Associative Learning of Standard Regularizing Operators in Early Vision
Name
AI_WP_264.pdf
Description
Main article
Size
333.89 KB
Format
Adobe PDF
Checksum (MD5)
98bf237ea76d32f9b05fa58d9aeef5c5
Author(s)
Poggio, Tomaso
Hurlbert, Anya
Date Issued
December 1984
Publisher
MIT Artificial Intelligence Laboratory
Series/Report no.
MIT Artificial Intelligence Laboratory Working Papers, WP-264
Abstract
Standard regularization methods can be used to solve satisfactorily several problems in early vision, including edge detection, surface reconstruction, the computation of motion and the recovery of color. In this paper, we suggest (a) that quadratic variational principles corresponding to standard regularization methods are equivalent to a linear regularizing operator acting on the data and (b) that this operator can be synthesized through associative learning. The synthesis of the regularizing operator involves the computation of the pseudoinverse of the data. The pseudoinverse can be computed by iterative methods, that can be implemented in analog networks. Possible implications for biological visual systems are also discussed.
Persistent DSpace Link
