Cambridge University PressEdition: Illustrated, 9/7/2009EAN 9780521887380, ISBN10: 0521887380Hardcover, 552 pages, 10.4 x 7.1 x 3.8 cmLanguage: EnglishThis 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.1. The evolution of object categorization and the challenge of image abstraction Sven Dickinson2. Can we understand how the brain solves object recognition James J. DiCarlo3. Visual recognitionwhere do we come from? What are we doing? Where should we go? Pietro Perona4. On what it means to see, and what we can do about it Shimon Edelman5. Generic object recognitionthe case for high level 3-D features Gerard Medioni6. Functional organization and development of the human ventral stream Kalanit Grill-Spector7. Reasoning about functionalityobject recognition and related developments Kevin Bowyer, Melanie Sutton and Louise Stark8. The user-interface theory of perceptionperception and categorization in the context of evolution Donald Hoffman9. 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 Wang10. Structural representation of object shape in the brain Charles Connor11. Learning hierarchical compositional representations of object structure Sanja Fidler, Marko Boben and Ales Leonardis12. Object categorization in man, monkey, and machinesome answers and some open questions Maximilian Riesenhuber13. Learning object category modeling, learning, and recognition by stochastic grammar Jake Porway, Benjamin Yao and Song Chun Zhu14. The neurophysiology and computational mechanisms of object representation Edmund Rolls15. Recognizing visual classes and individual objects by semantic hierarchies Shimon Ullman16. Early stages of object categorization Pawan Sinha, Benjamin Balas, Yuri Ostrovsky and Jonas Wulff17. Towards integration of different paradigms in modeling, representation and learning of visual categories Mario Fritz and Bernt Schiele18. Acquisition and breakdown of category-specificity in the ventral visual stream K. Suzanne Scherf, Marlene Behrmann and Kate Humphreys19. Using simple features and relations Marius Leordeanu, Martial Hebert and Rahul Sukthankar20. The proactive brainusing memory to anticipate what's next Kestutis Kveraga, Jasmine Boshyan and Moshe Bar21. Spatial pyramid matching Svetlana Lazebnik, Cordelia Schmid and Jean Ponce22. Perceptual decisions and visual learning in the human brain Zoe Kourtzi23. Shapes and shock graphsfrom segmented shapes to shapes embedded in images Benjamin Kimia24. 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 Samonds25. Medial models for recognition Kaleem Siddiqi and Stephen Pizer26. Multimodal categorization C. Wallraven and Heinrich Bulthoff27. Comparing images of 3-D objects David W. Jacobs.
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