LEADER 08149nam 22007695 450 001 996466232603316 005 20200704161349.0 010 $a3-540-47979-1 024 7 $a10.1007/3-540-47979-1 035 $a(CKB)1000000000211749 035 $a(SSID)ssj0000316916 035 $a(PQKBManifestationID)11237049 035 $a(PQKBTitleCode)TC0000316916 035 $a(PQKBWorkID)10286735 035 $a(PQKB)10868743 035 $a(SSID)ssj0000715599 035 $a(PQKBManifestationID)11408888 035 $a(PQKBTitleCode)TC0000715599 035 $a(PQKBWorkID)10704131 035 $a(PQKB)21039796 035 $a(DE-He213)978-3-540-47979-6 035 $a(MiAaPQ)EBC3068405 035 $a(PPN)155163841 035 $a(EXLCZ)991000000000211749 100 $a20100301d2002 u| 0 101 0 $aeng 135 $aurnn|008mamaa 181 $ctxt 182 $cc 183 $acr 200 10$aComputer Vision - ECCV 2002$b[electronic resource] $e7th European Conference on Computer Vision, Copenhagen, Denmark, May 28-31, 2002. Proceedings. Part IV /$fedited by Anders Heyden, Gunnar Sparr, Mads Nielsen, Peter Johansen 205 $a1st ed. 2002. 210 1$aBerlin, Heidelberg :$cSpringer Berlin Heidelberg :$cImprint: Springer,$d2002. 215 $a1 online resource (XXVIII, 844 p.) 225 1 $aLecture Notes in Computer Science,$x0302-9743 ;$v2353 300 $aBibliographic Level Mode of Issuance: Monograph 311 $a3-540-43748-7 320 $aIncludes bibliographical references and index. 327 $aObject Recognition / Vision Systems Engineering and Evaluation -- Face Identification by Fitting a 3D Morphable Model Using Linear Shape and Texture Error Functions -- Hausdorff Kernel for 3D Object Acquisition and Detection -- Evaluating Image Segmentation Algorithms Using the Pareto Front -- On Performance Characterization and Optimization for Image Retrieval -- Statistical Learning -- Statistical Learning of Multi-view Face Detection -- Dynamic Trees: Learning to Model Outdoor Scenes -- Object Recognition as Machine Translation: Learning a Lexicon for a Fixed Image Vocabulary -- Learning a Sparse Representation for Object Detection -- Calibration / Active and Real-Time and Robot Vision / Image and Video Indexing / Medical Image Understanding / Vision Systems / Engineering and Evaluations / Statistical Learning -- Stratified Self Calibration from Screw-Transform Manifolds -- Self-Organization of Randomly Placed Sensors -- Camera Calibration with One-Dimensional Objects -- Automatic Camera Calibration from a Single Manhattan Image -- What Can Be Known about the Radiometric Response from Images? -- Estimation of Illuminant Direction and Intensity of Multiple Light Sources -- 3D Modelling Using Geometric Constraints: A Parallelepiped Based Approach -- Geometric Properties of Central Catadioptric Line Images -- Another Way of Looking at Plane-Based Calibration: The Centre Circle Constraint -- Active Surface Reconstruction Using the Gradient Strategy -- Linear Pose Estimation from Points or Lines -- A Video-Based Drowning Detection System -- Visual Data Fusion for Objects Localization by Active Vision -- Towards Real-Time Cue Integration by Using Partial Results -- Tracking and Object Classification for Automated Surveillance -- Very Fast Template Matching -- Fusion of Multiple Tracking Algorithms for Robust People Tracking -- Video Summaries through Mosaic-Based Shot and Scene Clustering -- Optimization Algorithms for the Selection of Key Frame Sequences of Variable Length -- Multi-scale EM-ICP: A Fast and Robust Approach for Surface Registration -- An Unified Approach to Model-Based and Model-Free Visual Servoing -- Comparing Intensity Transformations and Their Invariants in the Context of Color Pattern Recognition -- A Probabilistic Framework for Spatio-Temporal Video Representation & Indexing -- Video Compass -- Computing Content-Plots for Video -- Classification and Localisation of Diabetic-Related Eye Disease -- Robust Active Shape Model Search -- A New Image Registration Technique with Free Boundary Constraints: Application to Mammography -- Registration Assisted Image Smoothing and Segmentation -- An Accurate and Efficient Bayesian Method for Automatic Segmentation of Brain MRI -- A PDE Approach for Thickness, Correspondence, and Gridding of Annular Tissues -- Statistical Characterization of Morphological Operator Sequences -- Image Registration for Foveated Omnidirectional Sensing -- Automatic Model Selection by Modelling the Distribution of Residuals -- Assorted Pixels: Multi-sampled Imaging with Structural Models -- Robust Parameterized Component Analysis -- Learning Intrinsic Video Content Using Levenshtein Distance in Graph Partitioning -- A Tale of Two Classifiers: SNoW vs. SVM in Visual Recognition -- Learning to Parse Pictures of People -- Learning Montages of Transformed Latent Images as Representations of Objects That Change in Appearance -- Exemplar-Based Face Recognition from Video -- Learning the Topology of Object Views -- A Robust PCA Algorithm for Building Representations from Panoramic Images -- Adjustment Learning and Relevant Component Analysis -- Texture, Shading, and Colour -- What Are Textons? -- Bidirectional Texture Contrast Function -- Removing Shadows from Images. 330 $aPremiering in 1990 in Antibes, France, the European Conference on Computer Vision, ECCV, has been held biennially at venues all around Europe. These conferences have been very successful, making ECCV a major event to the computer vision community. ECCV 2002 was the seventh in the series. The privilege of organizing it was shared by three universities: The IT University of Copenhagen, the University of Copenhagen, and Lund University, with the conference venue in Copenhagen. These universities lie ¨ geographically close in the vivid Oresund region, which lies partly in Denmark and partly in Sweden, with the newly built bridge (opened summer 2000) crossing the sound that formerly divided the countries. We are very happy to report that this year?s conference attracted more papers than ever before, with around 600 submissions. Still, together with the conference board, we decided to keep the tradition of holding ECCV as a single track conference. Each paper was anonymously refereed by three different reviewers. For the nal selection, for the rst time for ECCV, a system with area chairs was used. These met with the program chairsinLundfortwodaysinFebruary2002toselectwhatbecame45oralpresentations and 181 posters.Also at this meeting the selection was made without knowledge of the authors?identity. 410 0$aLecture Notes in Computer Science,$x0302-9743 ;$v2353 606 $aOptical data processing 606 $aComputer graphics 606 $aPattern recognition 606 $aArtificial intelligence 606 $aImage Processing and Computer Vision$3https://scigraph.springernature.com/ontologies/product-market-codes/I22021 606 $aComputer Graphics$3https://scigraph.springernature.com/ontologies/product-market-codes/I22013 606 $aPattern Recognition$3https://scigraph.springernature.com/ontologies/product-market-codes/I2203X 606 $aArtificial Intelligence$3https://scigraph.springernature.com/ontologies/product-market-codes/I21000 615 0$aOptical data processing. 615 0$aComputer graphics. 615 0$aPattern recognition. 615 0$aArtificial intelligence. 615 14$aImage Processing and Computer Vision. 615 24$aComputer Graphics. 615 24$aPattern Recognition. 615 24$aArtificial Intelligence. 676 $a006.6 676 $a006.37 702 $aHeyden$b Anders$4edt$4http://id.loc.gov/vocabulary/relators/edt 702 $aSparr$b Gunnar$4edt$4http://id.loc.gov/vocabulary/relators/edt 702 $aNielsen$b Mads$4edt$4http://id.loc.gov/vocabulary/relators/edt 702 $aJohansen$b Peter$4edt$4http://id.loc.gov/vocabulary/relators/edt 906 $aBOOK 912 $a996466232603316 996 $aComputer Vision — ECCV 2002$9771900 997 $aUNISA