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Object Categorization: Computer and Human Vision Perspectives

Object Categorization: Computer and Human Vision Perspectives

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Cambridge University Press
Edition: Illustrated, 9/7/2009
EAN 9780521887380, ISBN10: 0521887380

Hardcover, 552 pages, 10.4 x 7.1 x 3.8 cm
Language: English

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.

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.