LEADER 05835nam 22008295 450 001 996465986003316 005 20200707002238.0 010 $a1-280-86506-7 010 $a9786610865062 010 $a3-540-69866-3 024 7 $a10.1007/978-3-540-69866-1 035 $a(CKB)1000000000283767 035 $a(SSID)ssj0000292267 035 $a(PQKBManifestationID)11212702 035 $a(PQKBTitleCode)TC0000292267 035 $a(PQKBWorkID)10268876 035 $a(PQKB)11173775 035 $a(DE-He213)978-3-540-69866-1 035 $a(MiAaPQ)EBC3036647 035 $a(MiAaPQ)EBC6283293 035 $a(PPN)12314034X 035 $a(EXLCZ)991000000000283767 100 $a20100301d2007 u| 0 101 0 $aeng 135 $aurnn|008mamaa 181 $ctxt 182 $cc 183 $acr 200 10$aComplex Motion$b[electronic resource] $eFirst International Workshop, IWCM 2004, Günzburg, Germany, October 12-14, 2004, Revised Papers /$fedited by Bernd Jähne, Rudolf Mester, Erhardt Barth, Hanno Scharr 205 $a1st ed. 2007. 210 1$aBerlin, Heidelberg :$cSpringer Berlin Heidelberg :$cImprint: Springer,$d2007. 215 $a1 online resource (X, 238 p.) 225 1 $aImage Processing, Computer Vision, Pattern Recognition, and Graphics ;$v3417 300 $aBibliographic Level Mode of Issuance: Monograph 311 $a3-540-69864-7 320 $aIncludes bibliographical references and index. 327 $aOptical Flow Estimation from Monogenic Phase -- Optimal Filters for Extended Optical Flow -- Wiener-Optimized Discrete Filters for Differential Motion Estimation -- Boundary Characterization Within the Wedge-Channel Representation -- Multiple Motion Estimation Using Channel Matrices -- Divide-and-Conquer Strategies for Estimating Multiple Transparent Motions -- Towards a Multi-camera Generalization of Brightness Constancy -- Complex Motion in Environmental Physics and Live Sciences -- Bayesian Approaches to Motion-Based Image and Video Segmentation -- On Variational Methods for Fluid Flow Estimation -- Motion Based Estimation and Representation of 3D Surfaces and Boundaries -- A Probabilistic Formulation of Image Registration -- Myocardial Motion and Strain Rate Analysis from Ultrasound Sequences -- Determining the Translational Speed of a Camera from Time-Varying Optical Flow -- A Robust Approach for Ego-Motion Estimation Using a Mobile Stereo Platform -- Robust Monocular Detection of Independent Motion by a Moving Observer -- Tracking Complex Objects Using Graphical Object Models. 330 $aThe world we live in is a dynamic one: we explore it by moving through it, and many of the objects which we are interested in are also moving. Tra?c, for instance, is an example of a domain where detecting and processing visual motion is of vital interest, both in a metaphoric as well as in a purely literal sense. Visual communication is another important example of an area of science which is dominated by the need to measure, understand, and represent visual motion in an e?cient way. Visual motion is a subject of research which forces the investigator to deal withcomplexity;complexityinthesenseoffacinge?ectsofmotioninaverylarge diversity of forms, starting from analyzing simple motion in a changing envir- ment (illumination, shadows, . . . ), under adverse observation conditions, such as bad signal-to-noiseratio (low illumination, small-scaleprocesses, low-dosex-ray, etc. ), covering also multiple motions of independent objects, occlusions, and - ing as far as dealing with objects which are complex in themselves (articulated objects such as bodies of living beings). The spectrum of problems includes, but does not end at, objects which are not ?bodies? at all, e. g. , when anal- ing ?uid motion, cloud motion, and so on. Analyzing the motion of a crowd in a shopping mall or in an airport is a further example that implies the need to struggleagainsttheproblemsinducedbycomplexity. 410 0$aImage Processing, Computer Vision, Pattern Recognition, and Graphics ;$v3417 606 $aPattern recognition 606 $aOptical data processing 606 $aArtificial intelligence 606 $aComputer graphics 606 $aAlgorithms 606 $aPattern Recognition$3https://scigraph.springernature.com/ontologies/product-market-codes/I2203X 606 $aImage Processing and Computer Vision$3https://scigraph.springernature.com/ontologies/product-market-codes/I22021 606 $aArtificial Intelligence$3https://scigraph.springernature.com/ontologies/product-market-codes/I21000 606 $aComputer Graphics$3https://scigraph.springernature.com/ontologies/product-market-codes/I22013 606 $aAlgorithm Analysis and Problem Complexity$3https://scigraph.springernature.com/ontologies/product-market-codes/I16021 615 0$aPattern recognition. 615 0$aOptical data processing. 615 0$aArtificial intelligence. 615 0$aComputer graphics. 615 0$aAlgorithms. 615 14$aPattern Recognition. 615 24$aImage Processing and Computer Vision. 615 24$aArtificial Intelligence. 615 24$aComputer Graphics. 615 24$aAlgorithm Analysis and Problem Complexity. 676 $a006.37 702 $aJähne$b Bernd$4edt$4http://id.loc.gov/vocabulary/relators/edt 702 $aMester$b Rudolf$4edt$4http://id.loc.gov/vocabulary/relators/edt 702 $aBarth$b Erhardt$4edt$4http://id.loc.gov/vocabulary/relators/edt 702 $aScharr$b Hanno$4edt$4http://id.loc.gov/vocabulary/relators/edt 712 12$aInternational Workshop on Complex Motion 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a996465986003316 996 $aComplex Motion$9771874 997 $aUNISA