Early visual learning
Author(s)
Bibliographic Information
Early visual learning
Oxford University Press, 1996
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Note
Includes bibliographical references and index
Description and Table of Contents
Description
Featuring contributions from experts in the field of computer vision, this work focuses on learning techniques that are applied more or less directly to the signals provided by vision sensors. The emphasis is on low-level visual learning techniques that draw on results in the fields of statistics, pattern recognition and neural networks. This book should be of interest to researchers and has potential as a graduate level text in a visual learning course.
Table of 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
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