Advances in computer vision
Author(s)
Bibliographic Information
Advances in computer vision
Lawrence Erlbaum Associates, 1988
- v. 1
- v. 2
Available at / 22 libraries
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The Library of the Faculty of Education, Kyoto University
v. 1007.64||B 8188025416,
v. 2007.64||B 8188025417 -
The University of Electro-Communications Library研
v. 1549.98||A16||12219702189,
v. 2549.98||A16||2 -
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Note
Includes bibliography and indexes
Description and Table of Contents
- Volume
-
v. 2 ISBN 9780805800920
Description
First Published in 1988. The series Advances in Computer Vision has the goal of presenting current approaches to basic problems that arise in the construction of a computer vision system, written by leading researchers and practitioners in the field. In these volumes, computer vision means computer programs analyzing visual input (like a television image of a three-dimensional scene) and deriving from the image some description of the scene that is helpful to further reasoning or action concerning the scene.
Table of Contents
- Chapter 1 The use of the Facet Model and the Topographic Primal Sketch in Image Analysis, Linda G. Shapiro, Robert M. Haralick, Ting-Chuen Pong
- Chapter 2 Scaling and Fingerprint Theorems for Zero-Crossings, Alan L. Yuille, Tomaso Poggio
- Chapter 3 Form Perception Using Transformation Networks, Dana H. Ballard
- Chapter 4 The Parts of Perception, Alex P. Pentland
- Volume
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v. 1 ISBN 9780898596489
Description
First published in 1988. The series Advances in Computer Vision has the goal of presenting current approaches to basic problems that arise in the construction of a computer vision system, written by leading researchers and practitioners in the field. The first two volumes in the series comprise seven chapters, which together cover much of the scope of computer vision. This is Volume I.
Table of Contents
Contents: A. Hanson, E. Riseman, The Visions Image Understanding System. J. Aloimonos, C. Brown, Robust Computation of Intrinsic Images from Multiple Cues. A. Waxman, K. Wohn, Image Flow Theory: A Framework for 3-D Inference From Time-Varying Imagery.
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