Ponce / Hebert / Schmid Toward Category-Level Object Recognition

2006
ISBN: 978-3-540-68795-5
Verlag: Springer
Format: PDF
Kopierschutz: 1 - PDF Watermark

E-Book, Englisch, Band 4170, 620 Seiten, eBook

Reihe: Lecture Notes in Computer Science

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 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.

Zielgruppe

Research

Autoren/Hrsg.

Ponce, Jean

Hebert, Martial

Schmid, Cordelia

Zisserman, Andrew

Inhaltsverzeichnis

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.