Download or read book QUALITATIVE MOTION ANALYSIS USING A SPATIO TEMPORAL APPROACH COMPUTER VISION EDGE DETECTION written by SHIH-PING LIOU and published by . This book was released on 1990 with total page 196 pages. Available in PDF, EPUB and Kindle. Book excerpt: qualitative motion information of various kinds can be obtained from image sequences easily and reliably.

Download or read book Computer Vision ECCV 2000 written by David Vernon and published by Springer Science & Business Media. This book was released on 2000-06-19 with total page 881 pages. Available in PDF, EPUB and Kindle. Book excerpt: The two-volume set LNCS 1842/1843 constitutes the refereed proceedings of the 6th European Conference on Computer Vision, ECCV 2000, held in Dublin, Ireland in June/July 2000. The 116 revised full papers presented were carefully selected from a total of 266 submissions. The two volumes offer topical sections on recognitions and modelling; stereoscopic vision; texture and shading; shape; structure from motion; image features; active, real-time, and robot vision; segmentation and grouping; vision systems engineering and evaluation; calibration; medical image understanding; and visual motion.

Download or read book Qualitative Motion Understanding written by Wilhelm Burger and published by Springer Science & Business Media. This book was released on 1992-06-30 with total page 234 pages. Available in PDF, EPUB and Kindle. Book excerpt: Mobile robots operating in real-world, outdoor scenarios depend on dynamic scene understanding for detecting and avoiding obstacles, recognizing landmarks, acquiring models, and for detecting and tracking moving objects. Motion understanding has been an active research effort for more than a decade, searching for solutions to some of these problems; however, it still remains one of the more difficult and challenging areas of computer vision research. Qualitative Motion Understanding describes a qualitative approach to dynamic scene and motion analysis, called DRIVE (Dynamic Reasoning from Integrated Visual Evidence). The DRIVE system addresses the problems of (a) estimating the robot's egomotion, (b) reconstructing the observed 3-D scene structure; and (c) evaluating the motion of individual objects from a sequence of monocular images. The approach is based on the FOE (focus of expansion) concept, but it takes a somewhat unconventional route. The DRIVE system uses a qualitative scene model and a fuzzy focus of expansion to estimate robot motion from visual cues, to detect and track moving objects, and to construct and maintain a global dynamic reference model.

Download or read book Qualitative Spatial Properties of Scenes Using Motion Information written by Kathleen Mary Mutch and published by . This book was released on 1983 with total page 276 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Edge Contour Tracking in Image Sequences by Analysis of Spatiotemporal Connectivity written by Kadri Nizar Jabri and published by . This book was released on 1994 with total page 160 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Spatio temporal Visual Information Analysis for Moving Object Detection and Retrieval in Video Sequences written by Dianting Liu and published by . This book was released on 2013 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: The development of the Internet makes the number of online videos increase dramatically, which brings new demands to the video search engines for automatic retrieval and classification. We propose an unsupervised moving object detection and retrieval framework by exploiting and analyzing spatio-temporal visual information in the video sequences. The motivation is to use visual content information to estimate the locations of the moving objects in the spatio-temporal domain. Compared with the existing approaches, our proposed detection algorithm is unsupervised. It does not need to train models for specific objects. Furthermore, it is suitable for the detection of unknown objects. Therefore, after object detection, the object-level features can be extracted for video retrieval. The proposed moving object detection algorithm consists of two layers: global motion estimation layer and local motion estimation layer. The two layers explore and estimate motion information from different scopes in the spatio-temporal domain. The global motion estimation layer uses a temporal-centered estimation method to obtain a preliminary region of motion. Specially, it analyzes the motion in the temporal domain by using our proposed novel motion representation method called the weighted histogram of Harris3D volume which combines the optical flow field and Harris3D corner detector to obtain a good spatio-temporal estimation in the video sequences. The idea is motivated by taking advantages of the two sources of motion knowledge identified by different methods to get a complementary motion data to be kept in the new motion representation. The method, considering integrated motion information, works well with the dynamic background and camera motion, and demonstrates the advantages of integrating multiple spatio-temporal cues in the proposed framework. In addition, a center-surround coherency evaluation model is proposed to compute the local motion saliency and weight the spatio-temporal motion to find the region of a moving object by the integral density algorithm. The global motion estimation layer passes the preliminary region of motion to the local motion estimation layer. The latter uses a spatial-centered estimation method to integrate visual information spatially in adjacent frames to obtain the region of the moving object. The visual information in the frame is analyzed to find visual key locations which are defined as the maxima and minima of the result of the difference-of-Gaussian function. A motion map of adjacent frames is obtained to represent the temporal information from the differences of the outcomes from the simultaneous partition and class parameter estimation (SPCPE) framework. The motion map filters visual key locations into key motion locations (KMLs) where the existence of the moving object is implied. The integral density method is employed to find the region with the highest density of KMLs as the moving object. The features extracted from the motion region are used to train the global Gaussian mixture models for the video representation. The representation significantly reduces the classification model training time in comparison to the time needed when the whole feature sets are used. It also achieves better classification performance. When combined with the information of scenes, the performance is further enhanced. Besides the proposed spatio-temporal object detection work, two other related methods are also proposed since they play subsidiary roles in the detection model. One is the innovative key frame detection method which selects representative frames as the key frames to provide key locations in the spatial-centered estimation method. By analyzing the visual differences between frames and utilizing the clustering technique, a set of key frame candidates is first selected at the shot level, and then the information within a video shot and between video shots is used to adaptively filter the candidate set to generate the final set of key frames for spatial motion analysis. Another new method is to segment and track two objects under occlusion situations, which is useful in multiple object detection scenarios.

Download or read book High Orders Motion Analysis written by Yan Sun and published by Springer Nature. This book was released on with total page 90 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Scientific and Technical Aerospace Reports written by and published by . This book was released on 1994 with total page 836 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Proceedings of the 5th International Conference on Frontiers in Intelligent Computing Theory and Applications written by Suresh Chandra Satapathy and published by Springer. This book was released on 2017-03-02 with total page 660 pages. Available in PDF, EPUB and Kindle. Book excerpt: The book is a collection of high-quality peer-reviewed research papers presented at International Conference on Frontiers of Intelligent Computing: Theory and applications (FICTA 2016) held at School of Computer Engineering, KIIT University, Bhubaneswar, India during 16 - 17 September 2016. The book aims to present theories, methodologies, new ideas, experiences, applications in all areas of intelligent computing and its applications to various engineering disciplines like computer science, electronics, electrical, mechanical engineering, etc.

Download or read book Special Issue on Spatiotemporal Coherence for Visual Motion Analysis written by W. James MacLean and published by . This book was released on 2007 with total page 78 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Machine Learning for Vision Based Motion Analysis written by Liang Wang and published by Springer. This book was released on 2011-04-08 with total page 372 pages. Available in PDF, EPUB and Kindle. Book excerpt: Techniques of vision-based motion analysis aim to detect, track, identify, and generally understand the behavior of objects in image sequences. With the growth of video data in a wide range of applications from visual surveillance to human-machine interfaces, the ability to automatically analyze and understand object motions from video footage is of increasing importance. Among the latest developments in this field is the application of statistical machine learning algorithms for object tracking, activity modeling, and recognition. Developed from expert contributions to the first and second International Workshop on Machine Learning for Vision-Based Motion Analysis, this important text/reference highlights the latest algorithms and systems for robust and effective vision-based motion understanding from a machine learning perspective. Highlighting the benefits of collaboration between the communities of object motion understanding and machine learning, the book discusses the most active forefronts of research, including current challenges and potential future directions. Topics and features: provides a comprehensive review of the latest developments in vision-based motion analysis, presenting numerous case studies on state-of-the-art learning algorithms; examines algorithms for clustering and segmentation, and manifold learning for dynamical models; describes the theory behind mixed-state statistical models, with a focus on mixed-state Markov models that take into account spatial and temporal interaction; discusses object tracking in surveillance image streams, discriminative multiple target tracking, and guidewire tracking in fluoroscopy; explores issues of modeling for saliency detection, human gait modeling, modeling of extremely crowded scenes, and behavior modeling from video surveillance data; investigates methods for automatic recognition of gestures in Sign Language, and human action recognition from small training sets. Researchers, professional engineers, and graduate students in computer vision, pattern recognition and machine learning, will all find this text an accessible survey of machine learning techniques for vision-based motion analysis. The book will also be of interest to all who work with specific vision applications, such as surveillance, sport event analysis, healthcare, video conferencing, and motion video indexing and retrieval.

Download or read book Computer Vision Research Progress written by Zhongkai Zhu and published by Nova Publishers. This book was released on 2008 with total page 358 pages. Available in PDF, EPUB and Kindle. Book excerpt: Computer vision is the science and technology of machines that see. As a scientific discipline, computer vision is concerned with the theory and technology for building artificial systems that obtain information from images. The image data can take many forms, such as a video sequence, views from multiple cameras, or multi-dimensional data from a medical scanner. As a technological discipline, computer vision seeks to apply the theories and models of computer vision to the construction of computer vision systems. Examples of applications of computer vision systems include systems for controlling processes (e.g. an industrial robot or an autonomous vehicle). Detecting events (e.g. for visual surveillance). Organizing information (e.g. for indexing databases of images and image sequences), Modeling objects or environments (e.g. industrial inspection, medical image analysis or topographical modeling), Interaction (e.g. as the input to a device for computer-human interaction). Computer vision can also be described as a complement (but not necessarily the opposite) of biological vision. In biological vision, the visual perception of humans and various animals are studied, resulting in models of how these systems operate in terms of physiological processes. Computer vision, on the other hand, studies and describes artificial vision system that are implemented in software and/or hardware. Interdisciplinary exchange between biological and computer vision has proven increasingly fruitful for both fields. Sub-domains of computer vision include scene reconstruction, event detection, tracking, object recognition, learning, indexing, ego-motion and image restoration. This new book presents leading-edge new research from around the world.

Download or read book Intelligent Video Event Analysis and Understanding written by Jianguo Zhang and published by . This book was released on 2010 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: With the vast development of Internet and media technologies, intelligent video event analysis is a gradually growing field of research in recent decades. Significant challenges include how to handle background clutter, occlusions, as well as interactions. Another difficulty is the lacking of widely accepted definition of event in the literature. Though the research achievement is still far from its promise, steady progress has been made in past years. This book collects a set of selected contributions in this area from international experts including leading academic researchers, and industrial practitioners. It presents the latest advances of intelligent video event analysis in both theoretical and application viewpoints. Topics and features: · - Addresses the concept of events by introducing a double view of understanding meaningful events in gesture based interaction · - Investigates motion segmentation based on the subspace technique by incorporating the cues from the neighbourhood of intensity edges of images · - Provides the state of the art techniques on human action description, and recognition based on 3D spatial temporal features · - Presents efficient object localization and detection approaches in challenging scenes · - Describes motion analysis techniques in various applications including sports videos, household environment, and surveillance videos It provides researchers and practitioners a rich resource for future research directions and successful practice. It could also serve as a reference tool and handbook for researchers in a number of applications including visual surveillance, human-computer interaction, and video search and indexing, etc. Graduate students working on video analysis in various disciplines such as computer vision, pattern recognition, information security, artificial intelligence will also find it useful.

Download or read book Topic Modeling for Discovering Spatio temporal Relationships in Motion Patterns written by Dalwinderjeet Kaur Kular and published by . This book was released on 2015 with total page 242 pages. Available in PDF, EPUB and Kindle. Book excerpt: Understanding spatio-ternporal relationships among motion patterns in a video is a key problem in computer vision with applications such as scene understanding and analysis, human-action classification, and facial expression recognition. The problem is challenging because of the noisy nature of low-level motion features and the complexities of collective dynamics of multiple activities. To perform higher-level reasoning concerning activities in a video, algorithms need to identify both spatial and temporal factors. In this work we propose to identify spatial and temporal relationships in motion patterns by probabilistic topic modeling and Granger causality. Two main approaches are presented. First, we combine probabilistic topic modeling with Granger causality. Second, we focus on inter-relationship among spatially co-occurring motion patterns using the relational topic model approach. Our experiments demonstrate that our methods discover relevant motion patterns by learning spatial patterns and their temporal relationships.

Download or read book Transform Methods for the Spatio temporal Analysis of Visual Motion written by Philippe Burlina and published by . This book was released on 1994 with total page 31 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Analysis of Human centric Activities in Video Via Qualitative Spatio temporal Reasoning written by Hajar Sadeghi Sokeh and published by . This book was released on 2015 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Applying qualitative spatio-temporal reasoning in video analysis is now a very active research topic in computer vision and artificial intelligence. Among all video analysis applications, monitoring and understanding human activities is of great interest. Many human activities can be understood by analysing the interaction between objects in space and time. Qualitative spatio-temporal reasoning encapsulates information that is useful for analysing huma-centric videos. This information can be represented in a very compact form involving interactions between objects of interest in the form of qualitative spatio-temporal relationships. This thesis focuses on three different aspects of interpreting human-centric videos; first introducing a representation of interactions between objects of interest, second determining which objects in the scene are relevant to the activity, and third recognising of human actions by applying the proposed representation model between human body joints and body parts. As a first contribution, we present an accurate and comprehensive model for representing several aspects of space over time from videos called "AngledCORE-9", a modified version of CORE-9 (proposed by Cohn et al. [2012]). This model is as efficient as CORE-9 and allows us to extract spatial information with much higher accuracy than previously possible. We evaluate our new knowledge representation method on a real video dataset to perform action clustering. Our next contribution is proposing a model for differentiating relevant from irrelevant objects to the human actions in the videos. The chief issue of recognising different human actions in videos using spatio-temporal features is that there are usually many moving objects in the scene. No existing method can successfully find the involved objects in the activity. The output of our system is a list of tracks for all possible objects in the video with their probabilities for being involved in the activity. The track with the highest probability is most likely to be the object with which the person is interacting. Knowing the involved object(s) in the activities is very advantageous. Since it can be used to improve the human action recognition rate. Finally, instead of looking at human-object interactions, we consider skeleton joints as the points of interest. Working on joints provides more information about how a person is moving to perform the activity. In this part of the thesis, we use videos with human skeletons in 3D captured by Kinect, MSR3D-action dataset. We use our proposed model "AngledCORE-9" to extract features and describe the temporal variation of these features frame by frame. We compare our results against some of the recent works on the same dataset.

Download or read book Computer Vision in Control Systems 3 written by Margarita N. Favorskaya and published by Springer. This book was released on 2017-10-25 with total page 351 pages. Available in PDF, EPUB and Kindle. Book excerpt: The research book is a continuation of the authors’ previous works, which are focused on recent advances in computer vision methodologies and technical solutions using conventional and intelligent paradigms. The book gathers selected contributions addressing aerial and satellite image processing and related fields. Topics covered include novel tensor and wave models, a new comparative morphology scheme, warping compensation in video stabilization, image deblurring based on physical processes of blur impacts, and a rapid and robust core structural verification algorithm for feature extraction in images and videos, among others. All chapters focus on practical implementations. Given the tremendous interest among researchers in the development and applications of computer vision paradigms in the field of business, engineering, medicine, security and aviation, this book offers a timely guide.