERVICES MULTICHANNEL VIDEO AND CABLE TELEVISION SERVICE Open Video Systems § 76.1503...

47 CFR 76.1503 - Carriage of video programming providers on open video systems.

Code of Federal Regulations, 2014 CFR

2014-10-01

... 47 Telecommunication 4 2014-10-01 2014-10-01 false Carriage of video programming providers on open video systems. 76.1503 Section 76.1503 Telecommunication FEDERAL COMMUNICATIONS COMMISSION (CONTINUED) BROADCAST RADIO SERVICES MULTICHANNEL VIDEO AND CABLE TELEVISION SERVICE Open Video Systems § 76.1503...

47 CFR 76.1503 - Carriage of video programming providers on open video systems.

Code of Federal Regulations, 2010 CFR

2010-10-01

... 47 Telecommunication 4 2010-10-01 2010-10-01 false Carriage of video programming providers on open video systems. 76.1503 Section 76.1503 Telecommunication FEDERAL COMMUNICATIONS COMMISSION (CONTINUED) BROADCAST RADIO SERVICES MULTICHANNEL VIDEO AND CABLE TELEVISION SERVICE Open Video Systems § 76.1503...

47 CFR 76.1503 - Carriage of video programming providers on open video systems.

Code of Federal Regulations, 2013 CFR

2013-10-01

... 47 Telecommunication 4 2013-10-01 2013-10-01 false Carriage of video programming providers on open video systems. 76.1503 Section 76.1503 Telecommunication FEDERAL COMMUNICATIONS COMMISSION (CONTINUED) BROADCAST RADIO SERVICES MULTICHANNEL VIDEO AND CABLE TELEVISION SERVICE Open Video Systems § 76.1503...

Information processing of motion in facial expression and the geometry of dynamical systems

NASA Astrophysics Data System (ADS)

Assadi, Amir H.; Eghbalnia, Hamid; McMenamin, Brenton W.

2005-01-01

An interesting problem in analysis of video data concerns design of algorithms that detect perceptually significant features in an unsupervised manner, for instance methods of machine learning for automatic classification of human expression. A geometric formulation of this genre of problems could be modeled with help of perceptual psychology. In this article, we outline one approach for a special case where video segments are to be classified according to expression of emotion or other similar facial motions. The encoding of realistic facial motions that convey expression of emotions for a particular person P forms a parameter space XP whose study reveals the "objective geometry" for the problem of unsupervised feature detection from video. The geometric features and discrete representation of the space XP are independent of subjective evaluations by observers. While the "subjective geometry" of XP varies from observer to observer, levels of sensitivity and variation in perception of facial expressions appear to share a certain level of universality among members of similar cultures. Therefore, statistical geometry of invariants of XP for a sample of population could provide effective algorithms for extraction of such features. In cases where frequency of events is sufficiently large in the sample data, a suitable framework could be provided to facilitate the information-theoretic organization and study of statistical invariants of such features. This article provides a general approach to encode motion in terms of a particular genre of dynamical systems and the geometry of their flow. An example is provided to illustrate the general theory.

Motivation Classification and Grade Prediction for MOOCs Learners

PubMed Central

Xu, Bin; Yang, Dan

2016-01-01

While MOOCs offer educational data on a new scale, many educators find great potential of the big data including detailed activity records of every learner. A learner's behavior such as if a learner will drop out from the course can be predicted. How to provide an effective, economical, and scalable method to detect cheating on tests such as surrogate exam-taker is a challenging problem. In this paper, we present a grade predicting method that uses student activity features to predict whether a learner may get a certification if he/she takes a test. The method consists of two-step classifications: motivation classification (MC) and grade classification (GC). The MC divides all learners into three groups including certification earning, video watching, and course sampling. The GC then predicts a certification earning learner may or may not obtain a certification. Our experiment shows that the proposed method can fit the classification model at a fine scale and it is possible to find a surrogate exam-taker. PMID:26884747

Motivation Classification and Grade Prediction for MOOCs Learners.

PubMed

Xu, Bin; Yang, Dan

2016-01-01

While MOOCs offer educational data on a new scale, many educators find great potential of the big data including detailed activity records of every learner. A learner's behavior such as if a learner will drop out from the course can be predicted. How to provide an effective, economical, and scalable method to detect cheating on tests such as surrogate exam-taker is a challenging problem. In this paper, we present a grade predicting method that uses student activity features to predict whether a learner may get a certification if he/she takes a test. The method consists of two-step classifications: motivation classification (MC) and grade classification (GC). The MC divides all learners into three groups including certification earning, video watching, and course sampling. The GC then predicts a certification earning learner may or may not obtain a certification. Our experiment shows that the proposed method can fit the classification model at a fine scale and it is possible to find a surrogate exam-taker.

Overview: Pyraloidea adults (Insecta: Lepidoptera)

USDA-ARS?s Scientific Manuscript database

There are over 16,000 species of pyraloid or snout moths worldwide and many are pests of crops and stored products. The purpose of this video is twofold: to provide an overview of the current, modern classification of snout moths and to provide tools using morphology to identify adult snout moths t...

Misleading Health-Related Information Promoted Through Video-Based Social Media: Anorexia on YouTube

PubMed Central

Li, Yu-Chuan; Crain, Steven; Hsu, Min-Huei; Wang, Yao-Chin; Khandregzen, Dorjsuren; Chuluunbaatar, Enkhzaya; Nguyen, Phung Anh

2013-01-01

Introduction The amount of information being uploaded onto social video platforms, such as YouTube, Vimeo, and Veoh, continues to spiral, making it increasingly difficult to discern reliable health information from misleading content. There are thousands of YouTube videos promoting misleading information about anorexia (eg, anorexia as a healthy lifestyle). Objective The aim of this study was to investigate anorexia-related misinformation disseminated through YouTube videos. Methods We retrieved YouTube videos related to anorexia using the keywords anorexia, anorexia nervosa, proana, and thinspo on October 10, 2011.Three doctors reviewed 140 videos with approximately 11 hours of video content, classifying them as informative, pro-anorexia, or others. By informative we mean content describing the health consequences of anorexia and advice on how to recover from it; by pro-anorexia we mean videos promoting anorexia as a fashion, a source of beauty, and that share tips and methods for becoming and remaining anorexic. The 40 most-viewed videos (20 informative and 20 pro-anorexia videos) were assessed to gauge viewer behavior. Results The interrater agreement of classification was moderate (Fleiss’ kappa=0.5), with 29.3% (n=41) being rated as pro-anorexia, 55.7% (n=78) as informative, and 15.0% (n=21) as others. Pro-anorexia videos were favored 3 times more than informative videos (odds ratio [OR] 3.3, 95% CI 3.3-3.4, P<.001). Conclusions Pro-anorexia information was identified in 29.3% of anorexia-related videos. Pro-anorexia videos are less common than informative videos; however, in proportional terms, pro-anorexia content is more highly favored and rated by its viewers. Efforts should focus on raising awareness, particularly among teenagers, about the trustworthiness of online information about beauty and healthy lifestyles. Health authorities producing videos to combat anorexia should consider involving celebrities and models to reach a wider audience. More research is needed to study the characteristics of pro-anorexia videos in order to develop algorithms that will automatically detect and filter those videos before they become popular. PMID:23406655

Misleading health-related information promoted through video-based social media: anorexia on YouTube.

PubMed

Syed-Abdul, Shabbir; Fernandez-Luque, Luis; Jian, Wen-Shan; Li, Yu-Chuan; Crain, Steven; Hsu, Min-Huei; Wang, Yao-Chin; Khandregzen, Dorjsuren; Chuluunbaatar, Enkhzaya; Nguyen, Phung Anh; Liou, Der-Ming

2013-02-13

The amount of information being uploaded onto social video platforms, such as YouTube, Vimeo, and Veoh, continues to spiral, making it increasingly difficult to discern reliable health information from misleading content. There are thousands of YouTube videos promoting misleading information about anorexia (eg, anorexia as a healthy lifestyle). The aim of this study was to investigate anorexia-related misinformation disseminated through YouTube videos. We retrieved YouTube videos related to anorexia using the keywords anorexia, anorexia nervosa, proana, and thinspo on October 10, 2011.Three doctors reviewed 140 videos with approximately 11 hours of video content, classifying them as informative, pro-anorexia, or others. By informative we mean content describing the health consequences of anorexia and advice on how to recover from it; by pro-anorexia we mean videos promoting anorexia as a fashion, a source of beauty, and that share tips and methods for becoming and remaining anorexic. The 40 most-viewed videos (20 informative and 20 pro-anorexia videos) were assessed to gauge viewer behavior. The interrater agreement of classification was moderate (Fleiss' kappa=0.5), with 29.3% (n=41) being rated as pro-anorexia, 55.7% (n=78) as informative, and 15.0% (n=21) as others. Pro-anorexia videos were favored 3 times more than informative videos (odds ratio [OR] 3.3, 95% CI 3.3-3.4, P<.001). Pro-anorexia information was identified in 29.3% of anorexia-related videos. Pro-anorexia videos are less common than informative videos; however, in proportional terms, pro-anorexia content is more highly favored and rated by its viewers. Efforts should focus on raising awareness, particularly among teenagers, about the trustworthiness of online information about beauty and healthy lifestyles. Health authorities producing videos to combat anorexia should consider involving celebrities and models to reach a wider audience. More research is needed to study the characteristics of pro-anorexia videos in order to develop algorithms that will automatically detect and filter those videos before they become popular.

47 CFR 76.1504 - Rates, terms and conditions for carriage on open video systems.

Code of Federal Regulations, 2012 CFR

2012-10-01

... open video systems. 76.1504 Section 76.1504 Telecommunication FEDERAL COMMUNICATIONS COMMISSION (CONTINUED) BROADCAST RADIO SERVICES MULTICHANNEL VIDEO AND CABLE TELEVISION SERVICE Open Video Systems § 76.1504 Rates, terms and conditions for carriage on open video systems. (a) Reasonable rate principle. An...

47 CFR 76.1504 - Rates, terms and conditions for carriage on open video systems.

Code of Federal Regulations, 2011 CFR

2011-10-01

... open video systems. 76.1504 Section 76.1504 Telecommunication FEDERAL COMMUNICATIONS COMMISSION (CONTINUED) BROADCAST RADIO SERVICES MULTICHANNEL VIDEO AND CABLE TELEVISION SERVICE Open Video Systems § 76.1504 Rates, terms and conditions for carriage on open video systems. (a) Reasonable rate principle. An...

47 CFR 76.1504 - Rates, terms and conditions for carriage on open video systems.

Code of Federal Regulations, 2014 CFR

2014-10-01

... open video systems. 76.1504 Section 76.1504 Telecommunication FEDERAL COMMUNICATIONS COMMISSION (CONTINUED) BROADCAST RADIO SERVICES MULTICHANNEL VIDEO AND CABLE TELEVISION SERVICE Open Video Systems § 76.1504 Rates, terms and conditions for carriage on open video systems. (a) Reasonable rate principle. An...

47 CFR 76.1504 - Rates, terms and conditions for carriage on open video systems.

Code of Federal Regulations, 2013 CFR

2013-10-01

... open video systems. 76.1504 Section 76.1504 Telecommunication FEDERAL COMMUNICATIONS COMMISSION (CONTINUED) BROADCAST RADIO SERVICES MULTICHANNEL VIDEO AND CABLE TELEVISION SERVICE Open Video Systems § 76.1504 Rates, terms and conditions for carriage on open video systems. (a) Reasonable rate principle. An...

A systematic review of serious video games used for vaccination.

PubMed

Ohannessian, Robin; Yaghobian, Sarina; Verger, Pierre; Vanhems, Philippe

2016-08-31

Vaccination is an effective and proven method of preventing infectious diseases. However, uptake has not been optimal with available vaccines partly due to vaccination hesitancy. Various public health approaches have adressed vaccination hesitancy. Serious video games involving vaccination may represent an innovative public health approach. The aim of this study was to identify, describe, and review existing serious video games on vaccination. A systematic review was performed. Various databases were used to find data on vaccination-related serious video games published from January 1st 2000 to May 15th 2015. Data including featured medical and vaccination content, publication characteristics and games classification were collected for each identified serious game. Sixteen serious video games involved in vaccination were identified. All games were developed in high-income countries between 2003 and 2014. The majority of games were available online and were sponsored by educational/health institutions. All games were free of charge to users. Edugame was the most prevalent serious game subcategory. Twelve games were infectious disease-specific and the majority concerned influenza. The main objective of the games was disease control with a collective perspective. Utilization data was available for two games. Two games were formally evaluated. The use of serious video games for vaccination is an innovative tool for public health. Evaluation of vaccination related serious video games should be encouraged to demonstrate their efficacy and utility. Copyright © 2016 Elsevier Ltd. All rights reserved.

Action Spotting and Recognition Based on a Spatiotemporal Orientation Analysis.

PubMed

Derpanis, Konstantinos G; Sizintsev, Mikhail; Cannons, Kevin J; Wildes, Richard P

2013-03-01

This paper provides a unified framework for the interrelated topics of action spotting, the spatiotemporal detection and localization of human actions in video, and action recognition, the classification of a given video into one of several predefined categories. A novel compact local descriptor of video dynamics in the context of action spotting and recognition is introduced based on visual spacetime oriented energy measurements. This descriptor is efficiently computed directly from raw image intensity data and thereby forgoes the problems typically associated with flow-based features. Importantly, the descriptor allows for the comparison of the underlying dynamics of two spacetime video segments irrespective of spatial appearance, such as differences induced by clothing, and with robustness to clutter. An associated similarity measure is introduced that admits efficient exhaustive search for an action template, derived from a single exemplar video, across candidate video sequences. The general approach presented for action spotting and recognition is amenable to efficient implementation, which is deemed critical for many important applications. For action spotting, details of a real-time GPU-based instantiation of the proposed approach are provided. Empirical evaluation of both action spotting and action recognition on challenging datasets suggests the efficacy of the proposed approach, with state-of-the-art performance documented on standard datasets.

State of the art in video system performance

NASA Technical Reports Server (NTRS)

Lewis, Michael J.

1990-01-01

The closed circuit television (CCTV) system that is onboard the Space Shuttle has the following capabilities: camera, video signal switching and routing unit (VSU); and Space Shuttle video tape recorder. However, this system is inadequate for use with many experiments that require video imaging. In order to assess the state-of-the-art in video technology and data storage systems, a survey was conducted of the High Resolution, High Frame Rate Video Technology (HHVT) products. The performance of the state-of-the-art solid state cameras and image sensors, video recording systems, data transmission devices, and data storage systems versus users' requirements are shown graphically.

Applying emerging digital video interface standards to airborne avionics sensor and digital map integrations: benefits outweigh the initial costs

NASA Astrophysics Data System (ADS)

Kuehl, C. Stephen

1996-06-01

Video signal system performance can be compromised in a military aircraft cockpit management system (CMS) with the tailoring of vintage Electronics Industries Association (EIA) RS170 and RS343A video interface standards. Video analog interfaces degrade when induced system noise is present. Further signal degradation has been traditionally associated with signal data conversions between avionics sensor outputs and the cockpit display system. If the CMS engineering process is not carefully applied during the avionics video and computing architecture development, extensive and costly redesign will occur when visual sensor technology upgrades are incorporated. Close monitoring and technical involvement in video standards groups provides the knowledge-base necessary for avionic systems engineering organizations to architect adaptable and extendible cockpit management systems. With the Federal Communications Commission (FCC) in the process of adopting the Digital HDTV Grand Alliance System standard proposed by the Advanced Television Systems Committee (ATSC), the entertainment and telecommunications industries are adopting and supporting the emergence of new serial/parallel digital video interfaces and data compression standards that will drastically alter present NTSC-M video processing architectures. The re-engineering of the U.S. Broadcasting system must initially preserve the electronic equipment wiring networks within broadcast facilities to make the transition to HDTV affordable. International committee activities in technical forums like ITU-R (former CCIR), ANSI/SMPTE, IEEE, and ISO/IEC are establishing global consensus on video signal parameterizations that support a smooth transition from existing analog based broadcasting facilities to fully digital computerized systems. An opportunity exists for implementing these new video interface standards over existing video coax/triax cabling in military aircraft cockpit management systems. Reductions in signal conversion processing steps, major improvement in video noise reduction, and an added capability to pass audio/embedded digital data within the digital video signal stream are the significant performance increases associated with the incorporation of digital video interface standards. By analyzing the historical progression of military CMS developments, establishing a systems engineering process for CMS design, tracing the commercial evolution of video signal standardization, adopting commercial video signal terminology/definitions, and comparing/contrasting CMS architecture modifications using digital video interfaces; this paper provides a technical explanation on how a systems engineering process approach to video interface standardization can result in extendible and affordable cockpit management systems.

System Synchronizes Recordings from Separated Video Cameras

NASA Technical Reports Server (NTRS)

Nail, William; Nail, William L.; Nail, Jasper M.; Le, Doung T.

2009-01-01

A system of electronic hardware and software for synchronizing recordings from multiple, physically separated video cameras is being developed, primarily for use in multiple-look-angle video production. The system, the time code used in the system, and the underlying method of synchronization upon which the design of the system is based are denoted generally by the term "Geo-TimeCode(TradeMark)." The system is embodied mostly in compact, lightweight, portable units (see figure) denoted video time-code units (VTUs) - one VTU for each video camera. The system is scalable in that any number of camera recordings can be synchronized. The estimated retail price per unit would be about $350 (in 2006 dollars). The need for this or another synchronization system external to video cameras arises because most video cameras do not include internal means for maintaining synchronization with other video cameras. Unlike prior video-camera-synchronization systems, this system does not depend on continuous cable or radio links between cameras (however, it does depend on occasional cable links lasting a few seconds). Also, whereas the time codes used in prior video-camera-synchronization systems typically repeat after 24 hours, the time code used in this system does not repeat for slightly more than 136 years; hence, this system is much better suited for long-term deployment of multiple cameras.

The Addition of a Video Game to Stationary Cycling: The Impact on Energy Expenditure in Overweight Children.

PubMed

Haddock, Bryan L; Siegel, Shannon R; Wikin, Linda D

2009-01-01

INTRODUCTION: The prevalence of obesity in children has reached epidemic proportions with over 37% of children aged 6-11 years in the U.S. being classified as "at risk for overweight" or "overweight." Utilization of active video games has been proposed as one possible mechanism to help shift the tide of the obesity epidemic. PURPOSE: The purpose of this study was to determine if riding a stationary bike that controlled a video game would lead to significantly greater energy expenditure than riding the same bike without the video game connected. METHODS: Twenty children, 7-14 years old, with a BMI classification of "at risk for overweight" or "overweight" participated in this study. Following familiarization, energy expenditure was evaluated while riding a stationary bike for 20 minutes. One test was performed without the addition of a video game and one test with the bike controlling the speed of a car on the video game. RESULTS: Oxygen consumption and energy expenditure were significantly elevated above baseline in both conditions. Energy expenditure was significantly higher while riding the bike as it controlled the video game (4.4 ± 1.2 Kcal·min(-1)) than when riding the bike by itself (3.7 ± 1.1 Kcal·min(-1)) (p<0.05). Perceived exertion was not significantly different between the two sessions (p>0.05). CONCLUSION: Using a stationary bike to control a video game led to greater energy expenditure than riding a stationary bike without the video game and without a related increase in perceived exertion.

An Unequal Secure Encryption Scheme for H.264/AVC Video Compression Standard

NASA Astrophysics Data System (ADS)

Fan, Yibo; Wang, Jidong; Ikenaga, Takeshi; Tsunoo, Yukiyasu; Goto, Satoshi

H.264/AVC is the newest video coding standard. There are many new features in it which can be easily used for video encryption. In this paper, we propose a new scheme to do video encryption for H.264/AVC video compression standard. We define Unequal Secure Encryption (USE) as an approach that applies different encryption schemes (with different security strength) to different parts of compressed video data. This USE scheme includes two parts: video data classification and unequal secure video data encryption. Firstly, we classify the video data into two partitions: Important data partition and unimportant data partition. Important data partition has small size with high secure protection, while unimportant data partition has large size with low secure protection. Secondly, we use AES as a block cipher to encrypt the important data partition and use LEX as a stream cipher to encrypt the unimportant data partition. AES is the most widely used symmetric cryptography which can ensure high security. LEX is a new stream cipher which is based on AES and its computational cost is much lower than AES. In this way, our scheme can achieve both high security and low computational cost. Besides the USE scheme, we propose a low cost design of hybrid AES/LEX encryption module. Our experimental results show that the computational cost of the USE scheme is low (about 25% of naive encryption at Level 0 with VEA used). The hardware cost for hybrid AES/LEX module is 4678 Gates and the AES encryption throughput is about 50Mbps.

Image acquisition system for traffic monitoring applications

NASA Astrophysics Data System (ADS)

Auty, Glen; Corke, Peter I.; Dunn, Paul; Jensen, Murray; Macintyre, Ian B.; Mills, Dennis C.; Nguyen, Hao; Simons, Ben

1995-03-01

An imaging system for monitoring traffic on multilane highways is discussed. The system, named Safe-T-Cam, is capable of operating 24 hours per day in all but extreme weather conditions and can capture still images of vehicles traveling up to 160 km/hr. Systems operating at different remote locations are networked to allow transmission of images and data to a control center. A remote site facility comprises a vehicle detection and classification module (VCDM), an image acquisition module (IAM) and a license plate recognition module (LPRM). The remote site is connected to the central site by an ISDN communications network. The remote site system is discussed in this paper. The VCDM consists of a video camera, a specialized exposure control unit to maintain consistent image characteristics, and a 'real-time' image processing system that processes 50 images per second. The VCDM can detect and classify vehicles (e.g. cars from trucks). The vehicle class is used to determine what data should be recorded. The VCDM uses a vehicle tracking technique to allow optimum triggering of the high resolution camera of the IAM. The IAM camera combines the features necessary to operate consistently in the harsh environment encountered when imaging a vehicle 'head-on' in both day and night conditions. The image clarity obtained is ideally suited for automatic location and recognition of the vehicle license plate. This paper discusses the camera geometry, sensor characteristics and the image processing methods which permit consistent vehicle segmentation from a cluttered background allowing object oriented pattern recognition to be used for vehicle classification. The image capture of high resolution images and the image characteristics required for the LPRMs automatic reading of vehicle license plates, is also discussed. The results of field tests presented demonstrate that the vision based Safe-T-Cam system, currently installed on open highways, is capable of producing automatic classification of vehicle class and recording of vehicle numberplates with a success rate around 90 percent in a period of 24 hours.

47 CFR 76.1712 - Open video system (OVS) requests for carriage.

Code of Federal Regulations, 2011 CFR

2011-10-01

... 47 Telecommunication 4 2011-10-01 2011-10-01 false Open video system (OVS) requests for carriage... RADIO SERVICES MULTICHANNEL VIDEO AND CABLE TELEVISION SERVICE Documents to be Maintained for Inspection § 76.1712 Open video system (OVS) requests for carriage. An open video system operator shall maintain a...

47 CFR 76.1712 - Open video system (OVS) requests for carriage.

Code of Federal Regulations, 2010 CFR

2010-10-01

... 47 Telecommunication 4 2010-10-01 2010-10-01 false Open video system (OVS) requests for carriage... RADIO SERVICES MULTICHANNEL VIDEO AND CABLE TELEVISION SERVICE Documents to be Maintained for Inspection § 76.1712 Open video system (OVS) requests for carriage. An open video system operator shall maintain a...

47 CFR 76.1712 - Open video system (OVS) requests for carriage.

Code of Federal Regulations, 2014 CFR

2014-10-01

... 47 Telecommunication 4 2014-10-01 2014-10-01 false Open video system (OVS) requests for carriage... RADIO SERVICES MULTICHANNEL VIDEO AND CABLE TELEVISION SERVICE Documents to be Maintained for Inspection § 76.1712 Open video system (OVS) requests for carriage. An open video system operator shall maintain a...

47 CFR 76.1712 - Open video system (OVS) requests for carriage.

Code of Federal Regulations, 2013 CFR

2013-10-01

... 47 Telecommunication 4 2013-10-01 2013-10-01 false Open video system (OVS) requests for carriage... RADIO SERVICES MULTICHANNEL VIDEO AND CABLE TELEVISION SERVICE Documents to be Maintained for Inspection § 76.1712 Open video system (OVS) requests for carriage. An open video system operator shall maintain a...

47 CFR 76.1712 - Open video system (OVS) requests for carriage.

Code of Federal Regulations, 2012 CFR

2012-10-01

... 47 Telecommunication 4 2012-10-01 2012-10-01 false Open video system (OVS) requests for carriage... RADIO SERVICES MULTICHANNEL VIDEO AND CABLE TELEVISION SERVICE Documents to be Maintained for Inspection § 76.1712 Open video system (OVS) requests for carriage. An open video system operator shall maintain a...

47 CFR 76.1501 - Qualifications to be an open video system operator.

Code of Federal Regulations, 2011 CFR

2011-10-01

... 47 Telecommunication 4 2011-10-01 2011-10-01 false Qualifications to be an open video system... RADIO SERVICES MULTICHANNEL VIDEO AND CABLE TELEVISION SERVICE Open Video Systems § 76.1501 Qualifications to be an open video system operator. Any person may obtain a certification to operate an open...

47 CFR 76.1501 - Qualifications to be an open video system operator.

Code of Federal Regulations, 2014 CFR

2014-10-01

... 47 Telecommunication 4 2014-10-01 2014-10-01 false Qualifications to be an open video system... RADIO SERVICES MULTICHANNEL VIDEO AND CABLE TELEVISION SERVICE Open Video Systems § 76.1501 Qualifications to be an open video system operator. Any person may obtain a certification to operate an open...

47 CFR 76.1508 - Network non-duplication.

Code of Federal Regulations, 2014 CFR

2014-10-01

... MULTICHANNEL VIDEO AND CABLE TELEVISION SERVICE Open Video Systems § 76.1508 Network non-duplication. (a) Sections 76.92 through 76.97 shall apply to open video systems in accordance with the provisions contained... unit” shall apply to an open video system or that portion of an open video system that operates or will...

47 CFR 76.1508 - Network non-duplication.

Code of Federal Regulations, 2012 CFR

2012-10-01

... MULTICHANNEL VIDEO AND CABLE TELEVISION SERVICE Open Video Systems § 76.1508 Network non-duplication. (a) Sections 76.92 through 76.97 shall apply to open video systems in accordance with the provisions contained... unit” shall apply to an open video system or that portion of an open video system that operates or will...

47 CFR 76.1501 - Qualifications to be an open video system operator.

Code of Federal Regulations, 2012 CFR

2012-10-01

... 47 Telecommunication 4 2012-10-01 2012-10-01 false Qualifications to be an open video system... RADIO SERVICES MULTICHANNEL VIDEO AND CABLE TELEVISION SERVICE Open Video Systems § 76.1501 Qualifications to be an open video system operator. Any person may obtain a certification to operate an open...

47 CFR 76.1508 - Network non-duplication.

Code of Federal Regulations, 2013 CFR

2013-10-01

... MULTICHANNEL VIDEO AND CABLE TELEVISION SERVICE Open Video Systems § 76.1508 Network non-duplication. (a) Sections 76.92 through 76.97 shall apply to open video systems in accordance with the provisions contained... unit” shall apply to an open video system or that portion of an open video system that operates or will...

47 CFR 76.1501 - Qualifications to be an open video system operator.

Code of Federal Regulations, 2010 CFR

2010-10-01

... 47 Telecommunication 4 2010-10-01 2010-10-01 false Qualifications to be an open video system... RADIO SERVICES MULTICHANNEL VIDEO AND CABLE TELEVISION SERVICE Open Video Systems § 76.1501 Qualifications to be an open video system operator. Any person may obtain a certification to operate an open...

47 CFR 76.1501 - Qualifications to be an open video system operator.

Code of Federal Regulations, 2013 CFR

2013-10-01

... 47 Telecommunication 4 2013-10-01 2013-10-01 false Qualifications to be an open video system... RADIO SERVICES MULTICHANNEL VIDEO AND CABLE TELEVISION SERVICE Open Video Systems § 76.1501 Qualifications to be an open video system operator. Any person may obtain a certification to operate an open...

47 CFR 76.1508 - Network non-duplication.

Code of Federal Regulations, 2011 CFR

2011-10-01

... MULTICHANNEL VIDEO AND CABLE TELEVISION SERVICE Open Video Systems § 76.1508 Network non-duplication. (a) Sections 76.92 through 76.97 shall apply to open video systems in accordance with the provisions contained... unit” shall apply to an open video system or that portion of an open video system that operates or will...

Prime Contract Awards Alphabetically by Contractor, by State or Country, and Place, FY 85. Part 21 (Video Animation Systems Inc - Zytron Corporation).

DTIC Science & Technology

1985-01-01

22202-4302 ; a NAM E 0- FUND’NG ’SPONSORING Bb OFFICE SYMBOL 9 PROCUREMENT INSTRUMENT IDENTIFICATION NUMBER7 0 RGAN IZ A nON (if applicable) ’, fy Stare...CODES 18. SUBJECT TERMS (Continue on reverse if necessary and identify by block number) rIELD GROUP SUB-GROUP 9 ABSTRACT (Continue on reverse if necessary...used untl exhausted. SECURITY CLASSIFICATION OF THIS PAGE 86 3 18 135 ’- . * -’ -" LLJi -- <ZO z. I.D K Z. %.) v 0w 0- a La Z. L.-J. _ju j V) 9 , 1. caZ0 0

Early esophageal cancer detection using RF classifiers

NASA Astrophysics Data System (ADS)

Janse, Markus H. A.; van der Sommen, Fons; Zinger, Svitlana; Schoon, Erik J.; de With, Peter H. N.

2016-03-01

Esophageal cancer is one of the fastest rising forms of cancer in the Western world. Using High-Definition (HD) endoscopy, gastroenterology experts can identify esophageal cancer at an early stage. Recent research shows that early cancer can be found using a state-of-the-art computer-aided detection (CADe) system based on analyzing static HD endoscopic images. Our research aims at extending this system by applying Random Forest (RF) classification, which introduces a confidence measure for detected cancer regions. To visualize this data, we propose a novel automated annotation system, employing the unique characteristics of the previous confidence measure. This approach allows reliable modeling of multi-expert knowledge and provides essential data for real-time video processing, to enable future use of the system in a clinical setting. The performance of the CADe system is evaluated on a 39-patient dataset, containing 100 images annotated by 5 expert gastroenterologists. The proposed system reaches a precision of 75% and recall of 90%, thereby improving the state-of-the-art results by 11 and 6 percentage points, respectively.

A new automated quantification algorithm for the detection and evaluation of focal liver lesions with contrast-enhanced ultrasound.

PubMed

Gatos, Ilias; Tsantis, Stavros; Spiliopoulos, Stavros; Skouroliakou, Aikaterini; Theotokas, Ioannis; Zoumpoulis, Pavlos; Hazle, John D; Kagadis, George C

2015-07-01

Detect and classify focal liver lesions (FLLs) from contrast-enhanced ultrasound (CEUS) imaging by means of an automated quantification algorithm. The proposed algorithm employs a sophisticated segmentation method to detect and contour focal lesions from 52 CEUS video sequences (30 benign and 22 malignant). Lesion detection involves wavelet transform zero crossings utilization as an initialization step to the Markov random field model toward the lesion contour extraction. After FLL detection across frames, time intensity curve (TIC) is computed which provides the contrast agents' behavior at all vascular phases with respect to adjacent parenchyma for each patient. From each TIC, eight features were automatically calculated and employed into the support vector machines (SVMs) classification algorithm in the design of the image analysis model. With regard to FLLs detection accuracy, all lesions detected had an average overlap value of 0.89 ± 0.16 with manual segmentations for all CEUS frame-subsets included in the study. Highest classification accuracy from the SVM model was 90.3%, misdiagnosing three benign and two malignant FLLs with sensitivity and specificity values of 93.1% and 86.9%, respectively. The proposed quantification system that employs FLLs detection and classification algorithms may be of value to physicians as a second opinion tool for avoiding unnecessary invasive procedures.

Optimization of a Multi-Stage ATR System for Small Target Identification

NASA Technical Reports Server (NTRS)

Lin, Tsung-Han; Lu, Thomas; Braun, Henry; Edens, Western; Zhang, Yuhan; Chao, Tien- Hsin; Assad, Christopher; Huntsberger, Terrance

2010-01-01

An Automated Target Recognition system (ATR) was developed to locate and target small object in images and videos. The data is preprocessed and sent to a grayscale optical correlator (GOC) filter to identify possible regionsof- interest (ROIs). Next, features are extracted from ROIs based on Principal Component Analysis (PCA) and sent to neural network (NN) to be classified. The features are analyzed by the NN classifier indicating if each ROI contains the desired target or not. The ATR system was found useful in identifying small boats in open sea. However, due to "noisy background," such as weather conditions, background buildings, or water wakes, some false targets are mis-classified. Feedforward backpropagation and Radial Basis neural networks are optimized for generalization of representative features to reduce false-alarm rate. The neural networks are compared for their performance in classification accuracy, classifying time, and training time.

Seizure detection, seizure prediction, and closed-loop warning systems in epilepsy.

PubMed

Ramgopal, Sriram; Thome-Souza, Sigride; Jackson, Michele; Kadish, Navah Ester; Sánchez Fernández, Iván; Klehm, Jacquelyn; Bosl, William; Reinsberger, Claus; Schachter, Steven; Loddenkemper, Tobias

2014-08-01

Nearly one-third of patients with epilepsy continue to have seizures despite optimal medication management. Systems employed to detect seizures may have the potential to improve outcomes in these patients by allowing more tailored therapies and might, additionally, have a role in accident and SUDEP prevention. Automated seizure detection and prediction require algorithms which employ feature computation and subsequent classification. Over the last few decades, methods have been developed to detect seizures utilizing scalp and intracranial EEG, electrocardiography, accelerometry and motion sensors, electrodermal activity, and audio/video captures. To date, it is unclear which combination of detection technologies yields the best results, and approaches may ultimately need to be individualized. This review presents an overview of seizure detection and related prediction methods and discusses their potential uses in closed-loop warning systems in epilepsy. Copyright © 2014. Published by Elsevier Inc.

Evaluation of a Smartphone-based Human Activity Recognition System in a Daily Living Environment.

PubMed

Lemaire, Edward D; Tundo, Marco D; Baddour, Natalie

2015-12-11

An evaluation method that includes continuous activities in a daily-living environment was developed for Wearable Mobility Monitoring Systems (WMMS) that attempt to recognize user activities. Participants performed a pre-determined set of daily living actions within a continuous test circuit that included mobility activities (walking, standing, sitting, lying, ascending/descending stairs), daily living tasks (combing hair, brushing teeth, preparing food, eating, washing dishes), and subtle environment changes (opening doors, using an elevator, walking on inclines, traversing staircase landings, walking outdoors). To evaluate WMMS performance on this circuit, fifteen able-bodied participants completed the tasks while wearing a smartphone at their right front pelvis. The WMMS application used smartphone accelerometer and gyroscope signals to classify activity states. A gold standard comparison data set was created by video-recording each trial and manually logging activity onset times. Gold standard and WMMS data were analyzed offline. Three classification sets were calculated for each circuit: (i) mobility or immobility, ii) sit, stand, lie, or walking, and (iii) sit, stand, lie, walking, climbing stairs, or small standing movement. Sensitivities, specificities, and F-Scores for activity categorization and changes-of-state were calculated. The mobile versus immobile classification set had a sensitivity of 86.30% ± 7.2% and specificity of 98.96% ± 0.6%, while the second prediction set had a sensitivity of 88.35% ± 7.80% and specificity of 98.51% ± 0.62%. For the third classification set, sensitivity was 84.92% ± 6.38% and specificity was 98.17 ± 0.62. F1 scores for the first, second and third classification sets were 86.17 ± 6.3, 80.19 ± 6.36, and 78.42 ± 5.96, respectively. This demonstrates that WMMS performance depends on the evaluation protocol in addition to the algorithms. The demonstrated protocol can be used and tailored for evaluating human activity recognition systems in rehabilitation medicine where mobility monitoring may be beneficial in clinical decision-making.

Innovative Solution to Video Enhancement

NASA Technical Reports Server (NTRS)

2001-01-01

Through a licensing agreement, Intergraph Government Solutions adapted a technology originally developed at NASA's Marshall Space Flight Center for enhanced video imaging by developing its Video Analyst(TM) System. Marshall's scientists developed the Video Image Stabilization and Registration (VISAR) technology to help FBI agents analyze video footage of the deadly 1996 Olympic Summer Games bombing in Atlanta, Georgia. VISAR technology enhanced nighttime videotapes made with hand-held camcorders, revealing important details about the explosion. Intergraph's Video Analyst System is a simple, effective, and affordable tool for video enhancement and analysis. The benefits associated with the Video Analyst System include support of full-resolution digital video, frame-by-frame analysis, and the ability to store analog video in digital format. Up to 12 hours of digital video can be stored and maintained for reliable footage analysis. The system also includes state-of-the-art features such as stabilization, image enhancement, and convolution to help improve the visibility of subjects in the video without altering underlying footage. Adaptable to many uses, Intergraph#s Video Analyst System meets the stringent demands of the law enforcement industry in the areas of surveillance, crime scene footage, sting operations, and dash-mounted video cameras.

Thematic video indexing to support video database retrieval and query processing

NASA Astrophysics Data System (ADS)

Khoja, Shakeel A.; Hall, Wendy

1999-08-01

This paper presents a novel video database system, which caters for complex and long videos, such as documentaries, educational videos, etc. As compared to relatively structured format videos like CNN news or commercial advertisements, this database system has the capacity to work with long and unstructured videos.

A Usability Survey of a Contents-Based Video Retrieval System by Combining Digital Video and an Electronic Bulletin Board

ERIC Educational Resources Information Center

Haga, Hirohide; Kaneda, Shigeo

2005-01-01

This article describes the survey of the usability of a novel content-based video retrieval system. This system combines video streaming and an electronic bulletin board system (BBS). Comments submitted to the BBS are used to index video data. Following the development of the prototype system an experimental survey with ten subjects was performed.…

Nervous system examination on YouTube.

PubMed

Azer, Samy A; Aleshaiwi, Sarah M; Algrain, Hala A; Alkhelaif, Rana A

2012-12-22

Web 2.0 sites such as YouTube have become a useful resource for knowledge and are used by medical students as a learning resource. This study aimed at assessing videos covering the nervous system examination on YouTube. A research of YouTube was conducted from 2 November to 2 December 2011 using the following key words "nervous system examination", "nervous system clinical examination", "cranial nerves examination", "CNS examination", "examination of cerebellum", "balance and coordination examination". Only relevant videos in the English language were identified and related URL recorded. For each video, the following information was collected: title, author/s, duration, number of viewers, number of posted comments, and total number of days on YouTube. Using criteria comprising content, technical authority and pedagogy parameters, videos were rated independently by three assessors and grouped into educationally useful and non-educationally useful. A total of 2240 videos were screened; 129 were found to have relevant information to nervous system examination. Analysis revealed that 61 (47%) of the videos provided useful information on the nervous system examination. These videos scored (mean ± SD, 14.9 ± 0.2) and mainly covered examination of the whole nervous system (8 videos, 13%), cranial nerves (42 videos, 69%), upper limbs (6 videos, 10%), lower limbs (3 videos, 5%), balance and co-ordination (2 videos, 3%). The other 68 (53%) videos were not useful educationally; scoring (mean ± SD, 11.1 ± 3.0). The total viewers of all videos was 2,189,434. Useful videos were viewed by 1,050,445 viewers (48% of total viewers). The total viewership per day for useful videos was 1,794.5 and for non-useful videos 1,132.0. The differences between the three assessors were insignificant (less than 0.5 for the mean and 0.3 for the SD). Currently, YouTube provides an adequate resource for learning nervous system examination, which can be used by medical students. However, there were deficiencies in videos covering examination of the cerebellum and balance system. Useful videos can be used as learning resources to medical students.

Nervous system examination on YouTube

PubMed Central

2012-01-01

Background Web 2.0 sites such as YouTube have become a useful resource for knowledge and are used by medical students as a learning resource. This study aimed at assessing videos covering the nervous system examination on YouTube. Methods A research of YouTube was conducted from 2 November to 2 December 2011 using the following key words “nervous system examination”, “nervous system clinical examination”, “cranial nerves examination”, “CNS examination”, “examination of cerebellum”, “balance and coordination examination”. Only relevant videos in the English language were identified and related URL recorded. For each video, the following information was collected: title, author/s, duration, number of viewers, number of posted comments, and total number of days on YouTube. Using criteria comprising content, technical authority and pedagogy parameters, videos were rated independently by three assessors and grouped into educationally useful and non-educationally useful. Results A total of 2240 videos were screened; 129 were found to have relevant information to nervous system examination. Analysis revealed that 61 (47%) of the videos provided useful information on the nervous system examination. These videos scored (mean ± SD, 14.9 ± 0.2) and mainly covered examination of the whole nervous system (8 videos, 13%), cranial nerves (42 videos, 69%), upper limbs (6 videos, 10%), lower limbs (3 videos, 5%), balance and co-ordination (2 videos, 3%). The other 68 (53%) videos were not useful educationally; scoring (mean ± SD, 11.1 ± 3.0). The total viewers of all videos was 2,189,434. Useful videos were viewed by 1,050,445 viewers (48% of total viewers). The total viewership per day for useful videos was 1,794.5 and for non-useful videos 1,132.0. The differences between the three assessors were insignificant (less than 0.5 for the mean and 0.3 for the SD). Conclusions Currently, YouTube provides an adequate resource for learning nervous system examination, which can be used by medical students. However, there were deficiencies in videos covering examination of the cerebellum and balance system. Useful videos can be used as learning resources to medical students. PMID:23259768

Reliability of smartphone-based teleradiology for evaluating thoracolumbar spine fractures.

PubMed

Stahl, Ido; Dreyfuss, Daniel; Ofir, Dror; Merom, Lior; Raichel, Michael; Hous, Nir; Norman, Doron; Haddad, Elias

2017-02-01

Timely interpretation of computed tomography (CT) scans is of paramount importance in diagnosing and managing spinal column fractures, which can be devastating. Out-of-hospital, on-call spine surgeons are often asked to evaluate CT scans of patients who have sustained trauma to the thoracolumbar spine to make diagnosis and to determine the appropriate course of urgent treatment. Capturing radiographic scans and video clips from computer screens and sending them as instant messages have become common means of communication between physicians, aiding in triaging and transfer decision-making in orthopedic and neurosurgical emergencies. The present study aimed to compare the reliability of interpreting CT scans viewed by orthopedic surgeons in two ways for diagnosing, classifying, and treatment planning for thoracolumbar spine fractures: (1) captured as video clips from standard workstation-based picture archiving and communication system (PACS) and sent via a smartphone-based instant messaging application for viewing on a smartphone; and (2) viewed directly on a PACS. Reliability and agreement study. Thirty adults with thoracolumbar spine fractures who had been consecutively admitted to the Division of Orthopedic Surgery of a Level I trauma center during 2014. Intraobserver agreement. CT scans were captured by use of an iPhone 6 smartphone from a computer screen displaying PACS. Then by use of the WhatsApp instant messaging application, video clips of the scans were sent to the personal smartphones of five spine surgeons. These evaluators were asked to diagnose, classify, and determine the course of treatment for each case. Evaluation of the cases was repeated 4 weeks later, this time using the standard method of workstation-based PACS. Intraobserver agreement was interpreted based on the value of Cohen's kappa statistic. The study did not receive any outside funding. Intraobserver agreement for determining fracture level was near perfect (κ=0.94). Intraobserver agreement for AO classification, proposed treatment, neural canal penetration, and Denis classification were substantial (κ values, 0.75, 0.73, 0.71, and 0.69, respectively). Intraobserver agreement for loss of vertebral height and kyphosis were moderate (κ values, 0.55 and 0.45, respectively) CONCLUSIONS: Video clips of CT scans can be readily captured by a smartphone from a workstation-based PACS and then transmitted by use of the WhatsApp instant messaging application. Diagnosing, classifying, and proposing treatment of fractures of the thoracic and lumbar spine can be made with equal reliability by evaluating video clips of CT scans transmitted to a smartphone or by the standard method of viewing the CT scan on a workstation-based PACS. Evaluating video clips of CT scans transmitted to a smartphone is a readily accessible, simple, and inexpensive method. We believe that it can be reliably used for consultations between the emergency physicians or orthopedic or neurosurgical residents with offsite, on-call specialists. It might also enable rural orcommunity emergency department physicians to communicate more efficiently and effectively with surgeons in tertiary referral centers. Copyright © 2016 Elsevier Inc. All rights reserved.

Shape Distributions of Nonlinear Dynamical Systems for Video-Based Inference.

PubMed

Venkataraman, Vinay; Turaga, Pavan

2016-12-01

This paper presents a shape-theoretic framework for dynamical analysis of nonlinear dynamical systems which appear frequently in several video-based inference tasks. Traditional approaches to dynamical modeling have included linear and nonlinear methods with their respective drawbacks. A novel approach we propose is the use of descriptors of the shape of the dynamical attractor as a feature representation of nature of dynamics. The proposed framework has two main advantages over traditional approaches: a) representation of the dynamical system is derived directly from the observational data, without any inherent assumptions, and b) the proposed features show stability under different time-series lengths where traditional dynamical invariants fail. We illustrate our idea using nonlinear dynamical models such as Lorenz and Rossler systems, where our feature representations (shape distribution) support our hypothesis that the local shape of the reconstructed phase space can be used as a discriminative feature. Our experimental analyses on these models also indicate that the proposed framework show stability for different time-series lengths, which is useful when the available number of samples are small/variable. The specific applications of interest in this paper are: 1) activity recognition using motion capture and RGBD sensors, 2) activity quality assessment for applications in stroke rehabilitation, and 3) dynamical scene classification. We provide experimental validation through action and gesture recognition experiments on motion capture and Kinect datasets. In all these scenarios, we show experimental evidence of the favorable properties of the proposed representation.

47 CFR 76.1514 - Bundling of video and local exchange services.

Code of Federal Regulations, 2014 CFR

2014-10-01

... 47 Telecommunication 4 2014-10-01 2014-10-01 false Bundling of video and local exchange services... RADIO SERVICES MULTICHANNEL VIDEO AND CABLE TELEVISION SERVICE Open Video Systems § 76.1514 Bundling of video and local exchange services. An open video system operator may offer video and local exchange...

47 CFR 76.1514 - Bundling of video and local exchange services.

Code of Federal Regulations, 2013 CFR

2013-10-01

... 47 Telecommunication 4 2013-10-01 2013-10-01 false Bundling of video and local exchange services... RADIO SERVICES MULTICHANNEL VIDEO AND CABLE TELEVISION SERVICE Open Video Systems § 76.1514 Bundling of video and local exchange services. An open video system operator may offer video and local exchange...

47 CFR 76.1514 - Bundling of video and local exchange services.

Code of Federal Regulations, 2012 CFR

2012-10-01

... 47 Telecommunication 4 2012-10-01 2012-10-01 false Bundling of video and local exchange services... RADIO SERVICES MULTICHANNEL VIDEO AND CABLE TELEVISION SERVICE Open Video Systems § 76.1514 Bundling of video and local exchange services. An open video system operator may offer video and local exchange...

47 CFR 76.1514 - Bundling of video and local exchange services.

Code of Federal Regulations, 2010 CFR

2010-10-01

... 47 Telecommunication 4 2010-10-01 2010-10-01 false Bundling of video and local exchange services... RADIO SERVICES MULTICHANNEL VIDEO AND CABLE TELEVISION SERVICE Open Video Systems § 76.1514 Bundling of video and local exchange services. An open video system operator may offer video and local exchange...

47 CFR 76.1514 - Bundling of video and local exchange services.

Code of Federal Regulations, 2011 CFR

2011-10-01

... 47 Telecommunication 4 2011-10-01 2011-10-01 false Bundling of video and local exchange services... RADIO SERVICES MULTICHANNEL VIDEO AND CABLE TELEVISION SERVICE Open Video Systems § 76.1514 Bundling of video and local exchange services. An open video system operator may offer video and local exchange...

Rating the Accessibility of Library Tutorials from Leading Research Universities

ERIC Educational Resources Information Center

Clossen, Amanda; Proces, Paul

2017-01-01

Video and Web-based tutorials created by libraries from 71 public universities designated by the Carnegie Classification as having the Highest Research Activity (R1) were reviewed for accessibility and usability by disabled people. The results of this review indicate that a large portion of library tutorial content meets neither the minimum legal…

The Practical Use of the Educational Cable (Educable) Service.

ERIC Educational Resources Information Center

Debats, Pierre

1994-01-01

Analyzes Educable, a French program that includes a national educational film bank, a telematic server, and educational marketing. The analysis is divided into four sections: (1) teacher-users; (2) a classification of films by usage; (3) teacher use of audiovisual material; (4) the video on demand approach to a specific need. (KRN)

MATHEMATICS OF SENSING, EXPLOITATION, AND EXECUTION (MSEE) Sensing, Exploitation, and Execution (SEE) on a Foundation for Representation, Inference, and Learning

DTIC Science & Technology

2016-07-01

reconstruction, video synchronization, multi - view tracking, action recognition, reasoning with uncertainty 16. SECURITY CLASSIFICATION OF: 17...3.4.2. Human action recognition across multi - views ......................................................................................... 44 3.4.3...68 4.2.1. Multi - view Multi -object Tracking with 3D cues

Concept of Video Bookmark (Videomark) and Its Application to the Collaborative Indexing of Lecture Video in Video-Based Distance Education

ERIC Educational Resources Information Center

Haga, Hirohide

2004-01-01

This article describes the development of the video bookmark, hereinafter referred to as the videomark, and its application to the collaborative indexing of the lecture video in video-based distance education system. The combination of the videomark system with the bulletin board system (BBS), which is another network tool used for discussion, is…

High Performance Implementation of 3D Convolutional Neural Networks on a GPU.

PubMed

Lan, Qiang; Wang, Zelong; Wen, Mei; Zhang, Chunyuan; Wang, Yijie

2017-01-01

Convolutional neural networks have proven to be highly successful in applications such as image classification, object tracking, and many other tasks based on 2D inputs. Recently, researchers have started to apply convolutional neural networks to video classification, which constitutes a 3D input and requires far larger amounts of memory and much more computation. FFT based methods can reduce the amount of computation, but this generally comes at the cost of an increased memory requirement. On the other hand, the Winograd Minimal Filtering Algorithm (WMFA) can reduce the number of operations required and thus can speed up the computation, without increasing the required memory. This strategy was shown to be successful for 2D neural networks. We implement the algorithm for 3D convolutional neural networks and apply it to a popular 3D convolutional neural network which is used to classify videos and compare it to cuDNN. For our highly optimized implementation of the algorithm, we observe a twofold speedup for most of the 3D convolution layers of our test network compared to the cuDNN version.

High Performance Implementation of 3D Convolutional Neural Networks on a GPU

PubMed Central

Wang, Zelong; Wen, Mei; Zhang, Chunyuan; Wang, Yijie

2017-01-01

Convolutional neural networks have proven to be highly successful in applications such as image classification, object tracking, and many other tasks based on 2D inputs. Recently, researchers have started to apply convolutional neural networks to video classification, which constitutes a 3D input and requires far larger amounts of memory and much more computation. FFT based methods can reduce the amount of computation, but this generally comes at the cost of an increased memory requirement. On the other hand, the Winograd Minimal Filtering Algorithm (WMFA) can reduce the number of operations required and thus can speed up the computation, without increasing the required memory. This strategy was shown to be successful for 2D neural networks. We implement the algorithm for 3D convolutional neural networks and apply it to a popular 3D convolutional neural network which is used to classify videos and compare it to cuDNN. For our highly optimized implementation of the algorithm, we observe a twofold speedup for most of the 3D convolution layers of our test network compared to the cuDNN version. PMID:29250109

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

PubMed

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

2015-06-01

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 delivery applications on the Internet. Twenty-four elderly participants (70-90 years old) were recorded while watching Web-based health instruction videos involving easy and complex medical terminology. Relevant fragments of the participants' facial expressions were rated by 40 medical students for perceived level of confusion and analyzed with automatic software for facial movement recognition. A computer classification of the automatically detected facial features performed more accurately and with a higher sensitivity than the human observers (automatic detection and classification, 64% accuracy, 0.64 sensitivity; human observers, 41% accuracy, 0.43 sensitivity). A drill-down analysis of cues to confusion indicated the importance of the eye and eyebrow region. Confusion caused by misunderstanding of medical terminology is signaled by facial cues that can be automatically detected with currently available facial expression detection technology. The findings are relevant for the development of Web-based services for healthcare consumers.

Software-codec-based full motion video conferencing on the PC using visual pattern image sequence coding

NASA Astrophysics Data System (ADS)

Barnett, Barry S.; Bovik, Alan C.

1995-04-01

This paper presents a real time full motion video conferencing system based on the Visual Pattern Image Sequence Coding (VPISC) software codec. The prototype system hardware is comprised of two personal computers, two camcorders, two frame grabbers, and an ethernet connection. The prototype system software has a simple structure. It runs under the Disk Operating System, and includes a user interface, a video I/O interface, an event driven network interface, and a free running or frame synchronous video codec that also acts as the controller for the video and network interfaces. Two video coders have been tested in this system. Simple implementations of Visual Pattern Image Coding and VPISC have both proven to support full motion video conferencing with good visual quality. Future work will concentrate on expanding this prototype to support the motion compensated version of VPISC, as well as encompassing point-to-point modem I/O and multiple network protocols. The application will be ported to multiple hardware platforms and operating systems. The motivation for developing this prototype system is to demonstrate the practicality of software based real time video codecs. Furthermore, software video codecs are not only cheaper, but are more flexible system solutions because they enable different computer platforms to exchange encoded video information without requiring on-board protocol compatible video codex hardware. Software based solutions enable true low cost video conferencing that fits the `open systems' model of interoperability that is so important for building portable hardware and software applications.

Video-Based Big Data Analytics in Cyberlearning

ERIC Educational Resources Information Center

Wang, Shuangbao; Kelly, William

2017-01-01

In this paper, we present a novel system, inVideo, for video data analytics, and its use in transforming linear videos into interactive learning objects. InVideo is able to analyze video content automatically without the need for initial viewing by a human. Using a highly efficient video indexing engine we developed, the system is able to analyze…

47 CFR 76.1507 - Competitive access to satellite cable programming.

Code of Federal Regulations, 2011 CFR

2011-10-01

... RADIO SERVICES MULTICHANNEL VIDEO AND CABLE TELEVISION SERVICE Open Video Systems § 76.1507 Competitive....1000 through 76.1003 shall also apply to an operator of an open video system and its affiliate which provides video programming on its open video system, except as limited by paragraph (a) (1)-(3) of this...

47 CFR 76.1507 - Competitive access to satellite cable programming.

Code of Federal Regulations, 2014 CFR

2014-10-01

... RADIO SERVICES MULTICHANNEL VIDEO AND CABLE TELEVISION SERVICE Open Video Systems § 76.1507 Competitive....1000 through 76.1003 shall also apply to an operator of an open video system and its affiliate which provides video programming on its open video system, except as limited by paragraph (a) (1)-(3) of this...

47 CFR 76.1507 - Competitive access to satellite cable programming.

Code of Federal Regulations, 2010 CFR

2010-10-01

... RADIO SERVICES MULTICHANNEL VIDEO AND CABLE TELEVISION SERVICE Open Video Systems § 76.1507 Competitive....1000 through 76.1003 shall also apply to an operator of an open video system and its affiliate which provides video programming on its open video system, except as limited by paragraph (a) (1)-(3) of this...

47 CFR 76.1507 - Competitive access to satellite cable programming.

Code of Federal Regulations, 2012 CFR

2012-10-01

... RADIO SERVICES MULTICHANNEL VIDEO AND CABLE TELEVISION SERVICE Open Video Systems § 76.1507 Competitive....1000 through 76.1003 shall also apply to an operator of an open video system and its affiliate which provides video programming on its open video system, except as limited by paragraph (a) (1)-(3) of this...

47 CFR 76.1507 - Competitive access to satellite cable programming.

Code of Federal Regulations, 2013 CFR

2013-10-01

... RADIO SERVICES MULTICHANNEL VIDEO AND CABLE TELEVISION SERVICE Open Video Systems § 76.1507 Competitive....1000 through 76.1003 shall also apply to an operator of an open video system and its affiliate which provides video programming on its open video system, except as limited by paragraph (a) (1)-(3) of this...

BisQue: cloud-based system for management, annotation, visualization, analysis and data mining of underwater and remote sensing imagery

NASA Astrophysics Data System (ADS)

Fedorov, D.; Miller, R. J.; Kvilekval, K. G.; Doheny, B.; Sampson, S.; Manjunath, B. S.

2016-02-01

Logistical and financial limitations of underwater operations are inherent in marine science, including biodiversity observation. Imagery is a promising way to address these challenges, but the diversity of organisms thwarts simple automated analysis. Recent developments in computer vision methods, such as convolutional neural networks (CNN), are promising for automated classification and detection tasks but are typically very computationally expensive and require extensive training on large datasets. Therefore, managing and connecting distributed computation, large storage and human annotations of diverse marine datasets is crucial for effective application of these methods. BisQue is a cloud-based system for management, annotation, visualization, analysis and data mining of underwater and remote sensing imagery and associated data. Designed to hide the complexity of distributed storage, large computational clusters, diversity of data formats and inhomogeneous computational environments behind a user friendly web-based interface, BisQue is built around an idea of flexible and hierarchical annotations defined by the user. Such textual and graphical annotations can describe captured attributes and the relationships between data elements. Annotations are powerful enough to describe cells in fluorescent 4D images, fish species in underwater videos and kelp beds in aerial imagery. Presently we are developing BisQue-based analysis modules for automated identification of benthic marine organisms. Recent experiments with drop-out and CNN based classification of several thousand annotated underwater images demonstrated an overall accuracy above 70% for the 15 best performing species and above 85% for the top 5 species. Based on these promising results, we have extended bisque with a CNN-based classification system allowing continuous training on user-provided data.

47 CFR 76.1513 - Open video dispute resolution.

Code of Federal Regulations, 2012 CFR

2012-10-01

... 47 Telecommunication 4 2012-10-01 2012-10-01 false Open video dispute resolution. 76.1513 Section... MULTICHANNEL VIDEO AND CABLE TELEVISION SERVICE Open Video Systems § 76.1513 Open video dispute resolution. (a... with the following additions or changes. (b) Alternate dispute resolution. An open video system...

47 CFR 76.1513 - Open video dispute resolution.

Code of Federal Regulations, 2013 CFR

2013-10-01

... 47 Telecommunication 4 2013-10-01 2013-10-01 false Open video dispute resolution. 76.1513 Section... MULTICHANNEL VIDEO AND CABLE TELEVISION SERVICE Open Video Systems § 76.1513 Open video dispute resolution. (a... with the following additions or changes. (b) Alternate dispute resolution. An open video system...

47 CFR 76.1513 - Open video dispute resolution.

Code of Federal Regulations, 2014 CFR

2014-10-01

... 47 Telecommunication 4 2014-10-01 2014-10-01 false Open video dispute resolution. 76.1513 Section... MULTICHANNEL VIDEO AND CABLE TELEVISION SERVICE Open Video Systems § 76.1513 Open video dispute resolution. (a... with the following additions or changes. (b) Alternate dispute resolution. An open video system...

47 CFR 76.1513 - Open video dispute resolution.

Code of Federal Regulations, 2011 CFR

2011-10-01

... 47 Telecommunication 4 2011-10-01 2011-10-01 false Open video dispute resolution. 76.1513 Section... MULTICHANNEL VIDEO AND CABLE TELEVISION SERVICE Open Video Systems § 76.1513 Open video dispute resolution. (a... with the following additions or changes. (b) Alternate dispute resolution. An open video system...

47 CFR 76.1513 - Open video dispute resolution.

Code of Federal Regulations, 2010 CFR

2010-10-01

... 47 Telecommunication 4 2010-10-01 2010-10-01 false Open video dispute resolution. 76.1513 Section... MULTICHANNEL VIDEO AND CABLE TELEVISION SERVICE Open Video Systems § 76.1513 Open video dispute resolution. (a... with the following additions or changes. (b) Alternate dispute resolution. An open video system...

Crowdsourcing reproducible seizure forecasting in human and canine epilepsy.

PubMed

Brinkmann, Benjamin H; Wagenaar, Joost; Abbot, Drew; Adkins, Phillip; Bosshard, Simone C; Chen, Min; Tieng, Quang M; He, Jialune; Muñoz-Almaraz, F J; Botella-Rocamora, Paloma; Pardo, Juan; Zamora-Martinez, Francisco; Hills, Michael; Wu, Wei; Korshunova, Iryna; Cukierski, Will; Vite, Charles; Patterson, Edward E; Litt, Brian; Worrell, Gregory A

2016-06-01

SEE MORMANN AND ANDRZEJAK DOI101093/BRAIN/AWW091 FOR A SCIENTIFIC COMMENTARY ON THIS ARTICLE  : Accurate forecasting of epileptic seizures has the potential to transform clinical epilepsy care. However, progress toward reliable seizure forecasting has been hampered by lack of open access to long duration recordings with an adequate number of seizures for investigators to rigorously compare algorithms and results. A seizure forecasting competition was conducted on kaggle.com using open access chronic ambulatory intracranial electroencephalography from five canines with naturally occurring epilepsy and two humans undergoing prolonged wide bandwidth intracranial electroencephalographic monitoring. Data were provided to participants as 10-min interictal and preictal clips, with approximately half of the 60 GB data bundle labelled (interictal/preictal) for algorithm training and half unlabelled for evaluation. The contestants developed custom algorithms and uploaded their classifications (interictal/preictal) for the unknown testing data, and a randomly selected 40% of data segments were scored and results broadcasted on a public leader board. The contest ran from August to November 2014, and 654 participants submitted 17 856 classifications of the unlabelled test data. The top performing entry scored 0.84 area under the classification curve. Following the contest, additional held-out unlabelled data clips were provided to the top 10 participants and they submitted classifications for the new unseen data. The resulting area under the classification curves were well above chance forecasting, but did show a mean 6.54 ± 2.45% (min, max: 0.30, 20.2) decline in performance. The kaggle.com model using open access data and algorithms generated reproducible research that advanced seizure forecasting. The overall performance from multiple contestants on unseen data was better than a random predictor, and demonstrates the feasibility of seizure forecasting in canine and human epilepsy.media-1vid110.1093/brain/aww045_video_abstractaww045_video_abstract. © The Author (2016). Published by Oxford University Press on behalf of the Guarantors of Brain.

Incremental classification learning for anomaly detection in medical images

NASA Astrophysics Data System (ADS)

Giritharan, Balathasan; Yuan, Xiaohui; Liu, Jianguo

2009-02-01

Computer-aided diagnosis usually screens thousands of instances to find only a few positive cases that indicate probable presence of disease.The amount of patient data increases consistently all the time. In diagnosis of new instances, disagreement occurs between a CAD system and physicians, which suggests inaccurate classifiers. Intuitively, misclassified instances and the previously acquired data should be used to retrain the classifier. This, however, is very time consuming and, in some cases where dataset is too large, becomes infeasible. In addition, among the patient data, only a small percentile shows positive sign, which is known as imbalanced data.We present an incremental Support Vector Machines(SVM) as a solution for the class imbalance problem in classification of anomaly in medical images. The support vectors provide a concise representation of the distribution of the training data. Here we use bootstrapping to identify potential candidate support vectors for future iterations. Experiments were conducted using images from endoscopy videos, and the sensitivity and specificity were close to that of SVM trained using all samples available at a given incremental step with significantly improved efficiency in training the classifier.

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

NASA Astrophysics Data System (ADS)

Huber, David J.; Khosla, Deepak

2010-04-01

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

Automatic classification of the sub-techniques (gears) used in cross-country ski skating employing a mobile phone.

PubMed

Stöggl, Thomas; Holst, Anders; Jonasson, Arndt; Andersson, Erik; Wunsch, Tobias; Norström, Christer; Holmberg, Hans-Christer

2014-10-31

The purpose of the current study was to develop and validate an automatic algorithm for classification of cross-country (XC) ski-skating gears (G) using Smartphone accelerometer data. Eleven XC skiers (seven men, four women) with regional-to-international levels of performance carried out roller skiing trials on a treadmill using fixed gears (G2left, G2right, G3, G4left, G4right) and a 950-m trial using different speeds and inclines, applying gears and sides as they normally would. Gear classification by the Smartphone (on the chest) and based on video recordings were compared. Formachine-learning, a collective database was compared to individual data. The Smartphone application identified the trials with fixed gears correctly in all cases. In the 950-m trial, participants executed 140 ± 22 cycles as assessed by video analysis, with the automatic Smartphone application giving a similar value. Based on collective data, gears were identified correctly 86.0% ± 8.9% of the time, a value that rose to 90.3% ± 4.1% (P < 0.01) with machine learning from individual data. Classification was most often incorrect during transition between gears, especially to or from G3. Identification was most often correct for skiers who made relatively few transitions between gears. The accuracy of the automatic procedure for identifying G2left, G2right, G3, G4left and G4right was 96%, 90%, 81%, 88% and 94%, respectively. The algorithm identified gears correctly 100% of the time when a single gear was used and 90% of the time when different gears were employed during a variable protocol. This algorithm could be improved with respect to identification of transitions between gears or the side employed within a given gear.

A solid state video recorder as a direct replacement of a mechanically driven disc recording device in a security system

DOE Office of Scientific and Technical Information (OSTI.GOV)

Terry, P.L.

1989-01-01

Whether upgrading or developing a security system, investing in a solid state video recorder may prove to be quite prudent. Even though the initial cost of a solid state recorder may be more expensive, when comparing it to a disc recorder it is practically maintenance free. Thus, the cost effectiveness of a solid state video recorder over an extended period of time more than justifies the initial expense. This document illustrates the use of a solid state video recorder as a direct replacement. It replaces a mechanically driven disc recorder that existed in a synchronized video recording system. The originalmore » system was called the Universal Video Disc Recorder System. The modified system will now be referred to as the Solid State Video Recording System. 5 figs.« less

Strategies for Improved Interpretation of Computer-Aided Detections for CT Colonography Utilizing Distributed Human Intelligence

PubMed Central

McKenna, Matthew T.; Wang, Shijun; Nguyen, Tan B.; Burns, Joseph E.; Petrick, Nicholas; Summers, Ronald M.

2012-01-01

Computer-aided detection (CAD) systems have been shown to improve the diagnostic performance of CT colonography (CTC) in the detection of premalignant colorectal polyps. Despite the improvement, the overall system is not optimal. CAD annotations on true lesions are incorrectly dismissed, and false positives are misinterpreted as true polyps. Here, we conduct an observer performance study utilizing distributed human intelligence in the form of anonymous knowledge workers (KWs) to investigate human performance in classifying polyp candidates under different presentation strategies. We evaluated 600 polyp candidates from 50 patients, each case having at least one polyp • 6 mm, from a large database of CTC studies. Each polyp candidate was labeled independently as a true or false polyp by 20 KWs and an expert radiologist. We asked each labeler to determine whether the candidate was a true polyp after looking at a single 3D-rendered image of the candidate and after watching a video fly-around of the candidate. We found that distributed human intelligence improved significantly when presented with the additional information in the video fly-around. We noted that performance degraded with increasing interpretation time and increasing difficulty, but distributed human intelligence performed better than our CAD classifier for “easy” and “moderate” polyp candidates. Further, we observed numerous parallels between the expert radiologist and the KWs. Both showed similar improvement in classification moving from single-image to video interpretation. Additionally, difficulty estimates obtained from the KWs using an expectation maximization algorithm correlated well with the difficulty rating assigned by the expert radiologist. Our results suggest that distributed human intelligence is a powerful tool that will aid in the development of CAD for CTC. PMID:22705287

Strategies for improved interpretation of computer-aided detections for CT colonography utilizing distributed human intelligence.

PubMed

McKenna, Matthew T; Wang, Shijun; Nguyen, Tan B; Burns, Joseph E; Petrick, Nicholas; Summers, Ronald M

2012-08-01

Computer-aided detection (CAD) systems have been shown to improve the diagnostic performance of CT colonography (CTC) in the detection of premalignant colorectal polyps. Despite the improvement, the overall system is not optimal. CAD annotations on true lesions are incorrectly dismissed, and false positives are misinterpreted as true polyps. Here, we conduct an observer performance study utilizing distributed human intelligence in the form of anonymous knowledge workers (KWs) to investigate human performance in classifying polyp candidates under different presentation strategies. We evaluated 600 polyp candidates from 50 patients, each case having at least one polyp ≥6 mm, from a large database of CTC studies. Each polyp candidate was labeled independently as a true or false polyp by 20 KWs and an expert radiologist. We asked each labeler to determine whether the candidate was a true polyp after looking at a single 3D-rendered image of the candidate and after watching a video fly-around of the candidate. We found that distributed human intelligence improved significantly when presented with the additional information in the video fly-around. We noted that performance degraded with increasing interpretation time and increasing difficulty, but distributed human intelligence performed better than our CAD classifier for "easy" and "moderate" polyp candidates. Further, we observed numerous parallels between the expert radiologist and the KWs. Both showed similar improvement in classification moving from single-image to video interpretation. Additionally, difficulty estimates obtained from the KWs using an expectation maximization algorithm correlated well with the difficulty rating assigned by the expert radiologist. Our results suggest that distributed human intelligence is a powerful tool that will aid in the development of CAD for CTC. Copyright © 2012. Published by Elsevier B.V.

Recent advances in the treatment of air leaks.

PubMed

Cerfolio, Robert James

2005-07-01

Alveolar-pleural fistulas (air leaks) are an extremely common clinical problem and remain the most common complication after elective pulmonary resection and video-assisted procedures. The decision making process used to manage air leaks and chest tubes that control them has been, until very recently, based on opinions and training preferences as opposed to facts derived from randomized clinical trials. Recently, several prospective randomized trials have studied air leaks. An objective, reproducible classification system has also been designed and clinically validated to help study air leaks. This system and these studies have shown that water seal is superior to wall suction to help stop most leaks. Even in patients with a pneumothorax and an air leak, water seal is safe and best; however, if a patient has a large leak (greater than an expiratory 3 on the classification system) or experiences subcutaneous emphysema or an expanding pneumothorax that causes hypoxia, then some suction (-10 cm of water) should be applied to the chest tubes. Air leaks were a poorly understood yet extremely common clinical problem that had never been scientifically studied. Over the past 5 years, prospective randomized studies have shown that water seal is the best setting for chest tubes and that a pneumothorax is not a contraindication to leaving tubes on seal. Further studies are needed to investigate the ideal management of alveolar-pleural fistulas (air leaks) in different clinical scenarios besides those that occur postoperatively.

Cell dynamic morphology classification using deep convolutional neural networks.

PubMed

Li, Heng; Pang, Fengqian; Shi, Yonggang; Liu, Zhiwen

2018-05-15

Cell morphology is often used as a proxy measurement of cell status to understand cell physiology. Hence, interpretation of cell dynamic morphology is a meaningful task in biomedical research. Inspired by the recent success of deep learning, we here explore the application of convolutional neural networks (CNNs) to cell dynamic morphology classification. An innovative strategy for the implementation of CNNs is introduced in this study. Mouse lymphocytes were collected to observe the dynamic morphology, and two datasets were thus set up to investigate the performances of CNNs. Considering the installation of deep learning, the classification problem was simplified from video data to image data, and was then solved by CNNs in a self-taught manner with the generated image data. CNNs were separately performed in three installation scenarios and compared with existing methods. Experimental results demonstrated the potential of CNNs in cell dynamic morphology classification, and validated the effectiveness of the proposed strategy. CNNs were successfully applied to the classification problem, and outperformed the existing methods in the classification accuracy. For the installation of CNNs, transfer learning was proved to be a promising scheme. © 2018 International Society for Advancement of Cytometry. © 2018 International Society for Advancement of Cytometry.

Exercise intensity levels in children with cerebral palsy while playing with an active video game console.

PubMed

Robert, Maxime; Ballaz, Laurent; Hart, Raphael; Lemay, Martin

2013-08-01

Children with cerebral palsy (CP) are prone to secondary complications related to physical inactivity and poor cardiorespiratory capacity. This problem could be greatly attenuated through the use of video games that incorporate physical activity for 2 reasons: Video games already represent an important component of leisure time in younger people, and such games can lead to a high level of exercise intensity in people who are healthy. The study objective was to evaluate exercise intensity in children with spastic diplegic CP and children who were typically developing while playing with an active video game console. This was a cross-sectional study. Ten children (7-12 years old) with spastic diplegic CP (Gross Motor Function Classification System level I or II) and 10 children who were age matched and typically developing were evaluated in a movement analysis laboratory. Four games were played with the active video game console (jogging, bicycling, snowboarding, and skiing) for 40 minutes. Heart rate was recorded during the entire playing period with a heart rate belt monitor. Exercise intensity was defined as the percentage of heart rate reserve (HRR). In addition, lower extremity motion analysis was carried out during the final minute of the playing period for the jogging and bicycling games. No difference between groups was observed for any variables. A main effect of games was observed for the amount of time spent at an intensity greater than 40% of HRR. Specifically, more than 50% of the playing time for the jogging game and more than 30% of the playing time for the bicycling game were spent at an intensity greater than 40% of HRR. In addition, the jogging game produced a larger range of motion than the bicycling game. A limitation of this study was the relatively small and heterogeneous sample. For all 4 games, similar exercise intensity levels were observed for children who were typically developing and children with CP, suggesting that children with CP could obtain exercise-related benefits similar to those obtained by children without CP while playing with an active video game console.

Architectural Considerations for Highly Scalable Computing to Support On-demand Video Analytics

DTIC Science & Technology

2017-04-19

enforcement . The system was tested in the wild using video files as well as a commercial Video Management System supporting more than 100 surveillance...research were used to implement a distributed on-demand video analytics system that was prototyped for the use of forensics investigators in law...cameras as video sources. The architectural considerations of this system are presented. Issues to be reckoned with in implementing a scalable

Advanced Video Data-Acquisition System For Flight Research

NASA Technical Reports Server (NTRS)

Miller, Geoffrey; Richwine, David M.; Hass, Neal E.

1996-01-01

Advanced video data-acquisition system (AVDAS) developed to satisfy variety of requirements for in-flight video documentation. Requirements range from providing images for visualization of airflows around fighter airplanes at high angles of attack to obtaining safety-of-flight documentation. F/A-18 AVDAS takes advantage of very capable systems like NITE Hawk forward-looking infrared (FLIR) pod and recent video developments like miniature charge-couple-device (CCD) color video cameras and other flight-qualified video hardware.

Automated Visual Event Detection, Tracking, and Data Management System for Cabled- Observatory Video

NASA Astrophysics Data System (ADS)

Edgington, D. R.; Cline, D. E.; Schlining, B.; Raymond, E.

2008-12-01

Ocean observatories and underwater video surveys have the potential to unlock important discoveries with new and existing camera systems. Yet the burden of video management and analysis often requires reducing the amount of video recorded through time-lapse video or similar methods. It's unknown how many digitized video data sets exist in the oceanographic community, but we suspect that many remain under analyzed due to lack of good tools or human resources to analyze the video. To help address this problem, the Automated Visual Event Detection (AVED) software and The Video Annotation and Reference System (VARS) have been under development at MBARI. For detecting interesting events in the video, the AVED software has been developed over the last 5 years. AVED is based on a neuromorphic-selective attention algorithm, modeled on the human vision system. Frames are decomposed into specific feature maps that are combined into a unique saliency map. This saliency map is then scanned to determine the most salient locations. The candidate salient locations are then segmented from the scene using algorithms suitable for the low, non-uniform light and marine snow typical of deep underwater video. For managing the AVED descriptions of the video, the VARS system provides an interface and database for describing, viewing, and cataloging the video. VARS was developed by the MBARI for annotating deep-sea video data and is currently being used to describe over 3000 dives by our remotely operated vehicles (ROV), making it well suited to this deepwater observatory application with only a few modifications. To meet the compute and data intensive job of video processing, a distributed heterogeneous network of computers is managed using the Condor workload management system. This system manages data storage, video transcoding, and AVED processing. Looking to the future, we see high-speed networks and Grid technology as an important element in addressing the problem of processing and accessing large video data sets.

Seafloor monitoring west of Helgoland (German Bight, North Sea) using the acoustic ground discrimination system RoxAnn

NASA Astrophysics Data System (ADS)

Hass, H. Christian; Mielck, Finn; Fiorentino, Dario; Papenmeier, Svenja; Holler, Peter; Bartholomä, Alexander

2017-04-01

Marine habitats of shelf seas are in constant dynamic change and therefore need regular assessment particularly in areas of special interest. In this study, the single-beam acoustic ground discrimination system RoxAnn served to assess seafloor hardness and roughness, and combine these parameters into one variable expressed as RGB (red green blue) color code followed by k-means fuzzy cluster analysis (FCA). The data were collected at a monitoring site west of the island of Helgoland (German Bight, SE North Sea) in the course of four surveys between September 2011 and November 2014. The study area has complex characteristics varying from outcropping bedrock to sandy and muddy sectors with mostly gradual transitions. RoxAnn data enabled to discriminate all seafloor types that were suggested by ground-truth information (seafloor samples, video). The area appears to be quite stable overall; sediment import (including fluid mud) was detected only from the NW. Although hard substrates (boulders, bedrock) are clearly identified, the signal can be modified by inclination and biocover. Manually, six RoxAnn zones were identified; for the FCA, only three classes are suggested. The latter classification based on `hard' boundaries would suffice for stakeholder issues, but the former classification based on `soft' boundaries is preferred to meet state-of-the-art scientific objectives.

Design of video interface conversion system based on FPGA

NASA Astrophysics Data System (ADS)

Zhao, Heng; Wang, Xiang-jun

2014-11-01

This paper presents a FPGA based video interface conversion system that enables the inter-conversion between digital and analog video. Cyclone IV series EP4CE22F17C chip from Altera Corporation is used as the main video processing chip, and single-chip is used as the information interaction control unit between FPGA and PC. The system is able to encode/decode messages from the PC. Technologies including video decoding/encoding circuits, bus communication protocol, data stream de-interleaving and de-interlacing, color space conversion and the Camera Link timing generator module of FPGA are introduced. The system converts Composite Video Broadcast Signal (CVBS) from the CCD camera into Low Voltage Differential Signaling (LVDS), which will be collected by the video processing unit with Camera Link interface. The processed video signals will then be inputted to system output board and displayed on the monitor.The current experiment shows that it can achieve high-quality video conversion with minimum board size.

Effects of video-game ownership on young boys' academic and behavioral functioning: a randomized, controlled study.

PubMed

Weis, Robert; Cerankosky, Brittany C

2010-04-01

Young boys who did not own video games were promised a video-game system and child-appropriate games in exchange for participating in an "ongoing study of child development." After baseline assessment of boys' academic achievement and parent- and teacher-reported behavior, boys were randomly assigned to receive the video-game system immediately or to receive the video-game system after follow-up assessment, 4 months later. Boys who received the system immediately spent more time playing video games and less time engaged in after-school academic activities than comparison children. Boys who received the system immediately also had lower reading and writing scores and greater teacher-reported academic problems at follow-up than comparison children. Amount of video-game play mediated the relationship between video-game ownership and academic outcomes. Results provide experimental evidence that video games may displace after-school activities that have educational value and may interfere with the development of reading and writing skills in some children.

47 CFR 76.1511 - Fees.

Code of Federal Regulations, 2012 CFR

2012-10-01

... FEDERAL COMMUNICATIONS COMMISSION (CONTINUED) BROADCAST RADIO SERVICES MULTICHANNEL VIDEO AND CABLE TELEVISION SERVICE Open Video Systems § 76.1511 Fees. An open video system operator may be subject to the... open video system operator or its affiliates, including all revenues received from subscribers and all...

47 CFR 76.1511 - Fees.

Code of Federal Regulations, 2011 CFR

2011-10-01

... FEDERAL COMMUNICATIONS COMMISSION (CONTINUED) BROADCAST RADIO SERVICES MULTICHANNEL VIDEO AND CABLE TELEVISION SERVICE Open Video Systems § 76.1511 Fees. An open video system operator may be subject to the... open video system operator or its affiliates, including all revenues received from subscribers and all...

47 CFR 76.1511 - Fees.

Code of Federal Regulations, 2014 CFR

2014-10-01

... FEDERAL COMMUNICATIONS COMMISSION (CONTINUED) BROADCAST RADIO SERVICES MULTICHANNEL VIDEO AND CABLE TELEVISION SERVICE Open Video Systems § 76.1511 Fees. An open video system operator may be subject to the... open video system operator or its affiliates, including all revenues received from subscribers and all...

47 CFR 76.1511 - Fees.

Code of Federal Regulations, 2010 CFR

2010-10-01

... FEDERAL COMMUNICATIONS COMMISSION (CONTINUED) BROADCAST RADIO SERVICES MULTICHANNEL VIDEO AND CABLE TELEVISION SERVICE Open Video Systems § 76.1511 Fees. An open video system operator may be subject to the... open video system operator or its affiliates, including all revenues received from subscribers and all...

47 CFR 76.1511 - Fees.

Code of Federal Regulations, 2013 CFR

2013-10-01

... FEDERAL COMMUNICATIONS COMMISSION (CONTINUED) BROADCAST RADIO SERVICES MULTICHANNEL VIDEO AND CABLE TELEVISION SERVICE Open Video Systems § 76.1511 Fees. An open video system operator may be subject to the... open video system operator or its affiliates, including all revenues received from subscribers and all...

76 FR 23624 - In the Matter of Certain Video Game Systems and Wireless Controllers and Components Thereof...

Federal Register 2010, 2011, 2012, 2013, 2014

2011-04-27

... INTERNATIONAL TRADE COMMISSION [Inv. No. 337-TA-770] In the Matter of Certain Video Game Systems... importation of certain video game systems and wireless controllers and components thereof by reason of... sale within the United States after importation of certain video game systems and wireless controllers...

47 CFR 76.1509 - Syndicated program exclusivity.

Code of Federal Regulations, 2012 CFR

2012-10-01

... MULTICHANNEL VIDEO AND CABLE TELEVISION SERVICE Open Video Systems § 76.1509 Syndicated program exclusivity. (a) Sections 76.151 through 76.163 shall apply to open video systems in accordance with the provisions... to an open video system. (c) Any provision of § 76.155 that refers to a “cable system operator” or...

47 CFR 76.1509 - Syndicated program exclusivity.

Code of Federal Regulations, 2014 CFR

2014-10-01

... MULTICHANNEL VIDEO AND CABLE TELEVISION SERVICE Open Video Systems § 76.1509 Syndicated program exclusivity. (a) Sections 76.151 through 76.163 shall apply to open video systems in accordance with the provisions... to an open video system. (c) Any provision of § 76.155 that refers to a “cable system operator” or...

47 CFR 76.1509 - Syndicated program exclusivity.

Code of Federal Regulations, 2010 CFR

2010-10-01

... MULTICHANNEL VIDEO AND CABLE TELEVISION SERVICE Open Video Systems § 76.1509 Syndicated program exclusivity. (a) Sections 76.151 through 76.163 shall apply to open video systems in accordance with the provisions... to an open video system. (c) Any provision of § 76.155 that refers to a “cable system operator” or...

47 CFR 76.1509 - Syndicated program exclusivity.

Code of Federal Regulations, 2013 CFR

2013-10-01

... MULTICHANNEL VIDEO AND CABLE TELEVISION SERVICE Open Video Systems § 76.1509 Syndicated program exclusivity. (a) Sections 76.151 through 76.163 shall apply to open video systems in accordance with the provisions... to an open video system. (c) Any provision of § 76.155 that refers to a “cable system operator” or...

47 CFR 76.1509 - Syndicated program exclusivity.

Code of Federal Regulations, 2011 CFR

2011-10-01

... MULTICHANNEL VIDEO AND CABLE TELEVISION SERVICE Open Video Systems § 76.1509 Syndicated program exclusivity. (a) Sections 76.151 through 76.163 shall apply to open video systems in accordance with the provisions... to an open video system. (c) Any provision of § 76.155 that refers to a “cable system operator” or...

ASSESSING THE ACCURACY OF SATELLITE-DERIVED LAND COVER CLASSIFICATION USING HISTORICAL AERIAL PHOTOGRAPHY DIGITAL ORTHOPHOTO QUADRANGLES, AND AIRBORNE VIDEO DATA

EPA Science Inventory

As the rapidly growing archives of satellite remote sensing imagery now span decades'worth of data, there is increasing interest in the study of long-term regional land cover change across multiple image dates. In most cases, however, temporally coincident ground sampled data are...

ASSESSING THE ACCURACY OF SATELLITE-DERIVED LAND COVER CLASSIFICATION USING HISTORICAL AERIAL PHOTOGRAPHY, DIGITAL ORTHOPHOTO QUADDRANGLES, AND AIRBORNE VIDEO DATA

EPA Science Inventory

As the rapidly growing archives of satellite remote sensing imagery now span decades'worth of data, there is increasing interest in the study of long-term regional land cover change across multiple image dates. In most cases, however, temporally coincident ground sampled data are...

ASSESSING THE ACCURACY OF SATELLITE-DERIVED LAND COVER CLASSIFICATION USING HISTORICAL AERIAL PHOTOGRAPHY, DIGITAL ORTHOPHOTO QUADRANGLES, AND AIRBORNE VIDEO DATA

EPA Science Inventory

As the rapidly growing archives of satellite remote sensing imagery now span decades' worth of data, there is increasing interest in the study of long-term regional land cover change across multiple image dates. In most cases, however, temporally coincident ground sampled data ar...

A micro-fluidic treadmill for observing suspended plankton in the lab

NASA Astrophysics Data System (ADS)

Jaffe, J. S.; Laxton, B.; Garwood, J. C.; Franks, P. J. S.; Roberts, P. L.

2016-02-01

A significant obstacle to laboratory studies of interactions between small organisms ( mm) and their fluid environment is our ability to obtain high-resolution images while allowing freedom of motion. This is because as the organisms sink, they will often move out of the field of view of the observation system. One solution to this problem is to impose a water circulation pattern that preserves their location relative to the camera system while imaging the organisms away from the glass walls. To accomplish this we have designed and created a plankton treadmill. Our computer-controlled system consists of a digital video camera attached to a macro or microscope and a micro-fluidic pump whose flow is regulated to maintain a suspended organism's position relative to the field of view. Organisms are detected and tracked in real time in the video frames, allowing a control algorithm to compensate for any vertical movement by adjusting the flow. The flow control can be manually adjusted using on-screen controls, semi-automatically adjusted to allow the user to select a particular organism to be tracked or fully automatic through the use of classification and tracking algorithms. Experiments with a simple cm-sized cuvette and a number of organisms that are both positively and negatively buoyant have demonstrated the success of the system in permitting longer observation times than would be possible in the absence of a controlled-flow environment. The subjects were observed using a new dual-view, holographic imaging system that provides 3-dimensional microscopic observations with relatively isotropic resolution. We will present the system design, construction, the control algorithm, and some images obtained with the holographic system, demonstrating its effectiveness. Small particles seeded into the flow clearly show the 3D flow fields around the subjects as they freely sink or swim.

Development of a web-based video management and application processing system

NASA Astrophysics Data System (ADS)

Chan, Shermann S.; Wu, Yi; Li, Qing; Zhuang, Yueting

2001-07-01

How to facilitate efficient video manipulation and access in a web-based environment is becoming a popular trend for video applications. In this paper, we present a web-oriented video management and application processing system, based on our previous work on multimedia database and content-based retrieval. In particular, we extend the VideoMAP architecture with specific web-oriented mechanisms, which include: (1) Concurrency control facilities for the editing of video data among different types of users, such as Video Administrator, Video Producer, Video Editor, and Video Query Client; different users are assigned various priority levels for different operations on the database. (2) Versatile video retrieval mechanism which employs a hybrid approach by integrating a query-based (database) mechanism with content- based retrieval (CBR) functions; its specific language (CAROL/ST with CBR) supports spatio-temporal semantics of video objects, and also offers an improved mechanism to describe visual content of videos by content-based analysis method. (3) Query profiling database which records the `histories' of various clients' query activities; such profiles can be used to provide the default query template when a similar query is encountered by the same kind of users. An experimental prototype system is being developed based on the existing VideoMAP prototype system, using Java and VC++ on the PC platform.

Task-oriented situation recognition

NASA Astrophysics Data System (ADS)

Bauer, Alexander; Fischer, Yvonne

2010-04-01

From the advances in computer vision methods for the detection, tracking and recognition of objects in video streams, new opportunities for video surveillance arise: In the future, automated video surveillance systems will be able to detect critical situations early enough to enable an operator to take preventive actions, instead of using video material merely for forensic investigations. However, problems such as limited computational resources, privacy regulations and a constant change in potential threads have to be addressed by a practical automated video surveillance system. In this paper, we show how these problems can be addressed using a task-oriented approach. The system architecture of the task-oriented video surveillance system NEST and an algorithm for the detection of abnormal behavior as part of the system are presented and illustrated for the surveillance of guests inside a video-monitored building.

Gender classification from video under challenging operating conditions

NASA Astrophysics Data System (ADS)

Mendoza-Schrock, Olga; Dong, Guozhu

2014-06-01

The literature is abundant with papers on gender classification research. However the majority of such research is based on the assumption that there is enough resolution so that the subject's face can be resolved. Hence the majority of the research is actually in the face recognition and facial feature area. A gap exists for gender classification under challenging operating conditions—different seasonal conditions, different clothing, etc.—and when the subject's face cannot be resolved due to lack of resolution. The Seasonal Weather and Gender (SWAG) Database is a novel database that contains subjects walking through a scene under operating conditions that span a calendar year. This paper exploits a subset of that database—the SWAG One dataset—using data mining techniques, traditional classifiers (ex. Naïve Bayes, Support Vector Machine, etc.) and traditional (canny edge detection, etc.) and non-traditional (height/width ratios, etc.) feature extractors to achieve high correct gender classification rates (greater than 85%). Another novelty includes exploiting frame differentials.

Multilocation Video Conference By Optical Fiber

NASA Astrophysics Data System (ADS)

Gray, Donald J.

1982-10-01

An experimental system that permits interconnection of many offices in a single video conference is described. Video images transmitted to conference participants are selected by the conference chairman and switched by a microprocessor-controlled video switch. Speakers can, at their choice, transmit their own images or images of graphics they wish to display. Users are connected to the Switching Center by optical fiber subscriber loops that carry analog video, digitized telephone, data and signaling. The same system also provides user-selectable distribution of video program and video library material. Experience in the operation of the conference system is discussed.

Video copy protection and detection framework (VPD) for e-learning systems

NASA Astrophysics Data System (ADS)

ZandI, Babak; Doustarmoghaddam, Danial; Pour, Mahsa R.

2013-03-01

This Article reviews and compares the copyright issues related to the digital video files, which can be categorized as contended based and Digital watermarking copy Detection. Then we describe how to protect a digital video by using a special Video data hiding method and algorithm. We also discuss how to detect the copy right of the file, Based on expounding Direction of the technology of the video copy detection, and Combining with the own research results, brings forward a new video protection and copy detection approach in terms of plagiarism and e-learning systems using the video data hiding technology. Finally we introduce a framework for Video protection and detection in e-learning systems (VPD Framework).

47 CFR 76.1512 - Programming information.

Code of Federal Regulations, 2010 CFR

2010-10-01

... MULTICHANNEL VIDEO AND CABLE TELEVISION SERVICE Open Video Systems § 76.1512 Programming information. (a) An open video system operator shall not unreasonably discriminate in favor of itself or its affiliates... for the purpose of selecting programming on the open video system, or in the way such material or...

47 CFR 76.1505 - Public, educational and governmental access.

Code of Federal Regulations, 2011 CFR

2011-10-01

... SERVICES MULTICHANNEL VIDEO AND CABLE TELEVISION SERVICE Open Video Systems § 76.1505 Public, educational and governmental access. (a) An open video system operator shall be subject to public, educational and... video system operator must ensure that all subscribers receive any public, educational and governmental...

47 CFR 76.1512 - Programming information.

Code of Federal Regulations, 2013 CFR

2013-10-01

... MULTICHANNEL VIDEO AND CABLE TELEVISION SERVICE Open Video Systems § 76.1512 Programming information. (a) An open video system operator shall not unreasonably discriminate in favor of itself or its affiliates... for the purpose of selecting programming on the open video system, or in the way such material or...

47 CFR 76.1505 - Public, educational and governmental access.

Code of Federal Regulations, 2014 CFR

2014-10-01

... SERVICES MULTICHANNEL VIDEO AND CABLE TELEVISION SERVICE Open Video Systems § 76.1505 Public, educational and governmental access. (a) An open video system operator shall be subject to public, educational and... video system operator must ensure that all subscribers receive any public, educational and governmental...

47 CFR 76.1505 - Public, educational and governmental access.

Code of Federal Regulations, 2012 CFR

2012-10-01

... SERVICES MULTICHANNEL VIDEO AND CABLE TELEVISION SERVICE Open Video Systems § 76.1505 Public, educational and governmental access. (a) An open video system operator shall be subject to public, educational and... video system operator must ensure that all subscribers receive any public, educational and governmental...

47 CFR 76.1505 - Public, educational and governmental access.

Code of Federal Regulations, 2010 CFR

2010-10-01

... SERVICES MULTICHANNEL VIDEO AND CABLE TELEVISION SERVICE Open Video Systems § 76.1505 Public, educational and governmental access. (a) An open video system operator shall be subject to public, educational and... video system operator must ensure that all subscribers receive any public, educational and governmental...

47 CFR 76.1505 - Public, educational and governmental access.

Code of Federal Regulations, 2013 CFR

2013-10-01

... SERVICES MULTICHANNEL VIDEO AND CABLE TELEVISION SERVICE Open Video Systems § 76.1505 Public, educational and governmental access. (a) An open video system operator shall be subject to public, educational and... video system operator must ensure that all subscribers receive any public, educational and governmental...

47 CFR 76.1512 - Programming information.

Code of Federal Regulations, 2012 CFR

2012-10-01

... MULTICHANNEL VIDEO AND CABLE TELEVISION SERVICE Open Video Systems § 76.1512 Programming information. (a) An open video system operator shall not unreasonably discriminate in favor of itself or its affiliates... for the purpose of selecting programming on the open video system, or in the way such material or...

47 CFR 76.1512 - Programming information.

Code of Federal Regulations, 2014 CFR

2014-10-01

... MULTICHANNEL VIDEO AND CABLE TELEVISION SERVICE Open Video Systems § 76.1512 Programming information. (a) An open video system operator shall not unreasonably discriminate in favor of itself or its affiliates... for the purpose of selecting programming on the open video system, or in the way such material or...

47 CFR 63.02 - Exemptions for extensions of lines and for systems for the delivery of video programming.

Code of Federal Regulations, 2011 CFR

2011-10-01

... systems for the delivery of video programming. 63.02 Section 63.02 Telecommunication FEDERAL... systems for the delivery of video programming. (a) Any common carrier is exempt from the requirements of... with respect to the establishment or operation of a system for the delivery of video programming. [64...

47 CFR 63.02 - Exemptions for extensions of lines and for systems for the delivery of video programming.

Code of Federal Regulations, 2010 CFR

2010-10-01

... systems for the delivery of video programming. 63.02 Section 63.02 Telecommunication FEDERAL... systems for the delivery of video programming. (a) Any common carrier is exempt from the requirements of... with respect to the establishment or operation of a system for the delivery of video programming. [64...

47 CFR 63.02 - Exemptions for extensions of lines and for systems for the delivery of video programming.

Code of Federal Regulations, 2013 CFR

2013-10-01

... systems for the delivery of video programming. 63.02 Section 63.02 Telecommunication FEDERAL... systems for the delivery of video programming. (a) Any common carrier is exempt from the requirements of... with respect to the establishment or operation of a system for the delivery of video programming. [64...

47 CFR 63.02 - Exemptions for extensions of lines and for systems for the delivery of video programming.

Code of Federal Regulations, 2014 CFR

2014-10-01

... systems for the delivery of video programming. 63.02 Section 63.02 Telecommunication FEDERAL... systems for the delivery of video programming. (a) Any common carrier is exempt from the requirements of... with respect to the establishment or operation of a system for the delivery of video programming. [64...

47 CFR 63.02 - Exemptions for extensions of lines and for systems for the delivery of video programming.

Code of Federal Regulations, 2012 CFR

2012-10-01

... systems for the delivery of video programming. 63.02 Section 63.02 Telecommunication FEDERAL... systems for the delivery of video programming. (a) Any common carrier is exempt from the requirements of... with respect to the establishment or operation of a system for the delivery of video programming. [64...

Detection of Abnormal Events via Optical Flow Feature Analysis

PubMed Central

Wang, Tian; Snoussi, Hichem

2015-01-01

In this paper, a novel algorithm is proposed to detect abnormal events in video streams. The algorithm is based on the histogram of the optical flow orientation descriptor and the classification method. The details of the histogram of the optical flow orientation descriptor are illustrated for describing movement information of the global video frame or foreground frame. By combining one-class support vector machine and kernel principal component analysis methods, the abnormal events in the current frame can be detected after a learning period characterizing normal behaviors. The difference abnormal detection results are analyzed and explained. The proposed detection method is tested on benchmark datasets, then the experimental results show the effectiveness of the algorithm. PMID:25811227

Adaptive Gaussian mixture models for pre-screening in GPR data

NASA Astrophysics Data System (ADS)

Torrione, Peter; Morton, Kenneth, Jr.; Besaw, Lance E.

2011-06-01

Due to the large amount of data generated by vehicle-mounted ground penetrating radar (GPR) antennae arrays, advanced feature extraction and classification can only be performed on a small subset of data during real-time operation. As a result, most GPR based landmine detection systems implement "pre-screening" algorithms to processes all of the data generated by the antennae array and identify locations with anomalous signatures for more advanced processing. These pre-screening algorithms must be computationally efficient and obtain high probability of detection, but can permit a false alarm rate which might be higher than the total system requirements. Many approaches to prescreening have previously been proposed, including linear prediction coefficients, the LMS algorithm, and CFAR-based approaches. Similar pre-screening techniques have also been developed in the field of video processing to identify anomalous behavior or anomalous objects. One such algorithm, an online k-means approximation to an adaptive Gaussian mixture model (GMM), is particularly well-suited to application for pre-screening in GPR data due to its computational efficiency, non-linear nature, and relevance of the logic underlying the algorithm to GPR processing. In this work we explore the application of an adaptive GMM-based approach for anomaly detection from the video processing literature to pre-screening in GPR data. Results with the ARA Nemesis landmine detection system demonstrate significant pre-screening performance improvements compared to alternative approaches, and indicate that the proposed algorithm is a complimentary technique to existing methods.

Neutral face classification using personalized appearance models for fast and robust emotion detection.

PubMed

Chiranjeevi, Pojala; Gopalakrishnan, Viswanath; Moogi, Pratibha

2015-09-01

Facial expression recognition is one of the open problems in computer vision. Robust neutral face recognition in real time is a major challenge for various supervised learning-based facial expression recognition methods. This is due to the fact that supervised methods cannot accommodate all appearance variability across the faces with respect to race, pose, lighting, facial biases, and so on, in the limited amount of training data. Moreover, processing each and every frame to classify emotions is not required, as user stays neutral for majority of the time in usual applications like video chat or photo album/web browsing. Detecting neutral state at an early stage, thereby bypassing those frames from emotion classification would save the computational power. In this paper, we propose a light-weight neutral versus emotion classification engine, which acts as a pre-processer to the traditional supervised emotion classification approaches. It dynamically learns neutral appearance at key emotion (KE) points using a statistical texture model, constructed by a set of reference neutral frames for each user. The proposed method is made robust to various types of user head motions by accounting for affine distortions based on a statistical texture model. Robustness to dynamic shift of KE points is achieved by evaluating the similarities on a subset of neighborhood patches around each KE point using the prior information regarding the directionality of specific facial action units acting on the respective KE point. The proposed method, as a result, improves emotion recognition (ER) accuracy and simultaneously reduces computational complexity of the ER system, as validated on multiple databases.

Deep Learning Applications for Predicting Pharmacological Properties of Drugs and Drug Repurposing Using Transcriptomic Data.

PubMed

Aliper, Alexander; Plis, Sergey; Artemov, Artem; Ulloa, Alvaro; Mamoshina, Polina; Zhavoronkov, Alex

2016-07-05

Deep learning is rapidly advancing many areas of science and technology with multiple success stories in image, text, voice and video recognition, robotics, and autonomous driving. In this paper we demonstrate how deep neural networks (DNN) trained on large transcriptional response data sets can classify various drugs to therapeutic categories solely based on their transcriptional profiles. We used the perturbation samples of 678 drugs across A549, MCF-7, and PC-3 cell lines from the LINCS Project and linked those to 12 therapeutic use categories derived from MeSH. To train the DNN, we utilized both gene level transcriptomic data and transcriptomic data processed using a pathway activation scoring algorithm, for a pooled data set of samples perturbed with different concentrations of the drug for 6 and 24 hours. In both pathway and gene level classification, DNN achieved high classification accuracy and convincingly outperformed the support vector machine (SVM) model on every multiclass classification problem, however, models based on pathway level data performed significantly better. For the first time we demonstrate a deep learning neural net trained on transcriptomic data to recognize pharmacological properties of multiple drugs across different biological systems and conditions. We also propose using deep neural net confusion matrices for drug repositioning. This work is a proof of principle for applying deep learning to drug discovery and development.

Deep learning applications for predicting pharmacological properties of drugs and drug repurposing using transcriptomic data

PubMed Central

Aliper, Alexander; Plis, Sergey; Artemov, Artem; Ulloa, Alvaro; Mamoshina, Polina; Zhavoronkov, Alex

2016-01-01

Deep learning is rapidly advancing many areas of science and technology with multiple success stories in image, text, voice and video recognition, robotics and autonomous driving. In this paper we demonstrate how deep neural networks (DNN) trained on large transcriptional response data sets can classify various drugs to therapeutic categories solely based on their transcriptional profiles. We used the perturbation samples of 678 drugs across A549, MCF‐7 and PC‐3 cell lines from the LINCS project and linked those to 12 therapeutic use categories derived from MeSH. To train the DNN, we utilized both gene level transcriptomic data and transcriptomic data processed using a pathway activation scoring algorithm, for a pooled dataset of samples perturbed with different concentrations of the drug for 6 and 24 hours. In both gene and pathway level classification, DNN convincingly outperformed support vector machine (SVM) model on every multiclass classification problem, however, models based on a pathway level classification perform better. For the first time we demonstrate a deep learning neural net trained on transcriptomic data to recognize pharmacological properties of multiple drugs across different biological systems and conditions. We also propose using deep neural net confusion matrices for drug repositioning. This work is a proof of principle for applying deep learning to drug discovery and development. PMID:27200455

77 FR 75659 - Certain Video Analytics Software, Systems, Components Thereof, and Products Containing Same...

Federal Register 2010, 2011, 2012, 2013, 2014

2012-12-21

... INTERNATIONAL TRADE COMMISSION [Investigation No. 337-TA-852] Certain Video Analytics Software..., 2012, based on a complaint filed by ObjectVideo, Inc. (``ObjectVideo'') of Reston, Virginia. 77 FR... United States after importation of certain video analytics software systems, components thereof, and...

Maximizing Resource Utilization in Video Streaming Systems

ERIC Educational Resources Information Center

Alsmirat, Mohammad Abdullah

2013-01-01

Video streaming has recently grown dramatically in popularity over the Internet, Cable TV, and wire-less networks. Because of the resource demanding nature of video streaming applications, maximizing resource utilization in any video streaming system is a key factor to increase the scalability and decrease the cost of the system. Resources to…

Recognizing human activities using appearance metric feature and kinematics feature

NASA Astrophysics Data System (ADS)

Qian, Huimin; Zhou, Jun; Lu, Xinbiao; Wu, Xinye

2017-05-01

The problem of automatically recognizing human activities from videos through the fusion of the two most important cues, appearance metric feature and kinematics feature, is considered. And a system of two-dimensional (2-D) Poisson equations is introduced to extract the more discriminative appearance metric feature. Specifically, the moving human blobs are first detected out from the video by background subtraction technique to form a binary image sequence, from which the appearance feature designated as the motion accumulation image and the kinematics feature termed as centroid instantaneous velocity are extracted. Second, 2-D discrete Poisson equations are employed to reinterpret the motion accumulation image to produce a more differentiated Poisson silhouette image, from which the appearance feature vector is created through the dimension reduction technique called bidirectional 2-D principal component analysis, considering the balance between classification accuracy and time consumption. Finally, a cascaded classifier based on the nearest neighbor classifier and two directed acyclic graph support vector machine classifiers, integrated with the fusion of the appearance feature vector and centroid instantaneous velocity vector, is applied to recognize the human activities. Experimental results on the open databases and a homemade one confirm the recognition performance of the proposed algorithm.

Bilayer segmentation of webcam videos using tree-based classifiers.

PubMed

Yin, Pei; Criminisi, Antonio; Winn, John; Essa, Irfan

2011-01-01

This paper presents an automatic segmentation algorithm for video frames captured by a (monocular) webcam that closely approximates depth segmentation from a stereo camera. The frames are segmented into foreground and background layers that comprise a subject (participant) and other objects and individuals. The algorithm produces correct segmentations even in the presence of large background motion with a nearly stationary foreground. This research makes three key contributions: First, we introduce a novel motion representation, referred to as "motons," inspired by research in object recognition. Second, we propose estimating the segmentation likelihood from the spatial context of motion. The estimation is efficiently learned by random forests. Third, we introduce a general taxonomy of tree-based classifiers that facilitates both theoretical and experimental comparisons of several known classification algorithms and generates new ones. In our bilayer segmentation algorithm, diverse visual cues such as motion, motion context, color, contrast, and spatial priors are fused by means of a conditional random field (CRF) model. Segmentation is then achieved by binary min-cut. Experiments on many sequences of our videochat application demonstrate that our algorithm, which requires no initialization, is effective in a variety of scenes, and the segmentation results are comparable to those obtained by stereo systems.

Monitoring Hitting Load in Tennis Using Inertial Sensors and Machine Learning.

PubMed

Whiteside, David; Cant, Olivia; Connolly, Molly; Reid, Machar

2017-10-01

Quantifying external workload is fundamental to training prescription in sport. In tennis, global positioning data are imprecise and fail to capture hitting loads. The current gold standard (manual notation) is time intensive and often not possible given players' heavy travel schedules. To develop an automated stroke-classification system to help quantify hitting load in tennis. Nineteen athletes wore an inertial measurement unit (IMU) on their wrist during 66 video-recorded training sessions. Video footage was manually notated such that known shot type (serve, rally forehand, slice forehand, forehand volley, rally backhand, slice backhand, backhand volley, smash, or false positive) was associated with the corresponding IMU data for 28,582 shots. Six types of machine-learning models were then constructed to classify true shot type from the IMU signals. Across 10-fold cross-validation, a cubic-kernel support vector machine classified binned shots (overhead, forehand, or backhand) with an accuracy of 97.4%. A second cubic-kernel support vector machine achieved 93.2% accuracy when classifying all 9 shot types. With a view to monitoring external load, the combination of miniature inertial sensors and machine learning offers a practical and automated method of quantifying shot counts and discriminating shot types in elite tennis players.

Games people play: How video games improve probabilistic learning.

PubMed

Schenk, Sabrina; Lech, Robert K; Suchan, Boris

2017-09-29

Recent research suggests that video game playing is associated with many cognitive benefits. However, little is known about the neural mechanisms mediating such effects, especially with regard to probabilistic categorization learning, which is a widely unexplored area in gaming research. Therefore, the present study aimed to investigate the neural correlates of probabilistic classification learning in video gamers in comparison to non-gamers. Subjects were scanned in a 3T magnetic resonance imaging (MRI) scanner while performing a modified version of the weather prediction task. Behavioral data yielded evidence for better categorization performance of video gamers, particularly under conditions characterized by stronger uncertainty. Furthermore, a post-experimental questionnaire showed that video gamers had acquired higher declarative knowledge about the card combinations and the related weather outcomes. Functional imaging data revealed for video gamers stronger activation clusters in the hippocampus, the precuneus, the cingulate gyrus and the middle temporal gyrus as well as in occipital visual areas and in areas related to attentional processes. All these areas are connected with each other and represent critical nodes for semantic memory, visual imagery and cognitive control. Apart from this, and in line with previous studies, both groups showed activation in brain areas that are related to attention and executive functions as well as in the basal ganglia and in memory-associated regions of the medial temporal lobe. These results suggest that playing video games might enhance the usage of declarative knowledge as well as hippocampal involvement and enhances overall learning performance during probabilistic learning. In contrast to non-gamers, video gamers showed better categorization performance, independently of the uncertainty of the condition. Copyright © 2017 Elsevier B.V. All rights reserved.

Medical Education Videos for the World: An Analysis of Viewing Patterns for a YouTube Channel.

PubMed

Tackett, Sean; Slinn, Kyle; Marshall, Tanner; Gaglani, Shiv; Waldman, Vincent; Desai, Rishi

2018-01-02

Medical education videos can enhance learning and easily integrate into common instructional methods. YouTube permits worldwide access to high-quality medical education videos; however, no studies have described the reach of medical education videos on YouTube or what topics are preferred. One year of YouTube analytics data (February 1, 2016, to January 31, 2017) was collected for a medical education-focused channel called Osmosis. Created December 20, 2015, the channel had 189 disease-focused videos by January 2017. Viewer and subscriber data were analyzed according to the World Bank's 4 income and 7 region classifications. Topic viewing was analyzed according to income level. The channel had accumulated 105,117 subscribers and 5,226,405 views for 20,153,093 minutes (38.3 years) from viewers located in 213/218 (97.7%) World Bank economies. While the number of videos increased 4.8 fold from February 2016 to January 2017, monthly views increased 50 fold and subscribers increased 117 fold. Low or middle income countries generated 2.2 million (42%) views and 53,000 (50%) subscribers, with similar view proportions across income level during the 12 months. A plurality of views (1.5 million, 29%) came from North America; Sub-Saharan Africa had the lowest number (150,000, 2.9%). Topic viewing generally corresponded to population health statistics. Medical education content on YouTube can immediately and consistently reach a global viewership with relevant content. Educators may consider posting videos to YouTube to reach a broad audience. Future work should seek to optimize assessment of learning and investigate how videos may affect patients.

Deriving video content type from HEVC bitstream semantics

NASA Astrophysics Data System (ADS)

Nightingale, James; Wang, Qi; Grecos, Christos; Goma, Sergio R.

2014-05-01

As network service providers seek to improve customer satisfaction and retention levels, they are increasingly moving from traditional quality of service (QoS) driven delivery models to customer-centred quality of experience (QoE) delivery models. QoS models only consider metrics derived from the network however, QoE models also consider metrics derived from within the video sequence itself. Various spatial and temporal characteristics of a video sequence have been proposed, both individually and in combination, to derive methods of classifying video content either on a continuous scale or as a set of discrete classes. QoE models can be divided into three broad categories, full reference, reduced reference and no-reference models. Due to the need to have the original video available at the client for comparison, full reference metrics are of limited practical value in adaptive real-time video applications. Reduced reference metrics often require metadata to be transmitted with the bitstream, while no-reference metrics typically operate in the decompressed domain at the client side and require significant processing to extract spatial and temporal features. This paper proposes a heuristic, no-reference approach to video content classification which is specific to HEVC encoded bitstreams. The HEVC encoder already makes use of spatial characteristics to determine partitioning of coding units and temporal characteristics to determine the splitting of prediction units. We derive a function which approximates the spatio-temporal characteristics of the video sequence by using the weighted averages of the depth at which the coding unit quadtree is split and the prediction mode decision made by the encoder to estimate spatial and temporal characteristics respectively. Since the video content type of a sequence is determined by using high level information parsed from the video stream, spatio-temporal characteristics are identified without the need for full decoding and can be used in a timely manner to aid decision making in QoE oriented adaptive real time streaming.

Application of robust face recognition in video surveillance systems

NASA Astrophysics Data System (ADS)

Zhang, De-xin; An, Peng; Zhang, Hao-xiang

2018-03-01

In this paper, we propose a video searching system that utilizes face recognition as searching indexing feature. As the applications of video cameras have great increase in recent years, face recognition makes a perfect fit for searching targeted individuals within the vast amount of video data. However, the performance of such searching depends on the quality of face images recorded in the video signals. Since the surveillance video cameras record videos without fixed postures for the object, face occlusion is very common in everyday video. The proposed system builds a model for occluded faces using fuzzy principal component analysis (FPCA), and reconstructs the human faces with the available information. Experimental results show that the system has very high efficiency in processing the real life videos, and it is very robust to various kinds of face occlusions. Hence it can relieve people reviewers from the front of the monitors and greatly enhances the efficiency as well. The proposed system has been installed and applied in various environments and has already demonstrated its power by helping solving real cases.

75 FR 68379 - In the Matter of: Certain Video Game Systems and Controllers; Notice of Investigation

Federal Register 2010, 2011, 2012, 2013, 2014

2010-11-05

... INTERNATIONAL TRADE COMMISSION [Inv. No. 337-TA-743] In the Matter of: Certain Video Game Systems... within the United States after importation of certain video game systems and controllers by reason of... certain video game systems and controllers that infringe one or more of claims 16, 27-32, 44, 57, 68, 81...

SeeCoast: persistent surveillance and automated scene understanding for ports and coastal areas

NASA Astrophysics Data System (ADS)

Rhodes, Bradley J.; Bomberger, Neil A.; Freyman, Todd M.; Kreamer, William; Kirschner, Linda; L'Italien, Adam C.; Mungovan, Wendy; Stauffer, Chris; Stolzar, Lauren; Waxman, Allen M.; Seibert, Michael

2007-04-01

SeeCoast is a prototype US Coast Guard port and coastal area surveillance system that aims to reduce operator workload while maintaining optimal domain awareness by shifting their focus from having to detect events to being able to analyze and act upon the knowledge derived from automatically detected anomalous activities. The automated scene understanding capability provided by the baseline SeeCoast system (as currently installed at the Joint Harbor Operations Center at Hampton Roads, VA) results from the integration of several components. Machine vision technology processes the real-time video streams provided by USCG cameras to generate vessel track and classification (based on vessel length) information. A multi-INT fusion component generates a single, coherent track picture by combining information available from the video processor with that from surface surveillance radars and AIS reports. Based on this track picture, vessel activity is analyzed by SeeCoast to detect user-defined unsafe, illegal, and threatening vessel activities using a rule-based pattern recognizer and to detect anomalous vessel activities on the basis of automatically learned behavior normalcy models. Operators can optionally guide the learning system in the form of examples and counter-examples of activities of interest, and refine the performance of the learning system by confirming alerts or indicating examples of false alarms. The fused track picture also provides a basis for automated control and tasking of cameras to detect vessels in motion. Real-time visualization combining the products of all SeeCoast components in a common operating picture is provided by a thin web-based client.

Image/video understanding systems based on network-symbolic models

NASA Astrophysics Data System (ADS)

Kuvich, Gary

2004-03-01

Vision is a part of a larger information system that converts visual information into knowledge structures. These structures drive vision process, resolve ambiguity and uncertainty via feedback projections, and provide image understanding that is an interpretation of visual information in terms of such knowledge models. Computer simulation models are built on the basis of graphs/networks. The ability of human brain to emulate similar graph/network models is found. Symbols, predicates and grammars naturally emerge in such networks, and logic is simply a way of restructuring such models. Brain analyzes an image as a graph-type relational structure created via multilevel hierarchical compression of visual information. Primary areas provide active fusion of image features on a spatial grid-like structure, where nodes are cortical columns. Spatial logic and topology naturally present in such structures. Mid-level vision processes like perceptual grouping, separation of figure from ground, are special kinds of network transformations. They convert primary image structure into the set of more abstract ones, which represent objects and visual scene, making them easy for analysis by higher-level knowledge structures. Higher-level vision phenomena are results of such analysis. Composition of network-symbolic models combines learning, classification, and analogy together with higher-level model-based reasoning into a single framework, and it works similar to frames and agents. Computational intelligence methods transform images into model-based knowledge representation. Based on such principles, an Image/Video Understanding system can convert images into the knowledge models, and resolve uncertainty and ambiguity. This allows creating intelligent computer vision systems for design and manufacturing.

Converting laserdisc video to digital video: a demonstration project using brain animations.

PubMed

Jao, C S; Hier, D B; Brint, S U

1995-01-01

Interactive laserdiscs are of limited value in large group learning situations due to the expense of establishing multiple workstations. The authors implemented an alternative to laserdisc video by using indexed digital video combined with an expert system. High-quality video was captured from a laserdisc player and combined with waveform audio into an audio-video-interleave (AVI) file format in the Microsoft Video-for-Windows environment (Microsoft Corp., Seattle, WA). With the use of an expert system, a knowledge-based computer program provided random access to these indexed AVI files. The program can be played on any multimedia computer without the need for laserdiscs. This system offers a high level of interactive video without the overhead and cost of a laserdisc player.

Video Monitoring a Simulation-Based Quality Improvement Program in Bihar, India.

PubMed

Dyer, Jessica; Spindler, Hilary; Christmas, Amelia; Shah, Malay Bharat; Morgan, Melissa; Cohen, Susanna R; Sterne, Jason; Mahapatra, Tanmay; Walker, Dilys

2018-04-01

Simulation-based training has become an accepted clinical training andragogy in high-resource settings with its use increasing in low-resource settings. Video recordings of simulated scenarios are commonly used by facilitators. Beyond using the videos during debrief sessions, researchers can also analyze the simulation videos to quantify technical and nontechnical skills during simulated scenarios over time. Little is known about the feasibility and use of large-scale systems to video record and analyze simulation and debriefing data for monitoring and evaluation in low-resource settings. This manuscript describes the process of designing and implementing a large-scale video monitoring system. Mentees and Mentors were consented and all simulations and debriefs conducted at 320 Primary Health Centers (PHCs) were video recorded. The system design, number of video recordings, and inter-rater reliability of the coded videos were assessed. The final dataset included a total of 11,278 videos. Overall, a total of 2,124 simulation videos were coded and 183 (12%) were blindly double-coded. For the double-coded sample, the average inter-rater reliability (IRR) scores were 80% for nontechnical skills, and 94% for clinical technical skills. Among 4,450 long debrief videos received, 216 were selected for coding and all were double-coded. Data quality of simulation videos was found to be very good in terms of recorded instances of "unable to see" and "unable to hear" in Phases 1 and 2. This study demonstrates that video monitoring systems can be effectively implemented at scale in resource limited settings. Further, video monitoring systems can play several vital roles within program implementation, including monitoring and evaluation, provision of actionable feedback to program implementers, and assurance of program fidelity.

Utilization of KSC Present Broadband Communications Data System for Digital Video Services

NASA Technical Reports Server (NTRS)

Andrawis, Alfred S.

2002-01-01

This report covers a visibility study of utilizing present KSC broadband communications data system (BCDS) for digital video services. Digital video services include compressed digital TV delivery and video-on-demand. Furthermore, the study examines the possibility of providing interactive video on demand to desktop personal computers via KSC computer network.

Utilization of KSC Present Broadband Communications Data System For Digital Video Services

NASA Technical Reports Server (NTRS)

Andrawis, Alfred S.

2001-01-01

This report covers a visibility study of utilizing present KSC broadband communications data system (BCDS) for digital video services. Digital video services include compressed digital TV delivery and video-on-demand. Furthermore, the study examines the possibility of providing interactive video on demand to desktop personal computers via KSC computer network.

47 CFR 76.1510 - Application of certain Title VI provisions.

Code of Federal Regulations, 2010 CFR

2010-10-01

... SERVICES MULTICHANNEL VIDEO AND CABLE TELEVISION SERVICE Open Video Systems § 76.1510 Application of certain Title VI provisions. The following sections within part 76 shall also apply to open video systems..., that these sections shall apply to open video systems only to the extent that they do not conflict with...

47 CFR 76.1510 - Application of certain Title VI provisions.

Code of Federal Regulations, 2011 CFR

2011-10-01

... SERVICES MULTICHANNEL VIDEO AND CABLE TELEVISION SERVICE Open Video Systems § 76.1510 Application of certain Title VI provisions. The following sections within part 76 shall also apply to open video systems..., that these sections shall apply to open video systems only to the extent that they do not conflict with...

47 CFR 76.1510 - Application of certain Title VI provisions.

Code of Federal Regulations, 2013 CFR

2013-10-01

... SERVICES MULTICHANNEL VIDEO AND CABLE TELEVISION SERVICE Open Video Systems § 76.1510 Application of certain Title VI provisions. The following sections within part 76 shall also apply to open video systems..., that these sections shall apply to open video systems only to the extent that they do not conflict with...

47 CFR 76.1510 - Application of certain Title VI provisions.

Code of Federal Regulations, 2014 CFR

2014-10-01

... SERVICES MULTICHANNEL VIDEO AND CABLE TELEVISION SERVICE Open Video Systems § 76.1510 Application of certain Title VI provisions. The following sections within part 76 shall also apply to open video systems..., that these sections shall apply to open video systems only to the extent that they do not conflict with...

47 CFR 76.1510 - Application of certain Title VI provisions.

Code of Federal Regulations, 2012 CFR

2012-10-01

... SERVICES MULTICHANNEL VIDEO AND CABLE TELEVISION SERVICE Open Video Systems § 76.1510 Application of certain Title VI provisions. The following sections within part 76 shall also apply to open video systems..., that these sections shall apply to open video systems only to the extent that they do not conflict with...

A new visual navigation system for exploring biomedical Open Educational Resource (OER) videos

PubMed Central

Zhao, Baoquan; Xu, Songhua; Lin, Shujin; Luo, Xiaonan; Duan, Lian

2016-01-01

Objective Biomedical videos as open educational resources (OERs) are increasingly proliferating on the Internet. Unfortunately, seeking personally valuable content from among the vast corpus of quality yet diverse OER videos is nontrivial due to limitations of today’s keyword- and content-based video retrieval techniques. To address this need, this study introduces a novel visual navigation system that facilitates users’ information seeking from biomedical OER videos in mass quantity by interactively offering visual and textual navigational clues that are both semantically revealing and user-friendly. Materials and Methods The authors collected and processed around 25 000 YouTube videos, which collectively last for a total length of about 4000 h, in the broad field of biomedical sciences for our experiment. For each video, its semantic clues are first extracted automatically through computationally analyzing audio and visual signals, as well as text either accompanying or embedded in the video. These extracted clues are subsequently stored in a metadata database and indexed by a high-performance text search engine. During the online retrieval stage, the system renders video search results as dynamic web pages using a JavaScript library that allows users to interactively and intuitively explore video content both efficiently and effectively. Results The authors produced a prototype implementation of the proposed system, which is publicly accessible at https://patentq.njit.edu/oer. To examine the overall advantage of the proposed system for exploring biomedical OER videos, the authors further conducted a user study of a modest scale. The study results encouragingly demonstrate the functional effectiveness and user-friendliness of the new system for facilitating information seeking from and content exploration among massive biomedical OER videos. Conclusion Using the proposed tool, users can efficiently and effectively find videos of interest, precisely locate video segments delivering personally valuable information, as well as intuitively and conveniently preview essential content of a single or a collection of videos. PMID:26335986

Combating Tobacco Use in the United States Army

DTIC Science & Technology

2010-04-01

videogame , tobacco use, military. 16. SECURITY CLASSIFICATION OF: 17. LIMITATION OF ABSTRACT 18. NUMBER OF PAGES 19a. NAME OF RESPONSIBLE... videogame is theory-guided and uses animations, videos and interactive activities to provide facts about smoking and tobacco use, as well as provides...prevention and cessation interactive multimedia tool ( videogame ) among active Army personnel stationed at Fort Hood, Texas. Body Throughout the

Vehicle Surveillance with a Generic, Adaptive, 3D Vehicle Model.

PubMed

Leotta, Matthew J; Mundy, Joseph L

2011-07-01

In automated surveillance, one is often interested in tracking road vehicles, measuring their shape in 3D world space, and determining vehicle classification. To address these tasks simultaneously, an effective approach is the constrained alignment of a prior model of 3D vehicle shape to images. Previous 3D vehicle models are either generic but overly simple or rigid and overly complex. Rigid models represent exactly one vehicle design, so a large collection is needed. A single generic model can deform to a wide variety of shapes, but those shapes have been far too primitive. This paper uses a generic 3D vehicle model that deforms to match a wide variety of passenger vehicles. It is adjustable in complexity between the two extremes. The model is aligned to images by predicting and matching image intensity edges. Novel algorithms are presented for fitting models to multiple still images and simultaneous tracking while estimating shape in video. Experiments compare the proposed model to simple generic models in accuracy and reliability of 3D shape recovery from images and tracking in video. Standard techniques for classification are also used to compare the models. The proposed model outperforms the existing simple models at each task.

A Standard-Compliant Virtual Meeting System with Active Video Object Tracking

NASA Astrophysics Data System (ADS)

Lin, Chia-Wen; Chang, Yao-Jen; Wang, Chih-Ming; Chen, Yung-Chang; Sun, Ming-Ting

2002-12-01

This paper presents an H.323 standard compliant virtual video conferencing system. The proposed system not only serves as a multipoint control unit (MCU) for multipoint connection but also provides a gateway function between the H.323 LAN (local-area network) and the H.324 WAN (wide-area network) users. The proposed virtual video conferencing system provides user-friendly object compositing and manipulation features including 2D video object scaling, repositioning, rotation, and dynamic bit-allocation in a 3D virtual environment. A reliable, and accurate scheme based on background image mosaics is proposed for real-time extracting and tracking foreground video objects from the video captured with an active camera. Chroma-key insertion is used to facilitate video objects extraction and manipulation. We have implemented a prototype of the virtual conference system with an integrated graphical user interface to demonstrate the feasibility of the proposed methods.

An affordable wearable video system for emergency response training

NASA Astrophysics Data System (ADS)

King-Smith, Deen; Mikkilineni, Aravind; Ebert, David; Collins, Timothy; Delp, Edward J.

2009-02-01

Many emergency response units are currently faced with restrictive budgets that prohibit their use of advanced technology-based training solutions. Our work focuses on creating an affordable, mobile, state-of-the-art emergency response training solution through the integration of low-cost, commercially available products. The system we have developed consists of tracking, audio, and video capability, coupled with other sensors that can all be viewed through a unified visualization system. In this paper we focus on the video sub-system which helps provide real time tracking and video feeds from the training environment through a system of wearable and stationary cameras. These two camera systems interface with a management system that handles storage and indexing of the video during and after training exercises. The wearable systems enable the command center to have live video and tracking information for each trainee in the exercise. The stationary camera systems provide a fixed point of reference for viewing action during the exercise and consist of a small Linux based portable computer and mountable camera. The video management system consists of a server and database which work in tandem with a visualization application to provide real-time and after action review capability to the training system.

Digital Video Over Space Systems and Networks

NASA Technical Reports Server (NTRS)

Grubbs, Rodney

2010-01-01

This slide presentation reviews the use of digital video with space systems and networks. The earliest use of video was the use of film precluding live viewing, which gave way to live television from space. This has given way to digital video using internet protocol for transmission. This has provided for many improvements with new challenges. Some of these ehallenges are reviewed. The change to digital video transmitted over space systems can provide incredible imagery, however the process must be viewed as an entire system, rather than piece-meal.

Evaluation of a video image detection system : final report.

DOT National Transportation Integrated Search

1994-05-01

A video image detection system (VIDS) is an advanced wide-area traffic monitoring system : that processes input from a video camera. The Autoscope VIDS coupled with an information : management system was selected as the monitoring device because test...

Crystal surface analysis using matrix textural features classified by a probabilistic neural network

NASA Astrophysics Data System (ADS)

Sawyer, Curry R.; Quach, Viet; Nason, Donald; van den Berg, Lodewijk

1991-12-01

A system is under development in which surface quality of a growing bulk mercuric iodide crystal is monitored by video camera at regular intervals for early detection of growth irregularities. Mercuric iodide single crystals are employed in radiation detectors. A microcomputer system is used for image capture and processing. The digitized image is divided into multiple overlapping sub-images and features are extracted from each sub-image based on statistical measures of the gray tone distribution, according to the method of Haralick. Twenty parameters are derived from each sub-image and presented to a probabilistic neural network (PNN) for classification. This number of parameters was found to be optimal for the system. The PNN is a hierarchical, feed-forward network that can be rapidly reconfigured as additional training data become available. Training data is gathered by reviewing digital images of many crystals during their growth cycle and compiling two sets of images, those with and without irregularities.

Secure video communications system

DOEpatents

Smith, Robert L.

1991-01-01

A secure video communications system having at least one command network formed by a combination of subsystems. The combination of subsystems to include a video subsystem, an audio subsystem, a communications subsystem, and a control subsystem. The video communications system to be window driven and mouse operated, and having the ability to allow for secure point-to-point real-time teleconferencing.

47 CFR 76.1500 - Definitions.

Code of Federal Regulations, 2011 CFR

2011-10-01

... Telecommunication FEDERAL COMMUNICATIONS COMMISSION (CONTINUED) BROADCAST RADIO SERVICES MULTICHANNEL VIDEO AND CABLE TELEVISION SERVICE Open Video Systems § 76.1500 Definitions. (a) Open video system. A facility... that is designed to provide cable service which includes video programming and which is provided to...

47 CFR 76.1500 - Definitions.

Code of Federal Regulations, 2013 CFR

2013-10-01

... Telecommunication FEDERAL COMMUNICATIONS COMMISSION (CONTINUED) BROADCAST RADIO SERVICES MULTICHANNEL VIDEO AND CABLE TELEVISION SERVICE Open Video Systems § 76.1500 Definitions. (a) Open video system. A facility... that is designed to provide cable service which includes video programming and which is provided to...

A Classification System to Guide Physical Therapy Management in Huntington Disease: A Case Series.

PubMed

Fritz, Nora E; Busse, Monica; Jones, Karen; Khalil, Hanan; Quinn, Lori

2017-07-01

Individuals with Huntington disease (HD), a rare neurological disease, experience impairments in mobility and cognition throughout their disease course. The Medical Research Council framework provides a schema that can be applied to the development and evaluation of complex interventions, such as those provided by physical therapists. Treatment-based classifications, based on expert consensus and available literature, are helpful in guiding physical therapy management across the stages of HD. Such classifications also contribute to the development and further evaluation of well-defined complex interventions in this highly variable and complex neurodegenerative disease. The purpose of this case series was to illustrate the use of these classifications in the management of 2 individuals with late-stage HD. Two females, 40 and 55 years of age, with late-stage HD participated in this case series. Both experienced progressive declines in ambulatory function and balance as well as falls or fear of falling. Both individuals received daily care in the home for activities of daily living. Physical therapy Treatment-Based Classifications for HD guided the interventions and outcomes. Eight weeks of in-home balance training, strength training, task-specific practice of functional activities including transfers and walking tasks, and family/carer education were provided. Both individuals demonstrated improvements that met or exceeded the established minimal detectible change values for gait speed and Timed Up and Go performance. Both also demonstrated improvements on Berg Balance Scale and Physical Performance Test performance, with 1 of the 2 individuals exceeding the established minimal detectible changes for both tests. Reductions in fall risk were evident in both cases. These cases provide proof-of-principle to support use of treatment-based classifications for physical therapy management in individuals with HD. Traditional classification of early-, mid-, and late-stage disease progression may not reflect patients' true capabilities; those with late-stage HD may be as responsive to interventions as those at an earlier disease stage.Video Abstract available for additional insights from the authors (see Supplemental Digital Content 1, available at: http://links.lww.com/JNPT/A172).

Innovative Video Diagnostic Equipment for Material Science

NASA Technical Reports Server (NTRS)

Capuano, G.; Titomanlio, D.; Soellner, W.; Seidel, A.

2012-01-01

Materials science experiments under microgravity increasingly rely on advanced optical systems to determine the physical properties of the samples under investigation. This includes video systems with high spatial and temporal resolution. The acquisition, handling, storage and transmission to ground of the resulting video data are very challenging. Since the available downlink data rate is limited, the capability to compress the video data significantly without compromising the data quality is essential. We report on the development of a Digital Video System (DVS) for EML (Electro Magnetic Levitator) which provides real-time video acquisition, high compression using advanced Wavelet algorithms, storage and transmission of a continuous flow of video with different characteristics in terms of image dimensions and frame rates. The DVS is able to operate with the latest generation of high-performance cameras acquiring high resolution video images up to 4Mpixels@60 fps or high frame rate video images up to about 1000 fps@512x512pixels.

Bimanual Capacity of Children With Cerebral Palsy: Intra- and Interrater Reliability of a Revised Edition of the Bimanual Fine Motor Function Classification.

PubMed

Elvrum, Ann-Kristin G; Beckung, Eva; Sæther, Rannei; Lydersen, Stian; Vik, Torstein; Himmelmann, Kate

2017-08-01

To develop a revised edition of the Bimanual Fine Motor Function (BFMF 2), as a classification of fine motor capacity in children with cerebral palsy (CP), and establish intra- and interrater reliability of this edition. The content of the original BFMF was discussed by an expert panel, resulting in a revised edition comprising the original description of the classification levels, but in addition including figures with specific explanatory text. Four professionals classified fine motor function of 79 children (3-17 years; 45 boys) who represented all subtypes of CP and Manual Ability Classification levels (I-V). Intra- and inter-rater reliability was assessed using overall intra-class correlation coefficient (ICC), and Cohen's quadratic weighted kappa. The overall ICC was 0.86. Cohen's weighted kappa indicated high intra-rater (к w : >0.90) and inter-rater (к w : >0.85) reliability. The revised BFMF 2 had high intra- and interrater reliability. The classification levels could be determined from short video recordings (<5 minutes), using the figures and precise descriptions of the fine motor function levels included in the BFMF 2. Thus, the BFMF 2 may be a feasible and useful classification of fine motor capacity both in research and in clinical practice.

Investigation of correlation classification techniques

NASA Technical Reports Server (NTRS)

Haskell, R. E.

1975-01-01

A two-step classification algorithm for processing multispectral scanner data was developed and tested. The first step is a single pass clustering algorithm that assigns each pixel, based on its spectral signature, to a particular cluster. The output of that step is a cluster tape in which a single integer is associated with each pixel. The cluster tape is used as the input to the second step, where ground truth information is used to classify each cluster using an iterative method of potentials. Once the clusters have been assigned to classes the cluster tape is read pixel-by-pixel and an output tape is produced in which each pixel is assigned to its proper class. In addition to the digital classification programs, a method of using correlation clustering to process multispectral scanner data in real time by means of an interactive color video display is also described.

The National Capital Region closed circuit television video interoperability project.

PubMed

Contestabile, John; Patrone, David; Babin, Steven

2016-01-01

The National Capital Region (NCR) includes many government jurisdictions and agencies using different closed circuit TV (CCTV) cameras and video management software. Because these agencies often must work together to respond to emergencies and events, a means of providing interoperability for CCTV video is critically needed. Video data from different CCTV systems that are not inherently interoperable is represented in the "data layer." An "integration layer" ingests the data layer source video and normalizes the different video formats. It then aggregates and distributes this video to a "presentation layer" where it can be viewed by almost any application used by other agencies and without any proprietary software. A native mobile video viewing application is also developed that uses the presentation layer to provide video to different kinds of smartphones. The NCR includes Washington, DC, and surrounding counties in Maryland and Virginia. The video sharing architecture allows one agency to see another agency's video in their native viewing application without the need to purchase new CCTV software or systems. A native smartphone application was also developed to enable them to share video via mobile devices even when they use different video management systems. A video sharing architecture has been developed for the NCR that creates an interoperable environment for sharing CCTV video in an efficient and cost effective manner. In addition, it provides the desired capability of sharing video via a native mobile application.

Distributed Patterns of Reactivation Predict Vividness of Recollection.

PubMed

St-Laurent, Marie; Abdi, Hervé; Buchsbaum, Bradley R

2015-10-01

According to the principle of reactivation, memory retrieval evokes patterns of brain activity that resemble those instantiated when an event was first experienced. Intuitively, one would expect neural reactivation to contribute to recollection (i.e., the vivid impression of reliving past events), but evidence of a direct relationship between the subjective quality of recollection and multiregional reactivation of item-specific neural patterns is lacking. The current study assessed this relationship using fMRI to measure brain activity as participants viewed and mentally replayed a set of short videos. We used multivoxel pattern analysis to train a classifier to identify individual videos based on brain activity evoked during perception and tested how accurately the classifier could distinguish among videos during mental replay. Classification accuracy correlated positively with memory vividness, indicating that the specificity of multivariate brain patterns observed during memory retrieval was related to the subjective quality of a memory. In addition, we identified a set of brain regions whose univariate activity during retrieval predicted both memory vividness and the strength of the classifier's prediction irrespective of the particular video that was retrieved. Our results establish distributed patterns of neural reactivation as a valid and objective marker of the quality of recollection.

Evaluation of the educational value of YouTube videos about physical examination of the cardiovascular and respiratory systems.

PubMed

Azer, Samy A; Algrain, Hala A; AlKhelaif, Rana A; AlEshaiwi, Sarah M

2013-11-13

A number of studies have evaluated the educational contents of videos on YouTube. However, little analysis has been done on videos about physical examination. This study aimed to analyze YouTube videos about physical examination of the cardiovascular and respiratory systems. It was hypothesized that the educational standards of videos on YouTube would vary significantly. During the period from November 2, 2011 to December 2, 2011, YouTube was searched by three assessors for videos covering the clinical examination of the cardiovascular and respiratory systems. For each video, the following information was collected: title, authors, duration, number of viewers, and total number of days on YouTube. Using criteria comprising content, technical authority, and pedagogy parameters, videos were rated independently by three assessors and grouped into educationally useful and non-useful videos. A total of 1920 videos were screened. Only relevant videos covering the examination of adults in the English language were identified (n=56). Of these, 20 were found to be relevant to cardiovascular examinations and 36 to respiratory examinations. Further analysis revealed that 9 provided useful information on cardiovascular examinations and 7 on respiratory examinations: scoring mean 14.9 (SD 0.33) and mean 15.0 (SD 0.00), respectively. The other videos, 11 covering cardiovascular and 29 on respiratory examinations, were not useful educationally, scoring mean 11.1 (SD 1.08) and mean 11.2 (SD 1.29), respectively. The differences between these two categories were significant (P<.001 for both body systems). The concordance between the assessors on applying the criteria was 0.89, with a kappa score >.86. A small number of videos about physical examination of the cardiovascular and respiratory systems were identified as educationally useful; these videos can be used by medical students for independent learning and by clinical teachers as learning resources. The scoring system utilized by this study is simple, easy to apply, and could be used by other researchers on similar topics.

47 CFR 11.2 - Definitions.

Code of Federal Regulations, 2011 CFR

2011-10-01

... television stations, and wired and wireless cable television systems, DBS, DTV, SDARS, digital cable and DAB, and wireline video systems. (d) Wireline Video System. The system of a wireline common carrier used to provide video programming service. (e) Participating National (PN). PN stations are broadcast stations...

Real-time action recognition using a multilayer descriptor with variable size

NASA Astrophysics Data System (ADS)

Alcantara, Marlon F.; Moreira, Thierry P.; Pedrini, Helio

2016-01-01

Video analysis technology has become less expensive and more powerful in terms of storage resources and resolution capacity, promoting progress in a wide range of applications. Video-based human action detection has been used for several tasks in surveillance environments, such as forensic investigation, patient monitoring, medical training, accident prevention, and traffic monitoring, among others. We present a method for action identification based on adaptive training of a multilayer descriptor applied to a single classifier. Cumulative motion shapes (CMSs) are extracted according to the number of frames present in the video. Each CMS is employed as a self-sufficient layer in the training stage but belongs to the same descriptor. A robust classification is achieved through individual responses of classifiers for each layer, and the dominant result is used as a final outcome. Experiments are conducted on five public datasets (Weizmann, KTH, MuHAVi, IXMAS, and URADL) to demonstrate the effectiveness of the method in terms of accuracy in real time.

Automated frame selection process for high-resolution microendoscopy

NASA Astrophysics Data System (ADS)

Ishijima, Ayumu; Schwarz, Richard A.; Shin, Dongsuk; Mondrik, Sharon; Vigneswaran, Nadarajah; Gillenwater, Ann M.; Anandasabapathy, Sharmila; Richards-Kortum, Rebecca

2015-04-01

We developed an automated frame selection algorithm for high-resolution microendoscopy video sequences. The algorithm rapidly selects a representative frame with minimal motion artifact from a short video sequence, enabling fully automated image analysis at the point-of-care. The algorithm was evaluated by quantitative comparison of diagnostically relevant image features and diagnostic classification results obtained using automated frame selection versus manual frame selection. A data set consisting of video sequences collected in vivo from 100 oral sites and 167 esophageal sites was used in the analysis. The area under the receiver operating characteristic curve was 0.78 (automated selection) versus 0.82 (manual selection) for oral sites, and 0.93 (automated selection) versus 0.92 (manual selection) for esophageal sites. The implementation of fully automated high-resolution microendoscopy at the point-of-care has the potential to reduce the number of biopsies needed for accurate diagnosis of precancer and cancer in low-resource settings where there may be limited infrastructure and personnel for standard histologic analysis.

Fluorescence imaging to quantify crop residue cover

NASA Technical Reports Server (NTRS)

Daughtry, C. S. T.; Mcmurtrey, J. E., III; Chappelle, E. W.

1994-01-01

Crop residues, the portion of the crop left in the field after harvest, can be an important management factor in controlling soil erosion. Methods to quantify residue cover are needed that are rapid, accurate, and objective. Scenes with known amounts of crop residue were illuminated with long wave ultraviolet (UV) radiation and fluorescence images were recorded with an intensified video camera fitted with a 453 to 488 nm band pass filter. A light colored soil and a dark colored soil were used as background for the weathered soybean stems. Residue cover was determined by counting the proportion of the pixels in the image with fluorescence values greater than a threshold. Soil pixels had the lowest gray levels in the images. The values of the soybean residue pixels spanned nearly the full range of the 8-bit video data. Classification accuracies typically were within 3(absolute units) of measured cover values. Video imaging can provide an intuitive understanding of the fraction of the soil covered by residue.

Uniform Local Binary Pattern Based Texture-Edge Feature for 3D Human Behavior Recognition.

PubMed

Ming, Yue; Wang, Guangchao; Fan, Chunxiao

2015-01-01

With the rapid development of 3D somatosensory technology, human behavior recognition has become an important research field. Human behavior feature analysis has evolved from traditional 2D features to 3D features. In order to improve the performance of human activity recognition, a human behavior recognition method is proposed, which is based on a hybrid texture-edge local pattern coding feature extraction and integration of RGB and depth videos information. The paper mainly focuses on background subtraction on RGB and depth video sequences of behaviors, extracting and integrating historical images of the behavior outlines, feature extraction and classification. The new method of 3D human behavior recognition has achieved the rapid and efficient recognition of behavior videos. A large number of experiments show that the proposed method has faster speed and higher recognition rate. The recognition method has good robustness for different environmental colors, lightings and other factors. Meanwhile, the feature of mixed texture-edge uniform local binary pattern can be used in most 3D behavior recognition.

Improved supervised classification of accelerometry data to distinguish behaviors of soaring birds.

PubMed

Sur, Maitreyi; Suffredini, Tony; Wessells, Stephen M; Bloom, Peter H; Lanzone, Michael; Blackshire, Sheldon; Sridhar, Srisarguru; Katzner, Todd

2017-01-01

Soaring birds can balance the energetic costs of movement by switching between flapping, soaring and gliding flight. Accelerometers can allow quantification of flight behavior and thus a context to interpret these energetic costs. However, models to interpret accelerometry data are still being developed, rarely trained with supervised datasets, and difficult to apply. We collected accelerometry data at 140Hz from a trained golden eagle (Aquila chrysaetos) whose flight we recorded with video that we used to characterize behavior. We applied two forms of supervised classifications, random forest (RF) models and K-nearest neighbor (KNN) models. The KNN model was substantially easier to implement than the RF approach but both were highly accurate in classifying basic behaviors such as flapping (85.5% and 83.6% accurate, respectively), soaring (92.8% and 87.6%) and sitting (84.1% and 88.9%) with overall accuracies of 86.6% and 92.3% respectively. More detailed classification schemes, with specific behaviors such as banking and straight flights were well classified only by the KNN model (91.24% accurate; RF = 61.64% accurate). The RF model maintained its accuracy of classifying basic behavior classification accuracy of basic behaviors at sampling frequencies as low as 10Hz, the KNN at sampling frequencies as low as 20Hz. Classification of accelerometer data collected from free ranging birds demonstrated a strong dependence of predicted behavior on the type of classification model used. Our analyses demonstrate the consequence of different approaches to classification of accelerometry data, the potential to optimize classification algorithms with validated flight behaviors to improve classification accuracy, ideal sampling frequencies for different classification algorithms, and a number of ways to improve commonly used analytical techniques and best practices for classification of accelerometry data.

Improved supervised classification of accelerometry data to distinguish behaviors of soaring birds

PubMed Central

Suffredini, Tony; Wessells, Stephen M.; Bloom, Peter H.; Lanzone, Michael; Blackshire, Sheldon; Sridhar, Srisarguru; Katzner, Todd

2017-01-01

Soaring birds can balance the energetic costs of movement by switching between flapping, soaring and gliding flight. Accelerometers can allow quantification of flight behavior and thus a context to interpret these energetic costs. However, models to interpret accelerometry data are still being developed, rarely trained with supervised datasets, and difficult to apply. We collected accelerometry data at 140Hz from a trained golden eagle (Aquila chrysaetos) whose flight we recorded with video that we used to characterize behavior. We applied two forms of supervised classifications, random forest (RF) models and K-nearest neighbor (KNN) models. The KNN model was substantially easier to implement than the RF approach but both were highly accurate in classifying basic behaviors such as flapping (85.5% and 83.6% accurate, respectively), soaring (92.8% and 87.6%) and sitting (84.1% and 88.9%) with overall accuracies of 86.6% and 92.3% respectively. More detailed classification schemes, with specific behaviors such as banking and straight flights were well classified only by the KNN model (91.24% accurate; RF = 61.64% accurate). The RF model maintained its accuracy of classifying basic behavior classification accuracy of basic behaviors at sampling frequencies as low as 10Hz, the KNN at sampling frequencies as low as 20Hz. Classification of accelerometer data collected from free ranging birds demonstrated a strong dependence of predicted behavior on the type of classification model used. Our analyses demonstrate the consequence of different approaches to classification of accelerometry data, the potential to optimize classification algorithms with validated flight behaviors to improve classification accuracy, ideal sampling frequencies for different classification algorithms, and a number of ways to improve commonly used analytical techniques and best practices for classification of accelerometry data. PMID:28403159

Improved supervised classification of accelerometry data to distinguish behaviors of soaring birds

USGS Publications Warehouse

Sur, Maitreyi; Suffredini, Tony; Wessells, Stephen M.; Bloom, Peter H.; Lanzone, Michael J.; Blackshire, Sheldon; Sridhar, Srisarguru; Katzner, Todd

2017-01-01

Soaring birds can balance the energetic costs of movement by switching between flapping, soaring and gliding flight. Accelerometers can allow quantification of flight behavior and thus a context to interpret these energetic costs. However, models to interpret accelerometry data are still being developed, rarely trained with supervised datasets, and difficult to apply. We collected accelerometry data at 140Hz from a trained golden eagle (Aquila chrysaetos) whose flight we recorded with video that we used to characterize behavior. We applied two forms of supervised classifications, random forest (RF) models and K-nearest neighbor (KNN) models. The KNN model was substantially easier to implement than the RF approach but both were highly accurate in classifying basic behaviors such as flapping (85.5% and 83.6% accurate, respectively), soaring (92.8% and 87.6%) and sitting (84.1% and 88.9%) with overall accuracies of 86.6% and 92.3% respectively. More detailed classification schemes, with specific behaviors such as banking and straight flights were well classified only by the KNN model (91.24% accurate; RF = 61.64% accurate). The RF model maintained its accuracy of classifying basic behavior classification accuracy of basic behaviors at sampling frequencies as low as 10Hz, the KNN at sampling frequencies as low as 20Hz. Classification of accelerometer data collected from free ranging birds demonstrated a strong dependence of predicted behavior on the type of classification model used. Our analyses demonstrate the consequence of different approaches to classification of accelerometry data, the potential to optimize classification algorithms with validated flight behaviors to improve classification accuracy, ideal sampling frequencies for different classification algorithms, and a number of ways to improve commonly used analytical techniques and best practices for classification of accelerometry data.

Robust Transmission of H.264/AVC Streams Using Adaptive Group Slicing and Unequal Error Protection

NASA Astrophysics Data System (ADS)

Thomos, Nikolaos; Argyropoulos, Savvas; Boulgouris, Nikolaos V.; Strintzis, Michael G.

2006-12-01

We present a novel scheme for the transmission of H.264/AVC video streams over lossy packet networks. The proposed scheme exploits the error-resilient features of H.264/AVC codec and employs Reed-Solomon codes to protect effectively the streams. A novel technique for adaptive classification of macroblocks into three slice groups is also proposed. The optimal classification of macroblocks and the optimal channel rate allocation are achieved by iterating two interdependent steps. Dynamic programming techniques are used for the channel rate allocation process in order to reduce complexity. Simulations clearly demonstrate the superiority of the proposed method over other recent algorithms for transmission of H.264/AVC streams.

A web-based video annotation system for crowdsourcing surveillance videos

NASA Astrophysics Data System (ADS)

Gadgil, Neeraj J.; Tahboub, Khalid; Kirsh, David; Delp, Edward J.

2014-03-01

Video surveillance systems are of a great value to prevent threats and identify/investigate criminal activities. Manual analysis of a huge amount of video data from several cameras over a long period of time often becomes impracticable. The use of automatic detection methods can be challenging when the video contains many objects with complex motion and occlusions. Crowdsourcing has been proposed as an effective method for utilizing human intelligence to perform several tasks. Our system provides a platform for the annotation of surveillance video in an organized and controlled way. One can monitor a surveillance system using a set of tools such as training modules, roles and labels, task management. This system can be used in a real-time streaming mode to detect any potential threats or as an investigative tool to analyze past events. Annotators can annotate video contents assigned to them for suspicious activity or criminal acts. First responders are then able to view the collective annotations and receive email alerts about a newly reported incident. They can also keep track of the annotators' training performance, manage their activities and reward their success. By providing this system, the process of video analysis is made more efficient.

Service provider perceptions of transitioning from audio to video capability in a telehealth system: a qualitative evaluation.

PubMed

Clay-Williams, Robyn; Baysari, Melissa; Taylor, Natalie; Zalitis, Dianne; Georgiou, Andrew; Robinson, Maureen; Braithwaite, Jeffrey; Westbrook, Johanna

2017-08-14

Telephone consultation and triage services are increasingly being used to deliver health advice. Availability of high speed internet services in remote areas allows healthcare providers to move from telephone to video telehealth services. Current approaches for assessing video services have limitations. This study aimed to identify the challenges for service providers associated with transitioning from audio to video technology. Using a mixed-method, qualitative approach, we observed training of service providers who were required to switch from telephone to video, and conducted pre- and post-training interviews with 15 service providers and their trainers on the challenges associated with transitioning to video. Two full days of simulation training were observed. Data were transcribed and analysed using an inductive approach; a modified constant comparative method was employed to identify common themes. We found three broad categories of issues likely to affect implementation of the video service: social, professional, and technical. Within these categories, eight sub-themes were identified; they were: enhanced delivery of the health service, improved health advice for people living in remote areas, safety concerns, professional risks, poor uptake of video service, system design issues, use of simulation for system testing, and use of simulation for system training. This study identified a number of unexpected potential barriers to successful transition from telephone to the video system. Most prominent were technical and training issues, and personal safety concerns about transitioning from telephone to video media. Addressing identified issues prior to implementation of a new video telehealth system is likely to improve effectiveness and uptake.

Automated Thermal Image Processing for Detection and Classification of Birds and Bats - FY2012 Annual Report

DOE Office of Scientific and Technical Information (OSTI.GOV)

Duberstein, Corey A.; Matzner, Shari; Cullinan, Valerie I.

Surveying wildlife at risk from offshore wind energy development is difficult and expensive. Infrared video can be used to record birds and bats that pass through the camera view, but it is also time consuming and expensive to review video and determine what was recorded. We proposed to conduct algorithm and software development to identify and to differentiate thermally detected targets of interest that would allow automated processing of thermal image data to enumerate birds, bats, and insects. During FY2012 we developed computer code within MATLAB to identify objects recorded in video and extract attribute information that describes the objectsmore » recorded. We tested the efficiency of track identification using observer-based counts of tracks within segments of sample video. We examined object attributes, modeled the effects of random variability on attributes, and produced data smoothing techniques to limit random variation within attribute data. We also began drafting and testing methodology to identify objects recorded on video. We also recorded approximately 10 hours of infrared video of various marine birds, passerine birds, and bats near the Pacific Northwest National Laboratory (PNNL) Marine Sciences Laboratory (MSL) at Sequim, Washington. A total of 6 hours of bird video was captured overlooking Sequim Bay over a series of weeks. An additional 2 hours of video of birds was also captured during two weeks overlooking Dungeness Bay within the Strait of Juan de Fuca. Bats and passerine birds (swallows) were also recorded at dusk on the MSL campus during nine evenings. An observer noted the identity of objects viewed through the camera concurrently with recording. These video files will provide the information necessary to produce and test software developed during FY2013. The annotation will also form the basis for creation of a method to reliably identify recorded objects.« less

Geologic characteristics of benthic habitats in Glacier Bay, southeast Alaska

USGS Publications Warehouse

Harney, Jodi N.; Cochrane, Guy R.; Etherington, Lisa L.; Dartnell, Pete; Golden, Nadine E.; Chezar, Hank

2006-01-01

In April 2004, more than 40 hours of georeferenced submarine digital video was collected in water depths of 15-370 m in Glacier Bay to (1) ground-truth existing geophysical data (bathymetry and acoustic reflectance), (2) examine and record geologic characteristics of the sea floor, and (3) investigate the relation between substrate types and benthic communities, and (4) construct predictive maps of seafloor geomorphology and habitat distribution. Common substrates observed include rock, boulders, cobbles, rippled sand, bioturbated mud, and extensive beds of living horse mussels and scallops. Four principal sea-floor geomorphic types are distinguished by using video observations. Their distribution in lower and central Glacier Bay is predicted using a supervised, hierarchical decision-tree statistical classification of geophysical data.

A comparison between space-time video descriptors

NASA Astrophysics Data System (ADS)

Costantini, Luca; Capodiferro, Licia; Neri, Alessandro

2013-02-01

The description of space-time patches is a fundamental task in many applications such as video retrieval or classification. Each space-time patch can be described by using a set of orthogonal functions that represent a subspace, for example a sphere or a cylinder, within the patch. In this work, our aim is to investigate the differences between the spherical descriptors and the cylindrical descriptors. In order to compute the descriptors, the 3D spherical and cylindrical Zernike polynomials are employed. This is important because both the functions are based on the same family of polynomials, and only the symmetry is different. Our experimental results show that the cylindrical descriptor outperforms the spherical descriptor. However, the performances of the two descriptors are similar.

A Web-Based Video Digitizing System for the Study of Projectile Motion.

ERIC Educational Resources Information Center

Chow, John W.; Carlton, Les G.; Ekkekakis, Panteleimon; Hay, James G.

2000-01-01

Discusses advantages of a video-based, digitized image system for the study and analysis of projectile motion in the physics laboratory. Describes the implementation of a web-based digitized video system. (WRM)

Flexible mobile robot system for smart optical pipe inspection

NASA Astrophysics Data System (ADS)

Kampfer, Wolfram; Bartzke, Ralf; Ziehl, Wolfgang

1998-03-01

Damages of pipes can be inspected and graded by TV technology available on the market. Remotely controlled vehicles carry a TV-camera through pipes. Thus, depending on the experience and the capability of the operator, diagnosis failures can not be avoided. The classification of damages requires the knowledge of the exact geometrical dimensions of the damages such as width and depth of cracks, fractures and defect connections. Within the framework of a joint R&D project a sensor based pipe inspection system named RODIAS has been developed with two partners from industry and research institute. It consists of a remotely controlled mobile robot which carries intelligent sensors for on-line sewerage inspection purpose. The sensor is based on a 3D-optical sensor and a laser distance sensor. The laser distance sensor is integrated in the optical system of the camera and can measure the distance between camera and object. The angle of view can be determined from the position of the pan and tilt unit. With coordinate transformations it is possible to calculate the spatial coordinates for every point of the video image. So the geometry of an object can be described exactly. The company Optimess has developed TriScan32, a special software for pipe condition classification. The user can start complex measurements of profiles, pipe displacements or crack widths simply by pressing a push-button. The measuring results are stored together with other data like verbal damage descriptions and digitized images in a data base.

Automated Video Quality Assessment for Deep-Sea Video

NASA Astrophysics Data System (ADS)

Pirenne, B.; Hoeberechts, M.; Kalmbach, A.; Sadhu, T.; Branzan Albu, A.; Glotin, H.; Jeffries, M. A.; Bui, A. O. V.

2015-12-01

Video provides a rich source of data for geophysical analysis, often supplying detailed information about the environment when other instruments may not. This is especially true of deep-sea environments, where direct visual observations cannot be made. As computer vision techniques improve and volumes of video data increase, automated video analysis is emerging as a practical alternative to labor-intensive manual analysis. Automated techniques can be much more sensitive to video quality than their manual counterparts, so performing quality assessment before doing full analysis is critical to producing valid results.Ocean Networks Canada (ONC), an initiative of the University of Victoria, operates cabled ocean observatories that supply continuous power and Internet connectivity to a broad suite of subsea instruments from the coast to the deep sea, including video and still cameras. This network of ocean observatories has produced almost 20,000 hours of video (about 38 hours are recorded each day) and an additional 8,000 hours of logs from remotely operated vehicle (ROV) dives. We begin by surveying some ways in which deep-sea video poses challenges for automated analysis, including: 1. Non-uniform lighting: Single, directional, light sources produce uneven luminance distributions and shadows; remotely operated lighting equipment are also susceptible to technical failures. 2. Particulate noise: Turbidity and marine snow are often present in underwater video; particles in the water column can have sharper focus and higher contrast than the objects of interest due to their proximity to the light source and can also influence the camera's autofocus and auto white-balance routines. 3. Color distortion (low contrast): The rate of absorption of light in water varies by wavelength, and is higher overall than in air, altering apparent colors and lowering the contrast of objects at a distance.We also describe measures under development at ONC for detecting and mitigating these effects. These steps include filtering out unusable data, color and luminance balancing, and choosing the most appropriate image descriptors. We apply these techniques to generate automated quality assessment of video data and illustrate their utility with an example application where we perform vision-based substrate classification.

A system for endobronchial video analysis

NASA Astrophysics Data System (ADS)

Byrnes, Patrick D.; Higgins, William E.

2017-03-01

Image-guided bronchoscopy is a critical component in the treatment of lung cancer and other pulmonary disorders. During bronchoscopy, a high-resolution endobronchial video stream facilitates guidance through the lungs and allows for visual inspection of a patient's airway mucosal surfaces. Despite the detailed information it contains, little effort has been made to incorporate recorded video into the clinical workflow. Follow-up procedures often required in cancer assessment or asthma treatment could significantly benefit from effectively parsed and summarized video. Tracking diagnostic regions of interest (ROIs) could potentially better equip physicians to detect early airway-wall cancer or improve asthma treatments, such as bronchial thermoplasty. To address this need, we have developed a system for the postoperative analysis of recorded endobronchial video. The system first parses an input video stream into endoscopic shots, derives motion information, and selects salient representative key frames. Next, a semi-automatic method for CT-video registration creates data linkages between a CT-derived airway-tree model and the input video. These data linkages then enable the construction of a CT-video chest model comprised of a bronchoscopy path history (BPH) - defining all airway locations visited during a procedure - and texture-mapping information for rendering registered video frames onto the airwaytree model. A suite of analysis tools is included to visualize and manipulate the extracted data. Video browsing and retrieval is facilitated through a video table of contents (TOC) and a search query interface. The system provides a variety of operational modes and additional functionality, including the ability to define regions of interest. We demonstrate the potential of our system using two human case study examples.

A functional video-based anthropometric measuring system

NASA Technical Reports Server (NTRS)

Nixon, J. H.; Cater, J. P.

1982-01-01

A high-speed anthropometric three dimensional measurement system using the Selcom Selspot motion tracking instrument for visual data acquisition is discussed. A three-dimensional scanning system was created which collects video, audio, and performance data on a single standard video cassette recorder. Recording rates of 1 megabit per second for periods of up to two hours are possible with the system design. A high-speed off-the-shelf motion analysis system for collecting optical information as used. The video recording adapter (VRA) is interfaced to the Selspot data acquisition system.

Improving the Identification of Neonatal Encephalopathy: Utility of a Web-Based Video Tool.

PubMed

Ivy, Autumn S; Clark, Catherine L; Bahm, Sarah M; Meurs, Krisa P Van; Wusthoff, Courtney J

2017-04-01

Objective  This study tested the effectiveness of a video teaching tool in improving identification and classification of encephalopathy in infants. Study Design  We developed an innovative video teaching tool to help clinicians improve their skills in interpreting the neonatal neurological examination for grading encephalopathy. Pediatric residents were shown 1-minute video clips demonstrating exam findings in normal neonates and neonates with various degrees of encephalopathy. Findings from five domains were demonstrated: spontaneous activity, level of alertness, posture/tone, reflexes, and autonomic responses. After each clip, subjects were asked to identify whether the exam finding was normal or consistent with mild, moderate, or severe abnormality. Subjects were then directed to a web-based teaching toolkit, containing a compilation of videos demonstrating normal and abnormal findings on the neonatal neurological examination. Immediately after training, subjects underwent posttesting, again identifying exam findings as normal, mild, moderate, or severe abnormality. Results  Residents improved in their overall ability to identify and classify neonatal encephalopathy after viewing the teaching tool. In particular, the identification of abnormal spontaneous activity, reflexes, and autonomic responses were most improved. Conclusion  This pretest/posttest evaluation of an educational tool demonstrates that after viewing our toolkit, pediatric residents were able to improve their overall ability to detect neonatal encephalopathy. Thieme Medical Publishers 333 Seventh Avenue, New York, NY 10001, USA.

Toward a consensus definition of pathological video-gaming: a systematic review of psychometric assessment tools.

PubMed

King, Daniel L; Haagsma, Maria C; Delfabbro, Paul H; Gradisar, Michael; Griffiths, Mark D

2013-04-01

Pathological video-gaming, or its proposed DSM-V classification of "Internet Use Disorder", is of increasing interest to scholars and practitioners in allied health disciplines. This systematic review was designed to evaluate the standards in pathological video-gaming instrumentation, according to Cicchetti (1994) and Groth-Marnat's (2009) criteria and guidelines for sound psychometric assessment. A total of 63 quantitative studies, including eighteen instruments and representing 58,415 participants, were evaluated. Results indicated that reviewed instrumentation may be broadly characterized as inconsistent. Strengths of available measures include: (i) short length and ease of scoring, (ii) excellent internal consistency and convergent validity, and (iii) potentially adequate data for development of standardized norms for adolescent populations. However, key limitations included: (a) inconsistent coverage of core addiction indicators, (b) varying cut-off scores to indicate clinical status, (c) a lack of a temporal dimension, (d) untested or inconsistent dimensionality, and (e) inadequate data on predictive validity and inter-rater reliability. An emerging consensus suggests that pathological video-gaming is commonly defined by (1) withdrawal, (2) loss of control, and (3) conflict. It is concluded that a unified approach to assessment of pathological video-gaming is needed. A synthesis of extant research efforts by meta-analysis may be difficult in the context of several divergent approaches to assessment. Copyright © 2013 Elsevier Ltd. All rights reserved.

47 CFR 76.1200 - Definitions.

Code of Federal Regulations, 2010 CFR

2010-10-01

... in this subpart: (a) Multichannel video programming system. A distribution system that makes available for purchase, by customers or subscribers, multiple channels of video programming other than an...-to-home multichannel video programming via satellite, and satellite master antenna systems. (b...

A new visual navigation system for exploring biomedical Open Educational Resource (OER) videos.

PubMed

Zhao, Baoquan; Xu, Songhua; Lin, Shujin; Luo, Xiaonan; Duan, Lian

2016-04-01

Biomedical videos as open educational resources (OERs) are increasingly proliferating on the Internet. Unfortunately, seeking personally valuable content from among the vast corpus of quality yet diverse OER videos is nontrivial due to limitations of today's keyword- and content-based video retrieval techniques. To address this need, this study introduces a novel visual navigation system that facilitates users' information seeking from biomedical OER videos in mass quantity by interactively offering visual and textual navigational clues that are both semantically revealing and user-friendly. The authors collected and processed around 25 000 YouTube videos, which collectively last for a total length of about 4000 h, in the broad field of biomedical sciences for our experiment. For each video, its semantic clues are first extracted automatically through computationally analyzing audio and visual signals, as well as text either accompanying or embedded in the video. These extracted clues are subsequently stored in a metadata database and indexed by a high-performance text search engine. During the online retrieval stage, the system renders video search results as dynamic web pages using a JavaScript library that allows users to interactively and intuitively explore video content both efficiently and effectively.ResultsThe authors produced a prototype implementation of the proposed system, which is publicly accessible athttps://patentq.njit.edu/oer To examine the overall advantage of the proposed system for exploring biomedical OER videos, the authors further conducted a user study of a modest scale. The study results encouragingly demonstrate the functional effectiveness and user-friendliness of the new system for facilitating information seeking from and content exploration among massive biomedical OER videos. Using the proposed tool, users can efficiently and effectively find videos of interest, precisely locate video segments delivering personally valuable information, as well as intuitively and conveniently preview essential content of a single or a collection of videos. © The Author 2015. Published by Oxford University Press on behalf of the American Medical Informatics Association. All rights reserved. For Permissions, please email: journals.permissions@oup.com.

Power-rate-distortion analysis for wireless video communication under energy constraint

NASA Astrophysics Data System (ADS)

He, Zhihai; Liang, Yongfang; Ahmad, Ishfaq

2004-01-01

In video coding and streaming over wireless communication network, the power-demanding video encoding operates on the mobile devices with limited energy supply. To analyze, control, and optimize the rate-distortion (R-D) behavior of the wireless video communication system under the energy constraint, we need to develop a power-rate-distortion (P-R-D) analysis framework, which extends the traditional R-D analysis by including another dimension, the power consumption. Specifically, in this paper, we analyze the encoding mechanism of typical video encoding systems and develop a parametric video encoding architecture which is fully scalable in computational complexity. Using dynamic voltage scaling (DVS), a hardware technology recently developed in CMOS circuits design, the complexity scalability can be translated into the power consumption scalability of the video encoder. We investigate the rate-distortion behaviors of the complexity control parameters and establish an analytic framework to explore the P-R-D behavior of the video encoding system. Both theoretically and experimentally, we show that, using this P-R-D model, the encoding system is able to automatically adjust its complexity control parameters to match the available energy supply of the mobile device while maximizing the picture quality. The P-R-D model provides a theoretical guideline for system design and performance optimization in wireless video communication under energy constraint, especially over the wireless video sensor network.

pyAudioAnalysis: An Open-Source Python Library for Audio Signal Analysis.

PubMed

Giannakopoulos, Theodoros

2015-01-01

Audio information plays a rather important role in the increasing digital content that is available today, resulting in a need for methodologies that automatically analyze such content: audio event recognition for home automations and surveillance systems, speech recognition, music information retrieval, multimodal analysis (e.g. audio-visual analysis of online videos for content-based recommendation), etc. This paper presents pyAudioAnalysis, an open-source Python library that provides a wide range of audio analysis procedures including: feature extraction, classification of audio signals, supervised and unsupervised segmentation and content visualization. pyAudioAnalysis is licensed under the Apache License and is available at GitHub (https://github.com/tyiannak/pyAudioAnalysis/). Here we present the theoretical background behind the wide range of the implemented methodologies, along with evaluation metrics for some of the methods. pyAudioAnalysis has been already used in several audio analysis research applications: smart-home functionalities through audio event detection, speech emotion recognition, depression classification based on audio-visual features, music segmentation, multimodal content-based movie recommendation and health applications (e.g. monitoring eating habits). The feedback provided from all these particular audio applications has led to practical enhancement of the library.

pyAudioAnalysis: An Open-Source Python Library for Audio Signal Analysis

PubMed Central

Giannakopoulos, Theodoros

2015-01-01

Audio information plays a rather important role in the increasing digital content that is available today, resulting in a need for methodologies that automatically analyze such content: audio event recognition for home automations and surveillance systems, speech recognition, music information retrieval, multimodal analysis (e.g. audio-visual analysis of online videos for content-based recommendation), etc. This paper presents pyAudioAnalysis, an open-source Python library that provides a wide range of audio analysis procedures including: feature extraction, classification of audio signals, supervised and unsupervised segmentation and content visualization. pyAudioAnalysis is licensed under the Apache License and is available at GitHub (https://github.com/tyiannak/pyAudioAnalysis/). Here we present the theoretical background behind the wide range of the implemented methodologies, along with evaluation metrics for some of the methods. pyAudioAnalysis has been already used in several audio analysis research applications: smart-home functionalities through audio event detection, speech emotion recognition, depression classification based on audio-visual features, music segmentation, multimodal content-based movie recommendation and health applications (e.g. monitoring eating habits). The feedback provided from all these particular audio applications has led to practical enhancement of the library. PMID:26656189

Amplifiers in the radio-electronic equipment of aircraft

NASA Astrophysics Data System (ADS)

Khol'Nyi, Vladimir Ia.

The applications, classification, and technical specifications of airborne electronic amplifiers are discussed. Particular attention is given to the general design and principles of operation of single amplification cascades and multicascade amplifiers, including dc, audio, and video amplifiers used as part of the radio-electronic equipment of modern aircraft. The discussion also covers the principal technical and performance characteristics of various amplifiers, their operating conditions, service, and repair.

Multimodal Sparse Coding for Event Detection

DTIC Science & Technology

2015-10-13

classification tasks based on single modality. We present multimodal sparse coding for learning feature representations shared across multiple modalities...The shared representa- tions are applied to multimedia event detection (MED) and evaluated in compar- ison to unimodal counterparts, as well as other...and video tracks from the same multimedia clip, we can force the two modalities to share a similar sparse representation whose benefit includes robust

A professional and cost effective digital video editing and image storage system for the operating room.

PubMed

Scollato, A; Perrini, P; Benedetto, N; Di Lorenzo, N

2007-06-01

We propose an easy-to-construct digital video editing system ideal to produce video documentation and still images. A digital video editing system applicable to many video sources in the operating room is described in detail. The proposed system has proved easy to use and permits one to obtain videography quickly and easily. Mixing different streams of video input from all the devices in use in the operating room, the application of filters and effects produces a final, professional end-product. Recording on a DVD provides an inexpensive, portable and easy-to-use medium to store or re-edit or tape at a later time. From stored videography it is easy to extract high-quality, still images useful for teaching, presentations and publications. In conclusion digital videography and still photography can easily be recorded by the proposed system, producing high-quality video recording. The use of firewire ports provides good compatibility with next-generation hardware and software. The high standard of quality makes the proposed system one of the lowest priced products available today.

Impact of current video game playing on robotic simulation skills among medical students.

PubMed

Öge, Tufan; Borahay, Mostafa A; Achjian, Tamar; Kılıç, Sami Gökhan

2015-01-01

To evaluate the impact of current and prior video game playing on initial robotic simulation skill acquisition. This cross-sectional descriptive study (Canadian Task Force Classification II-1) was conducted at a medical university training center. The study subjects were medical students who currently played video games (Group I) and those who had not played video games in the last 2 years (Group II). The robotic skills of both groups were assessed using simulation. Twenty-two students enrolled in this study; however, only 21 completed it. The median age of the participants was 23 (22-24) years and 24 (23-26) years in Groups I and II, respectively. Among the participants, 15 (71.4%) were male and 6 (28.5%) were female, and 90.4% of the students started playing video games in primary school. When the 2 groups were compared according to the completion time of each exercise, Group I finished more quickly than Group II in the Peg Board-1 exercise (p>0.05), whereas Group II had better results in 3 exercises including Pick and Place, Ring and Rail, and Thread the Rings-1. However, none of the differences were found to be statistically significant (p>.05), and according to the overall scores based on the time to complete exercises, economy of motion, instrument collision, use of excessive instrument force, instruments out of view, and master workspace range, the scores were not statistically different between Groups I and II (p>.05). According to the basic robotic simulation exercise results, there was no difference between medical students who used to play video games and those who still played video games. Studies evaluating baseline visuospatial skills with larger sample sizes are needed.

Impact of current video game playing on robotic simulation skills among medical students

PubMed Central

Öge, Tufan; Borahay, Mostafa A.; Achjian, Tamar; Kılıç, Sami Gökhan

2015-01-01

Objective To evaluate the impact of current and prior video game playing on initial robotic simulation skill acquisition. Material and Methods This cross-sectional descriptive study (Canadian Task Force Classification II-1) was conducted at a medical university training center. The study subjects were medical students who currently played video games (Group I) and those who had not played video games in the last 2 years (Group II). The robotic skills of both groups were assessed using simulation. Results Twenty-two students enrolled in this study; however, only 21 completed it. The median age of the participants was 23 (22–24) years and 24 (23–26) years in Groups I and II, respectively. Among the participants, 15 (71.4%) were male and 6 (28.5%) were female, and 90.4% of the students started playing video games in primary school. When the 2 groups were compared according to the completion time of each exercise, Group I finished more quickly than Group II in the Peg Board-1 exercise (p>0.05), whereas Group II had better results in 3 exercises including Pick and Place, Ring and Rail, and Thread the Rings-1. However, none of the differences were found to be statistically significant (p>.05), and according to the overall scores based on the time to complete exercises, economy of motion, instrument collision, use of excessive instrument force, instruments out of view, and master workspace range, the scores were not statistically different between Groups I and II (p>.05). Conclusion According to the basic robotic simulation exercise results, there was no difference between medical students who used to play video games and those who still played video games. Studies evaluating baseline visuospatial skills with larger sample sizes are needed. PMID:25788841

Real-time differentiation of adenomatous and hyperplastic diminutive colorectal polyps during analysis of unaltered videos of standard colonoscopy using a deep learning model.

PubMed

Byrne, Michael F; Chapados, Nicolas; Soudan, Florian; Oertel, Clemens; Linares Pérez, Milagros; Kelly, Raymond; Iqbal, Nadeem; Chandelier, Florent; Rex, Douglas K

2017-10-24

In general, academic but not community endoscopists have demonstrated adequate endoscopic differentiation accuracy to make the 'resect and discard' paradigm for diminutive colorectal polyps workable. Computer analysis of video could potentially eliminate the obstacle of interobserver variability in endoscopic polyp interpretation and enable widespread acceptance of 'resect and discard'. We developed an artificial intelligence (AI) model for real-time assessment of endoscopic video images of colorectal polyps. A deep convolutional neural network model was used. Only narrow band imaging video frames were used, split equally between relevant multiclasses. Unaltered videos from routine exams not specifically designed or adapted for AI classification were used to train and validate the model. The model was tested on a separate series of 125 videos of consecutively encountered diminutive polyps that were proven to be adenomas or hyperplastic polyps. The AI model works with a confidence mechanism and did not generate sufficient confidence to predict the histology of 19 polyps in the test set, representing 15% of the polyps. For the remaining 106 diminutive polyps, the accuracy of the model was 94% (95% CI 86% to 97%), the sensitivity for identification of adenomas was 98% (95% CI 92% to 100%), specificity was 83% (95% CI 67% to 93%), negative predictive value 97% and positive predictive value 90%. An AI model trained on endoscopic video can differentiate diminutive adenomas from hyperplastic polyps with high accuracy. Additional study of this programme in a live patient clinical trial setting to address resect and discard is planned. © Article author(s) (or their employer(s) unless otherwise stated in the text of the article) 2017. All rights reserved. No commercial use is permitted unless otherwise expressly granted.

Diagnostic Utility of Wireless Video-Electroencephalography in Unsedated Dogs.

PubMed

James, F M K; Cortez, M A; Monteith, G; Jokinen, T S; Sanders, S; Wielaender, F; Fischer, A; Lohi, H

2017-09-01

Poor agreement between observers on whether an unusual event is a seizure drives the need for a specific diagnostic tool provided by video-electroencephalography (video-EEG) in human pediatric epileptology. That successful classification of events would be positively associated with increasing EEG recording length and higher event frequency reported before video-EEG evaluation; that a novel wireless video-EEG technique would clarify whether unusual behavioral events were seizures in unsedated dogs. Eighty-one client-owned dogs of various breeds undergoing investigation of unusual behavioral events at 4 institutions. Retrospective case series: evaluation of wireless video-EEG recordings in unsedated dogs performed at 4 institutions. Electroencephalography achieved/excluded diagnosis of epilepsy in 58 dogs (72%); 25 dogs confirmed with epileptic seizures based on ictal/interictal epileptiform discharges, and 33 dogs with no EEG abnormalities associated with their target events. As reported frequency of the target events decreased (annually, monthly, weekly, daily, hourly, minutes, seconds), EEG was less likely to achieve diagnosis (P < 0.001). Every increase in event frequency increased the odds of achieving diagnosis by 2.315 (95% confidence interval: 1.36-4.34). EEG recording length (mean = 3.69 hours, range: 0.17-22.5) was not associated (P = 0.2) with the likelihood of achieving a diagnosis. Wireless video-EEG in unsedated dogs had a high success for diagnosis of unusual behavioral events. This technique offered a reliable clinical tool to investigate the epileptic origin of behavioral events in dogs. Copyright © 2017 The Authors. Journal of Veterinary Internal Medicine published by Wiley Periodicals, Inc. on behalf of the American College of Veterinary Internal Medicine.

Discrete classification technique applied to TV advertisements liking recognition system based on low-cost EEG headsets.

PubMed

Soria Morillo, Luis M; Alvarez-Garcia, Juan A; Gonzalez-Abril, Luis; Ortega Ramírez, Juan A

2016-07-15

In this paper a new approach is applied to the area of marketing research. The aim of this paper is to recognize how brain activity responds during the visualization of short video advertisements using discrete classification techniques. By means of low cost electroencephalography devices (EEG), the activation level of some brain regions have been studied while the ads are shown to users. We may wonder about how useful is the use of neuroscience knowledge in marketing, or what could provide neuroscience to marketing sector, or why this approach can improve the accuracy and the final user acceptance compared to other works. By using discrete techniques over EEG frequency bands of a generated dataset, C4.5, ANN and the new recognition system based on Ameva, a discretization algorithm, is applied to obtain the score given by subjects to each TV ad. The proposed technique allows to reach more than 75 % of accuracy, which is an excellent result taking into account the typology of EEG sensors used in this work. Furthermore, the time consumption of the algorithm proposed is reduced up to 30 % compared to other techniques presented in this paper. This bring about a battery lifetime improvement on the devices where the algorithm is running, extending the experience in the ubiquitous context where the new approach has been tested.

Evaluation of two-dimensional accelerometers to monitor behavior of beef calves after castration.

PubMed

White, Brad J; Coetzee, Johann F; Renter, David G; Babcock, Abram H; Thomson, Daniel U; Andresen, Daniel

2008-08-01

To determine the accuracy of accelerometers for measuring behavior changes in calves and to determine differences in beef calf behavior from before to after castration. 3 healthy Holstein calves and 12 healthy beef calves. 2-dimensional accelerometers were placed on 3 calves, and data were logged simultaneous to video recording of animal behavior. Resulting data were used to generate and validate predictive models to classify posture (standing or lying) and type of activity (standing in place, walking, eating, getting up, lying awake, or lying sleeping). The algorithms developed were used to conduct a prospective trial to compare calf behavior in the first 24 hours after castration (n = 6) with behavior of noncastrated control calves (6) and with presurgical readings from the same castrated calves. On the basis of the analysis of the 2-dimensional accelerometer signal, posture was classified with a high degree of accuracy (98.3%) and the specific activity was estimated with a reasonably low misclassification rate (23.5%). Use of the system to compare behavior after castration revealed that castrated calves spent a significantly larger amount of time standing (82.2%), compared with presurgical readings (46.2%). 2-dimensional accelerometers provided accurate classification of posture and reasonable classification of activity. Applying the system in a castration trial illustrated the usefulness of accelerometers for measuring behavioral changes in individual calves.

Video monitoring system for car seat

NASA Technical Reports Server (NTRS)

Elrod, Susan Vinz (Inventor); Dabney, Richard W. (Inventor)

2004-01-01

A video monitoring system for use with a child car seat has video camera(s) mounted in the car seat. The video images are wirelessly transmitted to a remote receiver/display encased in a portable housing that can be removably mounted in the vehicle in which the car seat is installed.

77 FR 808 - Certain Video Analytics Software, Systems, Components Thereof, and Products Containing Same...

Federal Register 2010, 2011, 2012, 2013, 2014

2012-01-06

... INTERNATIONAL TRADE COMMISSION [Investigation No. 337-TA-795] Certain Video Analytics Software... filed by ObjectVideo, Inc. of Reston, Virginia. 76 FR 45859 (Aug. 1, 2011). The complaint, as amended... certain video analytics software, systems, components thereof, and products containing same by reason of...

47 CFR 76.1506 - Carriage of television broadcast signals.

Code of Federal Regulations, 2010 CFR

2010-10-01

... SERVICES MULTICHANNEL VIDEO AND CABLE TELEVISION SERVICE Open Video Systems § 76.1506 Carriage of television broadcast signals. (a) The provisions of Subpart D shall apply to open video systems in accordance... from multichannel video programming distributors as follows: (1) For a full or partial network station...

47 CFR 76.1506 - Carriage of television broadcast signals.

Code of Federal Regulations, 2012 CFR

2012-10-01

... SERVICES MULTICHANNEL VIDEO AND CABLE TELEVISION SERVICE Open Video Systems § 76.1506 Carriage of television broadcast signals. (a) The provisions of Subpart D shall apply to open video systems in accordance... from multichannel video programming distributors as follows: (1) For a full or partial network station...

47 CFR 76.1506 - Carriage of television broadcast signals.

Code of Federal Regulations, 2013 CFR

2013-10-01

... SERVICES MULTICHANNEL VIDEO AND CABLE TELEVISION SERVICE Open Video Systems § 76.1506 Carriage of television broadcast signals. (a) The provisions of Subpart D shall apply to open video systems in accordance... from multichannel video programming distributors as follows: (1) For a full or partial network station...

Interactive Videos Enhance Learning about Socio-Ecological Systems

ERIC Educational Resources Information Center

Smithwick, Erica; Baxter, Emily; Kim, Kyung; Edel-Malizia, Stephanie; Rocco, Stevie; Blackstock, Dean

2018-01-01

Two forms of interactive video were assessed in an online course focused on conservation. The hypothesis was that interactive video enhances student perceptions about learning and improves mental models of social-ecological systems. Results showed that students reported greater learning and attitudes toward the subject following interactive video.…

47 CFR 76.1506 - Carriage of television broadcast signals.

Code of Federal Regulations, 2011 CFR

2011-10-01

... SERVICES MULTICHANNEL VIDEO AND CABLE TELEVISION SERVICE Open Video Systems § 76.1506 Carriage of television broadcast signals. (a) The provisions of Subpart D shall apply to open video systems in accordance... from multichannel video programming distributors as follows: (1) For a full or partial network station...

47 CFR 76.1506 - Carriage of television broadcast signals.

Code of Federal Regulations, 2014 CFR

2014-10-01

... SERVICES MULTICHANNEL VIDEO AND CABLE TELEVISION SERVICE Open Video Systems § 76.1506 Carriage of television broadcast signals. (a) The provisions of Subpart D shall apply to open video systems in accordance... from multichannel video programming distributors as follows: (1) For a full or partial network station...

Design and implementation of H.264 based embedded video coding technology

NASA Astrophysics Data System (ADS)

Mao, Jian; Liu, Jinming; Zhang, Jiemin

2016-03-01

In this paper, an embedded system for remote online video monitoring was designed and developed to capture and record the real-time circumstances in elevator. For the purpose of improving the efficiency of video acquisition and processing, the system selected Samsung S5PV210 chip as the core processor which Integrated graphics processing unit. And the video was encoded with H.264 format for storage and transmission efficiently. Based on S5PV210 chip, the hardware video coding technology was researched, which was more efficient than software coding. After running test, it had been proved that the hardware video coding technology could obviously reduce the cost of system and obtain the more smooth video display. It can be widely applied for the security supervision [1].

Evaluation of automatic video summarization systems

NASA Astrophysics Data System (ADS)

Taskiran, Cuneyt M.

2006-01-01

Compact representations of video, or video summaries, data greatly enhances efficient video browsing. However, rigorous evaluation of video summaries generated by automatic summarization systems is a complicated process. In this paper we examine the summary evaluation problem. Text summarization is the oldest and most successful summarization domain. We show some parallels between these to domains and introduce methods and terminology. Finally, we present results for a comprehensive evaluation summary that we have performed.

Body worn camera

NASA Astrophysics Data System (ADS)

Aishwariya, A.; Pallavi Sudhir, Gulavani; Garg, Nemesa; Karthikeyan, B.

2017-11-01

A body worn camera is small video camera worn on the body, typically used by police officers to record arrests, evidence from crime scenes. It helps preventing and resolving complaints brought by members of the public; and strengthening police transparency, performance, and accountability. The main constants of this type of the system are video format, resolution, frames rate, and audio quality. This system records the video in .mp4 format with 1080p resolution and 30 frames per second. One more important aspect to while designing this system is amount of power the system requires as battery management becomes very critical. The main design challenges are Size of the Video, Audio for the video. Combining both audio and video and saving it in .mp4 format, Battery, size that is required for 8 hours of continuous recording, Security. For prototyping this system is implemented using Raspberry Pi model B.

Automatic Classification of the Sub-Techniques (Gears) Used in Cross-Country Ski Skating Employing a Mobile Phone

PubMed Central

Stöggl, Thomas; Holst, Anders; Jonasson, Arndt; Andersson, Erik; Wunsch, Tobias; Norström, Christer; Holmberg, Hans-Christer

2014-01-01

The purpose of the current study was to develop and validate an automatic algorithm for classification of cross-country (XC) ski-skating gears (G) using Smartphone accelerometer data. Eleven XC skiers (seven men, four women) with regional-to-international levels of performance carried out roller skiing trials on a treadmill using fixed gears (G2left, G2right, G3, G4left, G4right) and a 950-m trial using different speeds and inclines, applying gears and sides as they normally would. Gear classification by the Smartphone (on the chest) and based on video recordings were compared. Formachine-learning, a collective database was compared to individual data. The Smartphone application identified the trials with fixed gears correctly in all cases. In the 950-m trial, participants executed 140 ± 22 cycles as assessed by video analysis, with the automatic Smartphone application giving a similar value. Based on collective data, gears were identified correctly 86.0% ± 8.9% of the time, a value that rose to 90.3% ± 4.1% (P < 0.01) with machine learning from individual data. Classification was most often incorrect during transition between gears, especially to or from G3. Identification was most often correct for skiers who made relatively few transitions between gears. The accuracy of the automatic procedure for identifying G2left, G2right, G3, G4left and G4right was 96%, 90%, 81%, 88% and 94%, respectively. The algorithm identified gears correctly 100% of the time when a single gear was used and 90% of the time when different gears were employed during a variable protocol. This algorithm could be improved with respect to identification of transitions between gears or the side employed within a given gear. PMID:25365459

78 FR 11988 - Open Video Systems

Federal Register 2010, 2011, 2012, 2013, 2014

2013-02-21

... FEDERAL COMMUNICATIONS COMMISSION 47 CFR Part 76 [CS Docket No. 96-46, FCC 96-334] Open Video Systems AGENCY: Federal Communications Commission. ACTION: Final rule; announcement of effective date... 43160, August 21, 1996. The final rules modified rules and policies concerning Open Video Systems. DATES...

Prototype system of secure VOD

NASA Astrophysics Data System (ADS)

Minemura, Harumi; Yamaguchi, Tomohisa

1997-12-01

Secure digital contents delivery systems are to realize copyright protection and charging mechanism, and aim at secure delivery service of digital contents. Encrypted contents delivery and history (log) management are means to accomplish this purpose. Our final target is to realize a video-on-demand (VOD) system that can prevent illegal usage of video data and manage user history data to achieve a secure video delivery system on the Internet or Intranet. By now, mainly targeting client-server systems connected with enterprise LAN, we have implemented and evaluated a prototype system based on the investigation into the delivery method of encrypted video contents.

Evaluation of the Educational Value of YouTube Videos About Physical Examination of the Cardiovascular and Respiratory Systems

PubMed Central

2013-01-01

Background A number of studies have evaluated the educational contents of videos on YouTube. However, little analysis has been done on videos about physical examination. Objective This study aimed to analyze YouTube videos about physical examination of the cardiovascular and respiratory systems. It was hypothesized that the educational standards of videos on YouTube would vary significantly. Methods During the period from November 2, 2011 to December 2, 2011, YouTube was searched by three assessors for videos covering the clinical examination of the cardiovascular and respiratory systems. For each video, the following information was collected: title, authors, duration, number of viewers, and total number of days on YouTube. Using criteria comprising content, technical authority, and pedagogy parameters, videos were rated independently by three assessors and grouped into educationally useful and non-useful videos. Results A total of 1920 videos were screened. Only relevant videos covering the examination of adults in the English language were identified (n=56). Of these, 20 were found to be relevant to cardiovascular examinations and 36 to respiratory examinations. Further analysis revealed that 9 provided useful information on cardiovascular examinations and 7 on respiratory examinations: scoring mean 14.9 (SD 0.33) and mean 15.0 (SD 0.00), respectively. The other videos, 11 covering cardiovascular and 29 on respiratory examinations, were not useful educationally, scoring mean 11.1 (SD 1.08) and mean 11.2 (SD 1.29), respectively. The differences between these two categories were significant (P<.001 for both body systems). The concordance between the assessors on applying the criteria was 0.89, with a kappa score >.86. Conclusions A small number of videos about physical examination of the cardiovascular and respiratory systems were identified as educationally useful; these videos can be used by medical students for independent learning and by clinical teachers as learning resources. The scoring system utilized by this study is simple, easy to apply, and could be used by other researchers on similar topics. PMID:24225171

Robust dynamic classes revealed by measuring the response function of a social system

PubMed Central

Crane, Riley; Sornette, Didier

2008-01-01

We study the relaxation response of a social system after endogenous and exogenous bursts of activity using the time series of daily views for nearly 5 million videos on YouTube. We find that most activity can be described accurately as a Poisson process. However, we also find hundreds of thousands of examples in which a burst of activity is followed by an ubiquitous power-law relaxation governing the timing of views. We find that these relaxation exponents cluster into three distinct classes and allow for the classification of collective human dynamics. This is consistent with an epidemic model on a social network containing two ingredients: a power-law distribution of waiting times between cause and action and an epidemic cascade of actions becoming the cause of future actions. This model is a conceptual extension of the fluctuation-dissipation theorem to social systems [Ruelle, D (2004) Phys Today 57:48–53] and [Roehner BM, et al., (2004) Int J Mod Phys C 15:809–834], and provides a unique framework for the investigation of timing in complex systems. PMID:18824681

Robust dynamic classes revealed by measuring the response function of a social system.

PubMed

Crane, Riley; Sornette, Didier

2008-10-14

We study the relaxation response of a social system after endogenous and exogenous bursts of activity using the time series of daily views for nearly 5 million videos on YouTube. We find that most activity can be described accurately as a Poisson process. However, we also find hundreds of thousands of examples in which a burst of activity is followed by an ubiquitous power-law relaxation governing the timing of views. We find that these relaxation exponents cluster into three distinct classes and allow for the classification of collective human dynamics. This is consistent with an epidemic model on a social network containing two ingredients: a power-law distribution of waiting times between cause and action and an epidemic cascade of actions becoming the cause of future actions. This model is a conceptual extension of the fluctuation-dissipation theorem to social systems [Ruelle, D (2004) Phys Today 57:48-53] and [Roehner BM, et al., (2004) Int J Mod Phys C 15:809-834], and provides a unique framework for the investigation of timing in complex systems.

A video wireless capsule endoscopy system powered wirelessly: design, analysis and experiment

NASA Astrophysics Data System (ADS)

Pan, Guobing; Xin, Wenhui; Yan, Guozheng; Chen, Jiaoliao

2011-06-01

Wireless capsule endoscopy (WCE), as a relatively new technology, has brought about a revolution in the diagnosis of gastrointestinal (GI) tract diseases. However, the existing WCE systems are not widely applied in clinic because of the low frame rate and low image resolution. A video WCE system based on a wireless power supply is developed in this paper. This WCE system consists of a video capsule endoscope (CE), a wireless power transmission device, a receiving box and an image processing station. Powered wirelessly, the video CE has the abilities of imaging the GI tract and transmitting the images wirelessly at a frame rate of 30 frames per second (f/s). A mathematical prototype was built to analyze the power transmission system, and some experiments were performed to test the capability of energy transferring. The results showed that the wireless electric power supply system had the ability to transfer more than 136 mW power, which was enough for the working of a video CE. In in vitro experiments, the video CE produced clear images of the small intestine of a pig with the resolution of 320 × 240, and transmitted NTSC format video outside the body. Because of the wireless power supply, the video WCE system with high frame rate and high resolution becomes feasible, and provides a novel solution for the diagnosis of the GI tract in clinic.

Developing Communication Competence Using an Online Video Reflection System: Pre-Service Teachers' Experiences

ERIC Educational Resources Information Center

Bower, Matt; Cavanagh, Michael; Moloney, Robyn; Dao, MingMing

2011-01-01

This paper reports on how the cognitive, behavioural and affective communication competencies of undergraduate students were developed using an online Video Reflection system. Pre-service teachers were provided with communication scenarios and asked to record short videos of one another making presentations. Students then uploaded their videos to…

77 FR 45376 - Certain Video Analytics Software, Systems, Components Thereof, and Products Containing Same...

Federal Register 2010, 2011, 2012, 2013, 2014

2012-07-31

... INTERNATIONAL TRADE COMMISSION [Investigation No. 337-TA-852] Certain Video Analytics Software... 337 of the Tariff Act of 1930, as amended, 19 U.S.C. 1337, on behalf of ObjectVideo, Inc. of Reston... sale within the United States after importation of certain video analytics software, systems...

Qualitative and quantitative assessment of video transmitted by DVTS (digital video transport system) in surgical telemedicine.

PubMed

Shima, Yoichiro; Suwa, Akina; Gomi, Yuichiro; Nogawa, Hiroki; Nagata, Hiroshi; Tanaka, Hiroshi

2007-01-01

Real-time video pictures can be transmitted inexpensively via a broadband connection using the DVTS (digital video transport system). However, the degradation of video pictures transmitted by DVTS has not been sufficiently evaluated. We examined the application of DVTS to remote consultation by using images of laparoscopic and endoscopic surgeries. A subjective assessment by the double stimulus continuous quality scale (DSCQS) method of the transmitted video pictures was carried out by eight doctors. Three of the four video recordings were assessed as being transmitted with no degradation in quality. None of the doctors noticed any degradation in the images due to encryption by the VPN (virtual private network) system. We also used an automatic picture quality assessment system to make an objective assessment of the same images. The objective DSCQS values were similar to the subjective ones. We conclude that although the quality of video pictures transmitted by the DVTS was slightly reduced, they were useful for clinical purposes. Encryption with a VPN did not degrade image quality.

Video Games: A Human Factors Guide to Visual Display Design and Instructional System Design

DTIC Science & Technology

1984-04-01

Electronic video games have many of the same technological and psychological characteristics that are found in military computer-based systems. For...both of which employ video games as experimental stimuli, are presented here. The first research program seeks to identify and exploit the...characteristics of video games in the design of game-based training devices. The second program is designed to explore the effects of electronic video display

A micro-Doppler sonar for acoustic surveillance in sensor networks

NASA Astrophysics Data System (ADS)

Zhang, Zhaonian

Wireless sensor networks have been employed in a wide variety of applications, despite the limited energy and communication resources at each sensor node. Low power custom VLSI chips implementing passive acoustic sensing algorithms have been successfully integrated into an acoustic surveillance unit and demonstrated for detection and location of sound sources. In this dissertation, I explore active and passive acoustic sensing techniques, signal processing and classification algorithms for detection and classification in a multinodal sensor network environment. I will present the design and characterization of a continuous-wave micro-Doppler sonar to image objects with articulated moving components. As an example application for this system, we use it to image gaits of humans and four-legged animals. I will present the micro-Doppler gait signatures of a walking person, a dog and a horse. I will discuss the resolution and range of this micro-Doppler sonar and use experimental results to support the theoretical analyses. In order to reduce the data rate and make the system amenable to wireless sensor networks, I will present a second micro-Doppler sonar that uses bandpass sampling for data acquisition. Speech recognition algorithms are explored for biometric identifications from one's gait, and I will present and compare the classification performance of the two systems. The acoustic micro-Doppler sonar design and biometric identification results are the first in the field as the previous work used either video camera or microwave technology. I will also review bearing estimation algorithms and present results of applying these algorithms for bearing estimation and tracking of moving vehicles. Another major source of the power consumption at each sensor node is the wireless interface. To address the need of low power communications in a wireless sensor network, I will also discuss the design and implementation of ultra wideband transmitters in a three dimensional silicon on insulator process. Lastly, a prototype of neuromorphic interconnects using ultra wideband radio will be presented.

Telesign: a videophone system for sign language distant communication

NASA Astrophysics Data System (ADS)

Mozelle, Gerard; Preteux, Francoise J.; Viallet, Jean-Emmanuel

1998-09-01

This paper presents a low bit rate videophone system for deaf people communicating by means of sign language. Classic video conferencing systems have focused on head and shoulders sequences which are not well-suited for sign language video transmission since hearing impaired people also use their hands and arms to communicate. To address the above-mentioned functionality, we have developed a two-step content-based video coding system based on: (1) A segmentation step. Four or five video objects (VO) are extracted using a cooperative approach between color-based and morphological segmentation. (2) VO coding are achieved by using a standardized MPEG-4 video toolbox. Results of encoded sign language video sequences, presented for three target bit rates (32 kbits/s, 48 kbits/s and 64 kbits/s), demonstrate the efficiency of the approach presented in this paper.

Dactyl Alphabet Gesture Recognition in a Video Sequence Using Microsoft Kinect

NASA Astrophysics Data System (ADS)

Artyukhin, S. G.; Mestetskiy, L. M.

2015-05-01

This paper presents an efficient framework for solving the problem of static gesture recognition based on data obtained from the web cameras and depth sensor Kinect (RGB-D - data). Each gesture given by a pair of images: color image and depth map. The database store gestures by it features description, genereated by frame for each gesture of the alphabet. Recognition algorithm takes as input a video sequence (a sequence of frames) for marking, put in correspondence with each frame sequence gesture from the database, or decide that there is no suitable gesture in the database. First, classification of the frame of the video sequence is done separately without interframe information. Then, a sequence of successful marked frames in equal gesture is grouped into a single static gesture. We propose a method combined segmentation of frame by depth map and RGB-image. The primary segmentation is based on the depth map. It gives information about the position and allows to get hands rough border. Then, based on the color image border is specified and performed analysis of the shape of the hand. Method of continuous skeleton is used to generate features. We propose a method of skeleton terminal branches, which gives the opportunity to determine the position of the fingers and wrist. Classification features for gesture is description of the position of the fingers relative to the wrist. The experiments were carried out with the developed algorithm on the example of the American Sign Language. American Sign Language gesture has several components, including the shape of the hand, its orientation in space and the type of movement. The accuracy of the proposed method is evaluated on the base of collected gestures consisting of 2700 frames.

Evaluation of video detection systems, volume 3 : effects of windy conditions in the performance of video detection systems.

DOT National Transportation Integrated Search

2008-05-01

The performance of three Video Detection Systems (VDS), namely Autoscope, Iteris, and Peek, was evaluated : at stop bar and advance locations, at an instrumented signalized intersection located in Rantoul, Illinois, utilizing : a side-by-side install...

Evaluation of video detection systems, volume 2 : effects of illumination conditions in the performance of video detection systems.

DOT National Transportation Integrated Search

2009-05-01

The evaluation of three Video Detection Systems (VDS) at an instrumented signalized intersection in Rantoul : Illinois, at both stop bar and advance detection zones, was performed under a wide range of lighting and : weather conditions. The evaluated...

77 FR 75617 - 36(b)(1) Arms Sales Notification

Federal Register 2010, 2011, 2012, 2013, 2014

2012-12-21

... transmittal, policy justification, and Sensitivity of Technology. Dated: December 18, 2012. Aaron Siegel... Processor Cabinets, 2 Video Wall Screen and Projector Systems, 46 Flat Panel Displays, and 2 Distributed Video Systems), 2 ship sets AN/SPQ-15 Digital Video Distribution Systems, 2 ship sets Operational...

An intelligent crowdsourcing system for forensic analysis of surveillance video

NASA Astrophysics Data System (ADS)

Tahboub, Khalid; Gadgil, Neeraj; Ribera, Javier; Delgado, Blanca; Delp, Edward J.

2015-03-01

Video surveillance systems are of a great value for public safety. With an exponential increase in the number of cameras, videos obtained from surveillance systems are often archived for forensic purposes. Many automatic methods have been proposed to do video analytics such as anomaly detection and human activity recognition. However, such methods face significant challenges due to object occlusions, shadows and scene illumination changes. In recent years, crowdsourcing has become an effective tool that utilizes human intelligence to perform tasks that are challenging for machines. In this paper, we present an intelligent crowdsourcing system for forensic analysis of surveillance video that includes the video recorded as a part of search and rescue missions and large-scale investigation tasks. We describe a method to enhance crowdsourcing by incorporating human detection, re-identification and tracking. At the core of our system, we use a hierarchal pyramid model to distinguish the crowd members based on their ability, experience and performance record. Our proposed system operates in an autonomous fashion and produces a final output of the crowdsourcing analysis consisting of a set of video segments detailing the events of interest as one storyline.

HealthRecSys: A semantic content-based recommender system to complement health videos.

PubMed

Sanchez Bocanegra, Carlos Luis; Sevillano Ramos, Jose Luis; Rizo, Carlos; Civit, Anton; Fernandez-Luque, Luis

2017-05-15

The Internet, and its popularity, continues to grow at an unprecedented pace. Watching videos online is very popular; it is estimated that 500 h of video are uploaded onto YouTube, a video-sharing service, every minute and that, by 2019, video formats will comprise more than 80% of Internet traffic. Health-related videos are very popular on YouTube, but their quality is always a matter of concern. One approach to enhancing the quality of online videos is to provide additional educational health content, such as websites, to support health consumers. This study investigates the feasibility of building a content-based recommender system that links health consumers to reputable health educational websites from MedlinePlus for a given health video from YouTube. The dataset for this study includes a collection of health-related videos and their available metadata. Semantic technologies (such as SNOMED-CT and Bio-ontology) were used to recommend health websites from MedlinePlus. A total of 26 healths professionals participated in evaluating 253 recommended links for a total of 53 videos about general health, hypertension, or diabetes. The relevance of the recommended health websites from MedlinePlus to the videos was measured using information retrieval metrics such as the normalized discounted cumulative gain and precision at K. The majority of websites recommended by our system for health videos were relevant, based on ratings by health professionals. The normalized discounted cumulative gain was between 46% and 90% for the different topics. Our study demonstrates the feasibility of using a semantic content-based recommender system to enrich YouTube health videos. Evaluation with end-users, in addition to healthcare professionals, will be required to identify the acceptance of these recommendations in a nonsimulated information-seeking context.

A new method for digital video documentation in surgical procedures and minimally invasive surgery.

PubMed

Wurnig, P N; Hollaus, P H; Wurnig, C H; Wolf, R K; Ohtsuka, T; Pridun, N S

2003-02-01

Documentation of surgical procedures is limited to the accuracy of description, which depends on the vocabulary and the descriptive prowess of the surgeon. Even analog video recording could not solve the problem of documentation satisfactorily due to the abundance of recorded material. By capturing the video digitally, most problems are solved in the circumstances described in this article. We developed a cheap and useful digital video capturing system that consists of conventional computer components. Video images and clips can be captured intraoperatively and are immediately available. The system is a commercial personal computer specially configured for digital video capturing and is connected by wire to the video tower. Filming was done with a conventional endoscopic video camera. A total of 65 open and endoscopic procedures were documented in an orthopedic and a thoracic surgery unit. The median number of clips per surgical procedure was 6 (range, 1-17), and the median storage volume was 49 MB (range, 3-360 MB) in compressed form. The median duration of a video clip was 4 min 25 s (range, 45 s to 21 min). Median time for editing a video clip was 12 min for an advanced user (including cutting, title for the movie, and compression). The quality of the clips renders them suitable for presentations. This digital video documentation system allows easy capturing of intraoperative video sequences in high quality. All possibilities of documentation can be performed. With the use of an endoscopic video camera, no compromises with respect to sterility and surgical elbowroom are necessary. The cost is much lower than commercially available systems, and setting changes can be performed easily without trained specialists.

Seeing Tornado: How Video Traces Mediate Visitor Understandings of (Natural?) Phenomena in a Science Museum.

ERIC Educational Resources Information Center

Stevens, Reed; Hall, Rogers

1997-01-01

Reports on an exploratory study of how people see and explain a prominent exhibit (Tornado) at an interactive science museum (the Exploratorium). Data was assembled using a novel, technically mediated activity system (Video Traces). Argues that Video Traces is an effective tool and discusses an expanded Video Traces system. (Author/DKM)

Content-based management service for medical videos.

PubMed

Mendi, Engin; Bayrak, Coskun; Cecen, Songul; Ermisoglu, Emre

2013-01-01

Development of health information technology has had a dramatic impact to improve the efficiency and quality of medical care. Developing interoperable health information systems for healthcare providers has the potential to improve the quality and equitability of patient-centered healthcare. In this article, we describe an automated content-based medical video analysis and management service that provides convenience and ease in accessing the relevant medical video content without sequential scanning. The system facilitates effective temporal video segmentation and content-based visual information retrieval that enable a more reliable understanding of medical video content. The system is implemented as a Web- and mobile-based service and has the potential to offer a knowledge-sharing platform for the purpose of efficient medical video content access.

Individual recognition based on communication behaviour of male fowl.

PubMed

Smith, Carolynn L; Taubert, Jessica; Weldon, Kimberly; Evans, Christopher S

2016-04-01

Correctly directing social behaviour towards a specific individual requires an ability to discriminate between conspecifics. The mechanisms of individual recognition include phenotype matching and familiarity-based recognition. Communication-based recognition is a subset of familiarity-based recognition wherein the classification is based on behavioural or distinctive signalling properties. Male fowl (Gallus gallus) produce a visual display (tidbitting) upon finding food in the presence of a female. Females typically approach displaying males. However, males may tidbit without food. We used the distinctiveness of the visual display and the unreliability of some males to test for communication-based recognition in female fowl. We manipulated the prior experience of the hens with the males to create two classes of males: S(+) wherein the tidbitting signal was paired with a food reward to the female, and S (-) wherein the tidbitting signal occurred without food reward. We then conducted a sequential discrimination test with hens using a live video feed of a familiar male. The results of the discrimination tests revealed that hens discriminated between categories of males based on their signalling behaviour. These results suggest that fowl possess a communication-based recognition system. This is the first demonstration of live-to-video transfer of recognition in any species of bird. Copyright © 2016 Elsevier B.V. All rights reserved.

Diagnosis of Epilepsy and Related Episodic Disorders.

PubMed

St Louis, Erik K; Cascino, Gregory D

2016-02-01

This review identifies the diverse and variable clinical presentations associated with epilepsy that may create challenges in diagnosis and treatment. Epilepsy has recently been redefined as a disease characterized by one or more seizures with a relatively high recurrence risk (ie, 60% or greater likelihood). The implication of this definition for therapy is that antiepileptic drug therapy may be initiated following a first seizure in certain situations.EEG remains the most commonly used study in the evaluation of people with epilepsy. Routine EEG may assist in diagnosis, classification of seizure type(s), identification of treatment, and monitoring the efficacy of therapy. Video-EEG monitoring permits seizure classification, assessment of psychogenic nonepileptic seizures, and evaluation of candidacy for epilepsy surgery. MRI is pivotal in elucidating the etiology of the seizure disorder and in suggesting the localization of seizure onset. This article reviews the new International League Against Epilepsy practical clinical definition for epilepsy and the differential diagnosis of other physiologic paroxysmal spells, including syncope, parasomnias, transient ischemic attacks, and migraine, as well as psychogenic nonepileptic seizures. The initial investigational approaches to new-onset epilepsy are considered, including neuroimaging and neurophysiologic investigations with interictal and ictal video-EEG. Neurologists should maintain a high index of suspicion for epilepsy when children or adults present with a single paroxysmal spell or recurrent episodic events.

Optimized spatio-temporal descriptors for real-time fall detection: comparison of support vector machine and Adaboost-based classification

NASA Astrophysics Data System (ADS)

Charfi, Imen; Miteran, Johel; Dubois, Julien; Atri, Mohamed; Tourki, Rached

2013-10-01

We propose a supervised approach to detect falls in a home environment using an optimized descriptor adapted to real-time tasks. We introduce a realistic dataset of 222 videos, a new metric allowing evaluation of fall detection performance in a video stream, and an automatically optimized set of spatio-temporal descriptors which fed a supervised classifier. We build the initial spatio-temporal descriptor named STHF using several combinations of transformations of geometrical features (height and width of human body bounding box, the user's trajectory with her/his orientation, projection histograms, and moments of orders 0, 1, and 2). We study the combinations of usual transformations of the features (Fourier transform, wavelet transform, first and second derivatives), and we show experimentally that it is possible to achieve high performance using support vector machine and Adaboost classifiers. Automatic feature selection allows to show that the best tradeoff between classification performance and processing time is obtained by combining the original low-level features with their first derivative. Hence, we evaluate the robustness of the fall detection regarding location changes. We propose a realistic and pragmatic protocol that enables performance to be improved by updating the training in the current location with normal activities records.

Remote video assessment for missile launch facilities

DOE Office of Scientific and Technical Information (OSTI.GOV)

Wagner, G.G.; Stewart, W.A.

1995-07-01

The widely dispersed, unmanned launch facilities (LFs) for land-based ICBMs (intercontinental ballistic missiles) currently do not have visual assessment capability for existing intrusion alarms. The security response force currently must assess each alarm on-site. Remote assessment will enhance manpower, safety, and security efforts. Sandia National Laboratories was tasked by the USAF Electronic Systems Center to research, recommend, and demonstrate a cost-effective remote video assessment capability at missile LFs. The project`s charter was to provide: system concepts; market survey analysis; technology search recommendations; and operational hardware demonstrations for remote video assessment from a missile LF to a remote security center viamore » a cost-effective transmission medium and without using visible, on-site lighting. The technical challenges of this project were to: analyze various video transmission media and emphasize using the existing missile system copper line which can be as long as 30 miles; accentuate and extremely low-cost system because of the many sites requiring system installation; integrate the video assessment system with the current LF alarm system; and provide video assessment at the remote sites with non-visible lighting.« less

Video performance for high security applications.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Connell, Jack C.; Norman, Bradley C.

2010-06-01

The complexity of physical protection systems has increased to address modern threats to national security and emerging commercial technologies. A key element of modern physical protection systems is the data presented to the human operator used for rapid determination of the cause of an alarm, whether false (e.g., caused by an animal, debris, etc.) or real (e.g., a human adversary). Alarm assessment, the human validation of a sensor alarm, primarily relies on imaging technologies and video systems. Developing measures of effectiveness (MOE) that drive the design or evaluation of a video system or technology becomes a challenge, given the subjectivitymore » of the application (e.g., alarm assessment). Sandia National Laboratories has conducted empirical analysis using field test data and mathematical models such as binomial distribution and Johnson target transfer functions to develop MOEs for video system technologies. Depending on the technology, the task of the security operator and the distance to the target, the Probability of Assessment (PAs) can be determined as a function of a variety of conditions or assumptions. PAs used as an MOE allows the systems engineer to conduct trade studies, make informed design decisions, or evaluate new higher-risk technologies. This paper outlines general video system design trade-offs, discusses ways video can be used to increase system performance and lists MOEs for video systems used in subjective applications such as alarm assessment.« less

Objective video presentation QoE predictor for smart adaptive video streaming

NASA Astrophysics Data System (ADS)

Wang, Zhou; Zeng, Kai; Rehman, Abdul; Yeganeh, Hojatollah; Wang, Shiqi

2015-09-01

How to deliver videos to consumers over the network for optimal quality-of-experience (QoE) has been the central goal of modern video delivery services. Surprisingly, regardless of the large volume of videos being delivered everyday through various systems attempting to improve visual QoE, the actual QoE of end consumers is not properly assessed, not to say using QoE as the key factor in making critical decisions at the video hosting, network and receiving sites. Real-world video streaming systems typically use bitrate as the main video presentation quality indicator, but using the same bitrate to encode different video content could result in drastically different visual QoE, which is further affected by the display device and viewing condition of each individual consumer who receives the video. To correct this, we have to put QoE back to the driver's seat and redesign the video delivery systems. To achieve this goal, a major challenge is to find an objective video presentation QoE predictor that is accurate, fast, easy-to-use, display device adaptive, and provides meaningful QoE predictions across resolution and content. We propose to use the newly developed SSIMplus index (https://ece.uwaterloo.ca/~z70wang/research/ssimplus/) for this role. We demonstrate that based on SSIMplus, one can develop a smart adaptive video streaming strategy that leads to much smoother visual QoE impossible to achieve using existing adaptive bitrate video streaming approaches. Furthermore, SSIMplus finds many more applications, in live and file-based quality monitoring, in benchmarking video encoders and transcoders, and in guiding network resource allocations.

Technical and economic feasibility of integrated video service by satellite

NASA Technical Reports Server (NTRS)

Price, K. M.; Kwan, R. K.; White, L. W.; Garlow, R. K.; Henderson, T. R.

1992-01-01

A feasibility study is presented of utilizing modern satellite technology, or more advanced technology, to create a cost-effective, user-friendly, integrated video service, which can provide videophone, video conference, or other equivalent wideband service on demand. A system is described that permits a user to select a desired audience and establish the required links similar to arranging a teleconference by phone. Attention is given to video standards, video traffic scenarios, satellite system architecture, and user costs.

A multi-modal approach for activity classification and fall detection

NASA Astrophysics Data System (ADS)

Castillo, José Carlos; Carneiro, Davide; Serrano-Cuerda, Juan; Novais, Paulo; Fernández-Caballero, Antonio; Neves, José

2014-04-01

The society is changing towards a new paradigm in which an increasing number of old adults live alone. In parallel, the incidence of conditions that affect mobility and independence is also rising as a consequence of a longer life expectancy. In this paper, the specific problem of falls of old adults is addressed by devising a technological solution for monitoring these users. Video cameras, accelerometers and GPS sensors are combined in a multi-modal approach to monitor humans inside and outside the domestic environment. Machine learning techniques are used to detect falls and classify activities from accelerometer data. Video feeds and GPS are used to provide location inside and outside the domestic environment. It results in a monitoring solution that does not imply the confinement of the users to a closed environment.

Architecture and Protocol of a Semantic System Designed for Video Tagging with Sensor Data in Mobile Devices

PubMed Central

Macias, Elsa; Lloret, Jaime; Suarez, Alvaro; Garcia, Miguel

2012-01-01

Current mobile phones come with several sensors and powerful video cameras. These video cameras can be used to capture good quality scenes, which can be complemented with the information gathered by the sensors also embedded in the phones. For example, the surroundings of a beach recorded by the camera of the mobile phone, jointly with the temperature of the site can let users know via the Internet if the weather is nice enough to swim. In this paper, we present a system that tags the video frames of the video recorded from mobile phones with the data collected by the embedded sensors. The tagged video is uploaded to a video server, which is placed on the Internet and is accessible by any user. The proposed system uses a semantic approach with the stored information in order to make easy and efficient video searches. Our experimental results show that it is possible to tag video frames in real time and send the tagged video to the server with very low packet delay variations. As far as we know there is not any other application developed as the one presented in this paper. PMID:22438753

Architecture and protocol of a semantic system designed for video tagging with sensor data in mobile devices.

PubMed

Macias, Elsa; Lloret, Jaime; Suarez, Alvaro; Garcia, Miguel

2012-01-01

Current mobile phones come with several sensors and powerful video cameras. These video cameras can be used to capture good quality scenes, which can be complemented with the information gathered by the sensors also embedded in the phones. For example, the surroundings of a beach recorded by the camera of the mobile phone, jointly with the temperature of the site can let users know via the Internet if the weather is nice enough to swim. In this paper, we present a system that tags the video frames of the video recorded from mobile phones with the data collected by the embedded sensors. The tagged video is uploaded to a video server, which is placed on the Internet and is accessible by any user. The proposed system uses a semantic approach with the stored information in order to make easy and efficient video searches. Our experimental results show that it is possible to tag video frames in real time and send the tagged video to the server with very low packet delay variations. As far as we know there is not any other application developed as the one presented in this paper.

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

NASA Astrophysics Data System (ADS)

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.

Evaluation of smart video for transit event detection : final report.

DOT National Transportation Integrated Search

2009-06-01

Transit agencies are increasingly using video cameras to fight crime and terrorism. As the volume of video data increases, the existing digital video surveillance systems provide the infrastructure only to capture, store and distribute video, while l...

77 FR 1726 - Investigations: Terminations, Modifications and Rulings: Certain Video Game Systems and Controllers

Federal Register 2010, 2011, 2012, 2013, 2014

2012-01-11

... INTERNATIONAL TRADE COMMISSION [Investigation No. 337-TA-743] Investigations: Terminations, Modifications and Rulings: Certain Video Game Systems and Controllers AGENCY: U.S. International Trade... video game systems and controllers by reason of infringement of claims 16, 27-32, 44, 57, 68, 81, and 84...

77 FR 58577 - Certain Video Game Systems and Wireless Controllers and Components Thereof; Notice of Request for...

Federal Register 2010, 2011, 2012, 2013, 2014

2012-09-21

... INTERNATIONAL TRADE COMMISSION [Investigation No. 337-TA-770] Certain Video Game Systems and Wireless Controllers and Components Thereof; Notice of Request for Statements on the Public Interest AGENCY... desist order against certain video game systems and wireless controllers and components thereof, imported...

Evaluation of video detection systems, volume 4 : effects of adverse weather conditions in the performance of video detection systems.

DOT National Transportation Integrated Search

2009-03-01

The performance of three video detection systems (VDS): Iteris, Autoscope, and Peek, was evaluated : using a side-by-side installation at a signalized intersection under various adverse weather conditions including : rain and snow in both day and nig...

Evaluation of video detection systems, volume 1 : effects of configuration changes in the performance of video detection systems.

DOT National Transportation Integrated Search

2009-10-01

The effects of modifying the configuration of three video detection (VD) systems (Iteris, Autoscope, and Peek) : are evaluated in daytime and nighttime conditions. Four types of errors were used: false, missed, stuck-on, and : dropped calls. The thre...

Recent Developments in Interactive and Communicative CALL: Hypermedia and "Intelligent" Systems.

ERIC Educational Resources Information Center

Coughlin, Josette M.

Two recent developments in computer-assisted language learning (CALL), interactive video systems and "intelligent" games, are discussed. Under the first heading, systems combining the use of a computer and video disc player are described, and Compact Discs Interactive (CDI) and Digital Video Interactive (DVI) are reviewed. The…

SU-E-J-196: Implementation of An In-House Visual Feedback System for Motion Management During Radiation Therapy

DOE Office of Scientific and Technical Information (OSTI.GOV)

Nguyen, V; James, J; Wang, B

Purpose: To describe an in-house video goggle feedback system for motion management during simulation and treatment of radiation therapy patients. Methods: This video goggle system works by splitting and amplifying the video output signal directly from the Varian Real-Time Position Management (RPM) workstation or TrueBeam imaging workstation into two signals using a Distribution Amplifier. The first signal S[1] gets reconnected back to the monitor. The second signal S[2] gets connected to the input of a Video Scaler. The S[2] signal can be scaled, cropped and panned in real time to display only the relevant information to the patient. The outputmore » signal from the Video Scaler gets connected to an HDMI Extender Transmitter via a DVI-D to HDMI converter cable. The S[2] signal can be transported from the HDMI Extender Transmitter to the HDMI Extender Receiver located inside the treatment room via a Cat5e/6 cable. Inside the treatment room, the HDMI Extender Receiver is permanently mounted on the wall near the conduit where the Cat5e/6 cable is located. An HDMI cable is used to connect from the output of the HDMI Receiver to the video goggles. Results: This video goggle feedback system is currently being used at two institutions. At one institution, the system was just recently implemented for simulation and treatments on two breath-hold gated patients with 8+ total fractions over a two month period. At the other institution, the system was used to treat 100+ breath-hold gated patients on three Varian TrueBeam linacs and has been operational for twelve months. The average time to prepare the video goggle system for treatment is less than 1 minute. Conclusion: The video goggle system provides an efficient and reliable method to set up a video feedback signal for radiotherapy patients with motion management.« less