Object Categorization (eBook, PDF)

Computer and Human Vision Perspectives
Redaktion: Dickinson, Sven J.

• Format: PDF

Produktbeschreibung

This edited volume presents a unique multidisciplinary perspective on the problem of visual object categorization. The result of a series of four highly successful workshops on the topic, the book gathers many of the most distinguished researchers from both computer and human vision to reflect on their experience, identify open problems, and foster a cross-disciplinary discussion with the idea that parallel problems and solutions have arisen in both domains. Twenty-seven of these workshop speakers have contributed chapters, including fourteen from computer vision and thirteen from human vision. Their contributions range from broad perspectives on the problem to more specific approaches, collectively providing important historical context, identifying the major challenges, and presenting recent research results. This multidisciplinary collection is the first of its kind on the topic of object categorization, providing an outstanding context for graduate students and researchers in both computer and human vision.

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Produktdetails

• Verlag: Cambridge University Press
• Erscheinungstermin: 7. September 2009
• Englisch
• ISBN-13: 9780511630040
• Artikelnr.: 38193414

Inhaltsangabe

1. The evolution of object categorization and the challenge of image
abstraction Sven Dickinson; 2. Can we understand how the brain solves
object recognition James J. DiCarlo; 3. Visual recognition: where do we
come from? What are we doing? Where should we go? Pietro Perona; 4. On what
it means to see, and what we can do about it Shimon Edelman; 5. Generic
object recognition: the case for high level 3-D features Gerard Medioni; 6.
Functional organization and development of the human ventral stream Kalanit
Grill-Spector; 7. Reasoning about functionality: object recognition and
related developments Kevin Bowyer, Melanie Sutton and Louise Stark; 8. The
user-interface theory of perception: perception and categorization in the
context of evolution Donald Hoffman; 9. Digital images in large collections
or on the web often appear near text D. A. Forsyth, Tamara Berg, Cecilia
Ovesdotter Alm, Ali Farhadi, Julia Hockenmaier, Nicolas Loeff and Gang
Wang; 10. Structural representation of object shape in the brain Charles
Connor; 11. Learning hierarchical compositional representations of object
structure Sanja Fidler, Marko Boben and Ales Leonardis; 12. Object
categorization in man, monkey, and machine: some answers and some open
questions Maximilian Riesenhuber; 13. Learning object category modeling,
learning, and recognition by stochastic grammar Jake Porway, Benjamin Yao
and Song Chun Zhu; 14. The neurophysiology and computational mechanisms of
object representation Edmund Rolls; 15. Recognizing visual classes and
individual objects by semantic hierarchies Shimon Ullman; 16. Early stages
of object categorization Pawan Sinha, Benjamin Balas, Yuri Ostrovsky and
Jonas Wulff; 17. Towards integration of different paradigms in modeling,
representation and learning of visual categories Mario Fritz and Bernt
Schiele; 18. Acquisition and breakdown of category-specificity in the
ventral visual stream K. Suzanne Scherf, Marlene Behrmann and Kate
Humphreys; 19. Using simple features and relations Marius Leordeanu,
Martial Hebert and Rahul Sukthankar; 20. The proactive brain: using memory
to anticipate what's next Kestutis Kveraga, Jasmine Boshyan and Moshe Bar;
21. Spatial pyramid matching Svetlana Lazebnik, Cordelia Schmid and Jean
Ponce; 22. Perceptual decisions and visual learning in the human brain Zoe
Kourtzi; 23. Shapes and shock graphs: from segmented shapes to shapes
embedded in images Benjamin Kimia; 24. Correlated structures in natural
scenes and their implications on neural learning of prior models for
objects and surfaces Tai Sing Lee, Tom Stepleton, Brian Potetz and Jason
Samonds; 25. Medial models for recognition Kaleem Siddiqi and Stephen
Pizer; 26. Multimodal categorization C. Wallraven and Heinrich Bulthoff;
27. Comparing images of 3-D objects David W. Jacobs.