Toward Category-Level Object Recognition [electronic resource] / edited by Jean Ponce, Martial Hebert, Cordelia Schmid, Andrew Zisserman.

1st ed. 2006.
Berlin, Heidelberg : Springer Berlin Heidelberg : Imprint: Springer, 2006.
Computer Science (Springer-11645)
LNCS sublibrary. Image processing, computer vision, pattern recognition, and graphics SL 6, 4170
Image Processing, Computer Vision, Pattern Recognition, and Graphics ; 4170
1 online resource (XI, 620 pages)
Pattern perception.
Optical data processing.
Artificial intelligence.
Computer graphics.
Local subjects:
Pattern Recognition. (search)
Computer Imaging, Vision, Pattern Recognition and Graphics. (search)
Image Processing and Computer Vision. (search)
Artificial Intelligence. (search)
Computer Graphics. (search)
Algorithm Analysis and Problem Complexity. (search)
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Although 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.
Object 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.
Ponce, Jean. editor., Editor,
Hebert, Martial, editor., Editor,
Schmid, Cordelia, editor., Editor,
Zisserman, Andrew, editor., Editor,
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