LEADER 06367nam 22007695 450 001 996465837203316 005 20200701194655.0 010 $a1-280-86452-4 010 $a9786610864522 010 $a3-540-70932-0 024 7 $a10.1007/978-3-540-70932-9 035 $a(CKB)1000000000284087 035 $a(SSID)ssj0000294429 035 $a(PQKBManifestationID)11265887 035 $a(PQKBTitleCode)TC0000294429 035 $a(PQKBWorkID)10312155 035 $a(PQKB)10949962 035 $a(DE-He213)978-3-540-70932-9 035 $a(MiAaPQ)EBC3036651 035 $a(MiAaPQ)EBC6283011 035 $a(PPN)123160332 035 $a(EXLCZ)991000000000284087 100 $a20100301d2007 u| 0 101 0 $aeng 135 $aurnn|008mamaa 181 $ctxt 182 $cc 183 $acr 200 10$aDynamical Vision$b[electronic resource] $eICCV 2005 and ECCV 2006 Workshops, WDV 2005 and WDV 2006, Beijing, China, October 21, 2005, Graz, Austria, May 13, 2006, Revised Papers /$fedited by Rene Vidal, Anders Heyden, Yi Ma 205 $a1st ed. 2007. 210 1$aBerlin, Heidelberg :$cSpringer Berlin Heidelberg :$cImprint: Springer,$d2007. 215 $a1 online resource (IX, 329 p.) 225 1 $aImage Processing, Computer Vision, Pattern Recognition, and Graphics ;$v4358 300 $a"24 contributions presented at the First and Second International Workshops on Dynamical Vision, WDV 2006 and WDV 2006, which were held in conjunction with the 10th International Conference on Computer Vision (ICCV 2005) and 9th European Conference on Computer Vision (ECCV 2006), respectively"--Preface. 311 $a3-540-70931-2 320 $aIncludes bibliographical references and index. 327 $aMotion Segmentation and Estimation -- The Space of Multibody Fundamental Matrices: Rank, Geometry and Projection -- Direct Segmentation of Multiple 2-D Motion Models of Different Types -- Motion Segmentation Using an Occlusion Detector -- Robust 3D Segmentation of Multiple Moving Objects Under Weak Perspective -- Nonparametric Estimation of Multiple Structures with Outliers -- Human Motion Analysis, Tracking and Recognition -- Articulated Motion Segmentation Using RANSAC with Priors -- Articulated-Body Tracking Through Anisotropic Edge Detection -- Homeomorphic Manifold Analysis: Learning Decomposable Generative Models for Human Motion Analysis -- View-Invariant Modeling and Recognition of Human Actions Using Grammars -- Dynamic Textures -- Segmenting Dynamic Textures with Ising Descriptors, ARX Models and Level Sets -- Spatial Segmentation of Temporal Texture Using Mixture Linear Models -- Online Video Registration of Dynamic Scenes Using Frame Prediction -- Dynamic Texture Recognition Using Volume Local Binary Patterns -- Motion Tracking -- A Rao-Blackwellized Parts-Constellation Tracker -- Bayesian Tracking with Auxiliary Discrete Processes. Application to Detection and Tracking of Objects with Occlusions -- Tracking of Multiple Objects Using Optical Flow Based Multiscale Elastic Matching -- Real-Time Tracking with Classifiers -- Rigid and Non-rigid Motion Analysis -- A Probabilistic Framework for Correspondence and Egomotion -- Estimating the Pose of a 3D Sensor in a Non-rigid Environment -- A Batch Algorithm for Implicit Non-rigid Shape and Motion Recovery -- Motion Filtering and Vision-Based Control -- Using a Connected Filter for Structure Estimation in Perspective Systems -- Recursive Structure from Motion Using Hybrid Matching Constraints with Error Feedback -- Force/Vision Based Active Damping Control of Contact Transition in Dynamic Environments -- Segmentation and Guidance of Multiple Rigid Objects for Intra-operative Endoscopic Vision. 330 $aClassical multiple-view geometry studies the reconstruction of a static scene - served by a rigidly moving camera. However, in many real-world applications the scene may undergo much more complex dynamical changes. For instance, the scene may consist of multiple moving objects (e.g., a tra?c scene) or arti- lated motions (e.g., a walking human) or even non-rigid dynamics (e.g., smoke, ?re, or a waterfall). In addition, some applications may require interaction with the scene through a dynamical system (e.g., vision-guided robot navigation and coordination). To study the problem of reconstructing dynamical scenes, many new al- braic, geometric, statistical, and computational tools have recently emerged in computer vision, computer graphics, image processing, and vision-based c- trol. The goal of the International Workshop on Dynamical Vision (WDV) is to converge di?erent aspects of the research on dynamical vision and to identify common mathematical problems, models, and methods for future research in this emerging and active area. 410 0$aImage Processing, Computer Vision, Pattern Recognition, and Graphics ;$v4358 606 $aOptical data processing 606 $aPattern recognition 606 $aComputer graphics 606 $aUser interfaces (Computer systems) 606 $aImage Processing and Computer Vision$3https://scigraph.springernature.com/ontologies/product-market-codes/I22021 606 $aPattern Recognition$3https://scigraph.springernature.com/ontologies/product-market-codes/I2203X 606 $aComputer Graphics$3https://scigraph.springernature.com/ontologies/product-market-codes/I22013 606 $aUser Interfaces and Human Computer Interaction$3https://scigraph.springernature.com/ontologies/product-market-codes/I18067 615 0$aOptical data processing. 615 0$aPattern recognition. 615 0$aComputer graphics. 615 0$aUser interfaces (Computer systems). 615 14$aImage Processing and Computer Vision. 615 24$aPattern Recognition. 615 24$aComputer Graphics. 615 24$aUser Interfaces and Human Computer Interaction. 676 $a006.37 702 $aVidal$b Rene$4edt$4http://id.loc.gov/vocabulary/relators/edt 702 $aHeyden$b Anders$4edt$4http://id.loc.gov/vocabulary/relators/edt 702 $aMa$b Yi$4edt$4http://id.loc.gov/vocabulary/relators/edt 712 12$aWDV 2006$f(2006 :$eGraz, Austria) 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a996465837203316 996 $aDynamical Vision$9772521 997 $aUNISA