Toward Category-Level Object Recognition - Taschenbuch

EAN (ISBN-13): 9783540687948
ISBN (ISBN-10): 3540687947
Gebundene Ausgabe
Taschenbuch
Erscheinungsjahr: 2006
Herausgeber: Springer Berlin Heidelberg
618 Seiten
Gewicht: 0,889 kg
Sprache: eng/Englisch

Buch in der Datenbank seit 2007-05-28T21:35:00+02:00 (Zurich)
Detailseite zuletzt geändert am 2024-03-28T01:50:54+01:00 (Zurich)
ISBN/EAN: 9783540687948

ISBN - alternative Schreibweisen:
3-540-68794-7, 978-3-540-68794-8
Alternative Schreibweisen und verwandte Suchbegriffe:
Autor des Buches: martial, ponce, zisserman, hebert, zisser, jean little, cordelia, jean hébert, taormina
Titel des Buches: category, lecture notes computer science, level, pattern recognition, graphics, image science, object vision

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Daten vom Verlag:

Autor/in: Jean Ponce; Martial Hebert; Cordelia Schmid; Andrew Zisserman
Titel: Lecture Notes in Computer Science; Image Processing, Computer Vision, Pattern Recognition, and Graphics; Toward Category-Level Object Recognition
Verlag: Springer; Springer Berlin
620 Seiten
Erscheinungsjahr: 2006-12-22
Berlin; Heidelberg; DE
Sprache: Englisch
106,99 € (DE)
109,99 € (AT)
118,00 CHF (CH)
Available
XI, 620 p.

BC; Hardcover, Softcover / Informatik, EDV/Anwendungs-Software; Mustererkennung; Verstehen; Informatik; 3D; 3D objects; Textur; classification; cognition; cognitive computer vision; cognitive systems; computational attention; computer vision; computer vision systems; face detection; feature extraction; learning; modeling; object recognition; algorithm analysis and problem complexity; Automated Pattern Recognition; Computer Imaging, Vision, Pattern Recognition and Graphics; Computer Vision; Artificial Intelligence; Computer Graphics; Algorithms; Bildverarbeitung; Maschinelles Sehen, Bildverstehen; Künstliche Intelligenz; Grafikprogrammierung; Algorithmen und Datenstrukturen; EA

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.