shift invatiance

[Ilya Rybak]

Dear Connectionists,

I followed the discussion about shift invariance, which recently 
was on this list, with a huge interest. The question is still open.  
But, Lev Goldgarbs point of view looks more plausible (at least 
for me). Of course, human vision is able to recognize images 
invariantly to shift, scale and (possibly) rotation. However, it 
does not  mean  that this property results directly from the 
corresponding property of some perceptron-like neural network. 
Invariant recognition in human vision is related to much more 
complex processes, and probably it cannot be understood in limited 
frames of neural computations without taking into account attention
mechanisms and psychological and behavioral aspects of visual 
perception and recognition.

Anyway, using a kind of behavioral approach we have tried to build 
a model of visual system  without any invariant properties in neural 
networks. The model is called "BMV: Behavioral model of active 
visual perception and invariant recognition". BMV is able to 
recognize complex gray-level images (e.g. faces) invariantly to any 
2D transformations (shift, rotation and scale). The descriptions of 
our approach and BMV model, as well as our DEMO for DOS are now 
available in WWW. The URL is 

http://www.voicenet.com/~rybak/vnc.html

Look at it, maybe you find it interesting in the context of 
shift invariance discussion. Any feedback is welcome.

Ilya Rybak

DuPont Central Research
rybaki at eplrx7.es.duPont.com