LEADER 07023nam 22007695 450 001 996466162203316 005 20200706233954.0 010 $a3-540-68795-5 024 7 $a10.1007/11957959 035 $a(CKB)1000000000284034 035 $a(SSID)ssj0000320441 035 $a(PQKBManifestationID)11286220 035 $a(PQKBTitleCode)TC0000320441 035 $a(PQKBWorkID)10249490 035 $a(PQKB)10856806 035 $a(DE-He213)978-3-540-68795-5 035 $a(MiAaPQ)EBC3068562 035 $a(PPN)123140129 035 $a(EXLCZ)991000000000284034 100 $a20101223d2006 u| 0 101 0 $aeng 135 $aurnn|008mamaa 181 $ctxt 182 $cc 183 $acr 200 10$aToward Category-Level Object Recognition$b[electronic resource] /$fedited by Jean Ponce, Martial Hebert, Cordelia Schmid, Andrew Zisserman 205 $a1st ed. 2006. 210 1$aBerlin, Heidelberg :$cSpringer Berlin Heidelberg :$cImprint: Springer,$d2006. 215 $a1 online resource (XI, 620 p.) 225 1 $aImage Processing, Computer Vision, Pattern Recognition, and Graphics ;$v4170 300 $a"Outcome of two workshops that were held in Taormina in 2003 and 3004"--Pref. 311 $a3-540-68794-7 320 $aIncludes bibliographical references and index. 327 $aObject Recognition in the Geometric Era: A Retrospective -- Dataset Issues in Object Recognition -- Industry and Object Recognition: Applications, Applied Research and Challenges -- Recognition of Specific Objects -- What and Where: 3D Object Recognition with Accurate Pose -- Object Recognition Using Local Affine Frames on Maximally Stable Extremal Regions -- 3D Object Modeling and Recognition from Photographs and Image Sequences -- Video Google: Efficient Visual Search of Videos -- Simultaneous Object Recognition and Segmentation by Image Exploration -- Recognition of Object Categories -- Comparison of Generative and Discriminative Techniques for Object Detection and Classification -- Synergistic Face Detection and Pose Estimation with Energy-Based Models -- Generic Visual Categorization Using Weak Geometry -- Components for Object Detection and Identification -- Cross Modal Disambiguation -- Translating Images to Words for Recognizing Objects in Large Image and Video Collections -- A Semi-supervised Learning Approach to Object Recognition with Spatial Integration of Local Features and Segmentation Cues -- Towards the Optimal Training of Cascades of Boosted Ensembles -- Visual Classification by a Hierarchy of Extended Fragments -- Shared Features for Multiclass Object Detection -- Generative Models for Labeling Multi-object Configurations in Images -- Object Detection and Localization Using Local and Global Features -- The Trace Model for Object Detection and Tracking -- Recognition of Object Categories with Geometric Relations -- A Discriminative Framework for Texture and Object Recognition Using Local Image Features -- A Sparse Object Category Model for Efficient Learning and Complete Recognition -- Object Recognition by Combining Appearance and Geometry -- Shape Matching and Object Recognition -- An Implicit Shape Model for Combined Object Categorization and Segmentation -- Statistical Models of Shape and Texture for Face Recognition -- Joint Recognition and Segmentation -- Image Parsing: Unifying Segmentation, Detection, and Recognition -- Sequential Learning of Layered Models from Video -- An Object Category Specific mrf for Segmentation. 330 $aAlthough research in computer vision for recognizing 3D objects in photographs dates back to the 1960s, progress was relatively slow until the turn of the millennium, and only now do we see the emergence of effective techniques for recognizing object categories with different appearances under large variations in the observation conditions. Tremendous progress has been achieved in the past five years, thanks largely to the integration of new data representations, such as invariant semi-local features, developed in the computer vision community with the effective models of data distribution and classification procedures developed in the statistical machine-learning community. This volume is a post-event proceedings volume and contains selected papers based on presentations given, and vivid discussions held, during two workshops held in Taormina in 2003 and 2004. The main goals of these two workshops were to promote the creation of an international object recognition community, with common datasets and evaluation procedures, to map the state of the art and identify the main open problems and opportunities for synergistic research, and to articulate the industrial and societal needs and opportunities for object recognition research worldwide. The 30 thoroughly revised papers presented are organized in the following topical sections: recognition of specific objects, recognition of object categories, recognition of object categories with geometric relations, and joint recognition and segmentation. 410 0$aImage Processing, Computer Vision, Pattern Recognition, and Graphics ;$v4170 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 $aComputer Imaging, Vision, Pattern Recognition and Graphics$3https://scigraph.springernature.com/ontologies/product-market-codes/I22005 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$aPattern Recognition. 615 24$aComputer Imaging, Vision, Pattern Recognition and Graphics. 615 24$aImage Processing and Computer Vision. 615 24$aArtificial Intelligence. 615 24$aComputer Graphics. 615 24$aAlgorithm Analysis and Problem Complexity. 676 $a006.3/7 702 $aPonce$b Jean$4edt$4http://id.loc.gov/vocabulary/relators/edt 702 $aHebert$b Martial$4edt$4http://id.loc.gov/vocabulary/relators/edt 702 $aSchmid$b Cordelia$4edt$4http://id.loc.gov/vocabulary/relators/edt 702 $aZisserman$b Andrew$4edt$4http://id.loc.gov/vocabulary/relators/edt 906 $aBOOK 912 $a996466162203316 996 $aToward Category-Level Object Recognition$9772432 997 $aUNISA