Tech Report: Shift Invariance and Local Receptive Fields

[Rajesh Rao]

The following technical report on learning localized receptive fields
for transformation estimation is available on the WWW page:
http://www.cs.rochester.edu/u/rao/ 
or via anonymous ftp (see instructions below).

Comments and suggestions welcome (This message has been cross-posted -
my apologies to those who receive it more than once).

-- 
Rajesh Rao                       Internet: rao at cs.rochester.edu
Dept. of Computer Science	 VOX:  (716) 275-2527              
University of Rochester          FAX:  (716) 461-2018
Rochester  NY  14627-0226        WWW:  http://www.cs.rochester.edu/u/rao/

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		Localized Receptive Fields May Mediate
		 Transformation-Invariant Recognition
			 in the Visual Cortex

		 Rajesh P.N. Rao and Dana H. Ballard

		  	Technical Report 97.2
    National Resource Laboratory for the Study of Brain and Behavior
	 Department of Computer Science, University of Rochester
			     May 1997


  Neurons in the visual cortex are known to possess localized,
  oriented receptive fields. It has previously been suggested that
  these distinctive properties may reflect an efficient image encoding
  strategy based on maximizing the sparseness of the distribution of
  output neuronal activities or alternately, extracting the
  independent components of natural image ensembles. Here, we show
  that a relatively simple neural solution to the problem of
  transformation-invariant visual recognition also causes localized,
  oriented receptive fields to be learned from natural images. These
  receptive fields, which code for various transformations in the
  image plane, allow a pair of cooperating neural networks, one
  estimating object identity (``what'') and the other estimating
  object transformations (``where''), to simultaneously recognize an
  object and estimate its pose by jointly maximizing the 
  a posteriori probability of generating the observed visual data. We
  provide experimental results demonstrating the ability of these
  networks to factor retinal stimuli into object-centered features and
  object-invariant transformations. The resulting neuronal
  architecture suggests concrete computational roles for the
  neuroanatomical connections known to exist between the dorsal and
  ventral visual pathways.

Retrieval information:

FTP-host:       ftp.cs.rochester.edu
FTP-pathname:   /pub/u/rao/papers/local.ps.Z
WWW URL:        http://www.cs.rochester.edu/u/rao/ 

9 pages; 229K compressed.

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Anonymous ftp instructions:

>ftp ftp.cs.rochester.edu
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Name: [type 'anonymous' here]
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ftp> cd /pub/u/rao/papers/
ftp> get local.ps
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