Early Visual Learning
Published on 13. June 1996
Book
Hardback
378 pages
978-0-19-509522-7 (ISBN)
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.
More details
Language
English
Place of publication
New York
United States
Target group
College/higher education
Professional and scholarly
Illustrations
halftones, line figures, tables, bibliography
Dimensions
Height: 250 mm
Width: 170 mm
Weight
873 gr
ISBN-13
978-0-19-509522-7 (9780195095227)
Copyright in bibliographic data is held by Nielsen Book Services Limited or its licensors: all rights reserved.
Schweitzer Classification
Content
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