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Early visual learning

Author: S K Nayar; T Poggio
Publisher: Oxford : Oxford university press, 1996
Edition/Format:   Print book : EnglishView all editions and formats

Using contributions from experts in the field of computer vision, this work focuses on learning techniques that are applied to the signals provided by vision sensors. It emphasizes low-level visual  Read more...


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Document Type: Book
All Authors / Contributors: S K Nayar; T Poggio
ISBN: 0195095227 9780195095227
OCLC Number: 473401182
Description: 367 s.
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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"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 Read more...

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