Path: utzoo!attcan!utgpu!news-server.csri.toronto.edu!mailrus!ames!dftsrv!hq!sysp1!kilmer From: kilmer@hq.af.mil Newsgroups: comp.ai.neural-nets Subject: Which learning algorithm is best for scale/rotation invariant input? Message-ID: Date: 18 Sep 90 17:07:05 GMT Sender: news@hq.af.mil Lines: 46 I was wondering if anyone had done a study of network paradigms that were particularly well suited for scale and rotation invariance. My problem involves identifying similar input patterns of different scale, such as a letter 'A' of two different font sizes (ie. 14 or 16 point courier), or different rotations (i.e. landscape vs. portrait or 23deg angle...). I created a backprop net that would accept a 8x14 binary input matrix and created a font within this matrix that the net was to associate to an 8 bit output (representing the character as a number i.e. ASCII). I had 94 characters in the font. The network learned the 94 character associations fine, and I tested the network with 3-8% noise levels with very good success at finding the resulting binary output. For larger fonts, I was simply going to scale down the character to fit into the 8x14 matrix. This should work...I havn't tryed it yet. As for the rotation problem, I wasn't sure how to approach this (short of rotating the input object until I had a positive match). Well, while I was working on this I decided to try and approach the problem from a differnt angle. I was first going to teach the network different fonts until I learned as many as I had access to, but wondered whether this was a dead end issue. Wouldn't a net that was able to extract the various features of an object and output what features it has identified within the object be better than teaching it all fonts. Specifically extract features regardless of size, or rotation. I have heard of something known as a neo- cognitron that was able to correctly identify disproportioned input or something like that, but havn't been able to find out any info on it. Does anyone out there have any, or doing any research into this area??? I would appreciate any reply. Thanks, Richard -- .-------------------------------------------------------------------------. | Richard Kilmer Kilmer@Opsnet-Pentagon.af.mil | | VAX Systems Analyst (AKA Kilmer@26.24.0.26) | | .--->Look to the future --. "But when hope has gone away | | | | In a night or in a day | | `--- Through the past <---' In a vision or in none | | Is is therefore the less gone?"| `-------------------------------------------------------------------------' -- -------------------------------------------------------------------------. | Richard Kilmer Kilmer@Opsnet-Pentagon.af.mil | | VAX Systems Analyst (AKA Kilmer@26.24.0.26) | | .--->Look to the future --. "But when hope has gone away |