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|All Authors / Contributors:||
S K Nayar; T Poggio
|Contents:||1: Shree Nayar & Tomaso Poggio: Early Visual Learning. 2: Jon Pauls, Emanuela Bricolo, & Nikos Logothetis: View Invariant Representations in Monkey Temporal Cortex: Position, Scale, and Rotational Invariance. 3: Tomaso Poggio & David Beymer: Regularization Networks for Visual Learning. 4: Arthur R. Pope & David G. Lowe: Learning Probabilistic Appearance Models for Object Recognition. 5: Baback Moghaddam & Alex Pentland: Probabilistic Visual Learning for Object Representation. 6: Shree K. Nayar, Hiroshi Murase, & Sameer A. Nene: Parametric Appearance Representation. 7: Dean Pomerieau: Neural Network Vision for Robot Driving. 8: John J. Weng: Cresceptron and SHOSLIF: Toward Comprehensive Visual Learning. 9: Randal C. Nelson: Memorization Learning for Object Recognition. 10: Usama M. Fayyad, Padhraic H. Smyth, Michael C. Burt, & Pietro Perona: Learning to Catalog Science Images. 11: Bir Bhanu, Xing Wu, & Sungkee Lee: Genetic Algorithms for Adaptive Image Segmentation. 12: Hayit Greenspan: Non-Parametric Texture Learning. 13: Marcos Salganicoff, Michele Rucci, & Ruzena Bajcsy: Unsupervised Visual-Tactile Learning for Control of Manipulation|
"The book has a lot to offer to researchers interested in recognition. With few exceptions the assembled papers describe systems that learn to recognize.' In particular almost half of the book is