Path: utzoo!utgpu!news-server.csri.toronto.edu!mailrus!uwm.edu!zaphod.mps.ohio-state.edu!swrinde!ucsd!sdcc6!beowulf!schraudo From: schraudo@beowulf.ucsd.edu (Nici Schraudolph) Newsgroups: comp.ai.neural-nets Subject: Re: Which learning algorithm is best for scale/rotation invariant input? Message-ID: Date: 1 Oct 90 04:08:08 GMT References: Sender: news@sdcc6.ucsd.edu Lines: 20 Nntp-Posting-Host: beowulf.ucsd.edu One way to solve/circumvent the scale/translation/rotation invariance problem in visual recognition problems is through appropriate preprocessing of the inputs. I've seen an example of this approach at IJCNN'90 (San Diego): David Casasent and Etienne Barnard, "Adaptive Clustering Neural Net for Piecewise Nonlinear Discriminant Surfaces", Proc. IJCNN'90, p. I-423 (also, a paper by the same authors in IJCNN'89 (Washington) , p. I-111) They first perform a 2-D Fourier transform on the image (which gives them translation invariance), then use input neurons with ring- and wedge-shaped receptive fields on the transformed image. The "ring neurons" are scale sensitive but rotation invariant whereas the "wedge neurons" are rotation sensitive but scale invariant. The right mix of these may provide a good feature space for this kind of recognition task. -- Nicol N. Schraudolph, C-014 "Big Science, hallelujah. University of California, San Diego Big Science, yodellayheehoo." La Jolla, CA 92093-0114 - Laurie Anderson. nici%cs@ucsd.{edu,bitnet,uucp}