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Readings in Computer Vision
edited_book
Publication date
(Print):
1987
Publisher:
Elsevier
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Core Readings in Statistical Mediation Analysis
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Book
ISBN (Print):
9780080515816
Publication date (Print):
1987
SO-VID:
f0276283-7983-4329-b94b-ffca143c95da
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Book chapters
pp. 61
A computer algorithm for reconstructing a scene from two projections
pp. 184
A Computational Approach to Edge Detection
pp. 227
Parts of recognition**We are grateful to Thomas Banchoff, Aaron Bobick, Mike Brady, Carmen Egido, Jerry Fodor, Jim Hodgson, Jan Koenderink, Jay Lebed, Alex Pentland, John Rubin, Joseph Scheuhammer, and Andrew Witkin for their helpful discussions and. in some cases, for reading earlier drafts. We are also grateful to Alan Yuille for comments and corrections on the mathematics in the appendices. Preparation of this paper was supported in part by NSF and AFOSR under a combined grant for studies in Natural Computation, grant 79–23110-MCS, and by the AFOSR under an Image Understanding contract F49620–83-C-0135. Technical support was kindly provided by William Gilson; artwork was the creation of Julie Sandell and K. van Buskirk. Reprints may be obtained from D. Hoffman, School of Social Sciences, University of California, Irvine, CA 92717. U.S.A.
pp. 329
SCALE-SPACE FILTERING
pp. 522
A Learning Algorithm for Boltzmann Machines**The research reported here was supported by grants from the System Development Foundation. We thank Peter Brown, Francis Crick, Mark Derthick, Scott Fahlman, Jerry Feldman, Stuart Geman, Gail Gong, John Hopfield, Jay McClelland, Barak Pearlmutter, Harry Printz, Dave Rumelhart, Tim Shallice, Paul Smolensky, Rick Szeliski, and Venkataraman Venkatasubramanian for helpful discussions.Reprint requests should be addressed to David Ackley, Computer Science Department, Carnegie-Mellon University, Pittsburgh, PA 15213.
pp. 564
Stochastic Relaxation, Gibbs Distributions, and the Bayesian Restoration of Images
pp. 585
On the Foundations of Relaxation Labeling Processes
pp. 671
The Laplacian Pyramid as a Compact Image Code
pp. 726
Random Sample Consensus: A Paradigm for Model Fitting with Applications to Image Analysis and Automated Cartography
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