> Is there anyone who has (or knows about) literature to the subject of
> object recognition in the human visual system ? The literature I have
> is (or is derived of) the work of Hubel and Wiese on the striate
> cortex (hypercolumn theory).
Perhaps this is not exactly what you are looking for, but I believe it
is the next logical step upwards from the Hubel and Wiesel data. What
Hubel and Wiesel showed was that the basic "language" or "data
representation" of the visual system is moving or static oriented
edges (as opposed to pixels, in computer image representation). The
question is, what happens next in visual processing? What
"computations" are performed on this raw information?
Grossberg & Mingolla have answered this question using a different
source of information, that being perceptual data, visual illusions in
particular. Why do we perceive certain things that are clearly not
there in the input image? Grossberg & Mingolla have shown how we can
use such perceptual data to infer certain processing mechanisms of
biological vision, and have expressed those mechanisms in the form of
dynamic neural models which can be simulated by computer in order to
reproduce the original illusions. Using this method Grossberg and
Mingolla have been able to reproduce and explain a very wide range of
diverse visual illusions.
What is interesting about their model is that it reveals a complex
mechanism with very interesting properties, with a level of robustness
and adaptability quite different from anything conceived by man for
computer image processing. Essentially, the model consists of
multiple cooperative and competitive interactions in a recurrent
multi-level hierarchy that optimizes the multiple constraints posed by
the image by a simultaneous relaxation in all the levels of the
hierarchy.
Now this can by no means be described as "high level" object
recognition, but rather, it represents a disambiguation of the lowest
level input, or "image enhancement". The nature of this enhancement
however affords an insight into the way nature solves visual problems,
and gives us a hint as to how such higher level processing might be
accomplished in the visual system. In particular, this model
emphasizes the importance of recurrent and long range interactions
between local computational elements in order to achieve global
consistancy throughout the system. My own work, which is an extension
to the Grossberg and Mingolla model emphasizes this aspect of their
model.
I have prepared a lengthy text file that outlines the prinpal ideas
behind these interesting models, which I would be happy to email to
you, or other interested parties, on request.
Stephen Grossberg & Ennio Mingolla NEURAL DYNAMICS OF PERCEPTUAL
GROUPING: TEXTURES, BOUNDARIES AND EMERGENT SEGMENTATIONS Perception &
Psychophysics (1985), 38 (2), 141-171. This work presents the BCS /
FCS model with detailed psychophysical motivation for the model
components and computer simulation of the BCS.
Stephen Grossberg & Ennio Mingolla NEURAL DYNAMICS OF SURFACE
PERCEPTION: BOUNDARY WEBS, ILLUMINANTS, AND SHAPE-FROM-SHADING.
Computer Vision, Graphics and Image Processing (1987) 37, 116-165.
This model extends the BCS to explore its response to gradients of
illumination. It is mentioned here because of an elegant modification
of the second competitive stage that was utilized in our simulations.
Stephen Grossberg & Dejan Todorovic NEURAL DYNAMICS OF 1-D AND 2-D
BRIGHTNESS PERCEPTION Perception and Psychophysics (1988) 43, 241-277.
A beautifully lucid summary of BCS / FCS modules with 1-D and 2-D
computer simulations with excellent graphics reproducing several
brightness perception illusions. This algorithm dispenses with
boundary completion, but in return it simulates the FCS operation.
Reprinted in NEURAL NETWORKS AND NATURAL INTELLIGENCE, Stephen
Grossberg Editor, MIT Press (1988) Chapter 3.
Lehar S., Worth A. MULTIPLE RESONANT BOUNDARY CONTOUR SYSTEM. In:
PROGRESS IN NEURAL NETWORKS volume 3 (Ed. by Ablex Publishing Corp.)
In print.
Lehar S., Worth A. MULTIPLE RESONANT BOUNDARY CONTOUR SYSTEM.
Proceedings of the SPIE, Vol. 1469 (Applications of Artificial Neural
Networks Conference) April 1991.
Lehar S., Worth A. MULTI RESONANT BOUNDARY CONTOUR SYSTEM, Boston
University, Center for Adaptive Systems technical report #
CAS/CNS-TR-91-017
Boston University
Center for Adaptive Systems
111 Cummington Street, Second Floor
Boston, MA 02215
(617) 353-7857,7858
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