papers available

[Ilya Rybak]

Dear Connectionists,

Two below papers are accepted for IS&T/SPIE Conference on Human Vision,
Vision Procrssing and Digital Display IV, San Jose, 1993.
Hard copies of the papers are available with request by e-mail address
ilya at cheme.seas.upenn.edu

Ilya Rybak


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PHYSIOLOGICAL MODEL OF ORIENTATION SENSITIVITY IN THE VISUAL CORTEX
AND ITS USE FOR IMAGE PROCESSING

Ilya A. Rybak*, Lubov N. Podladchikova** and Natalia A. Shevtsova**

*  University of Pennsylvania, Philadelphia, PA, USA
           ilya at cheme.seas.upenn.edu
** A.B. Kogan Research Institute for Neurocybernetics
   Rostov State University, Rostov-on-Don, Russia

The objectives of the research were:
   (i) to investigate the dynamics of neuron responses and orientation
       selectivity in the primary visual cortex;
  (ii) to find a possible source of bifurcation of visual information into
      "what" and "where" processing pathways;
 (iii) to apply the obtained results for visual image processing.

The achieve the objectives, a model of the iso-orientation domain (orientation
column) of the visual cortex has been developed. The model is based on
neurophysiological data and on the idea that orientation selectivity results
from a spatial anisotropy of reciprocal lateral inhibition in the domain.
Temporal dynamics of neural responses to oriented stimuli were studied with
the model. It was shown that the later phase of neuron response had a much
sharper orientation tuning than the initial one. The results of modeling
were confirmed by neurophysiological experiments on the visual cortex.

The findings allow to suggest that the initial phase of neural response
encodes the location of the visual stimulus, whereas the later phase
encodes its orientation. Temporal dividing of information about object
features and their locations at the neuronal level of the primary visual
cortex may be considered to be a source for bifurcation of the visual
processing into "what" and "where" pathways and may be used for parallel-
sequential attentional image processing.

The model of neural network system for image processing based on the iso-
orientation domain models and above idea is proposed. An example of test
image processing is presented.

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BEHAVIORAL MODEL OF VISUAL PERCEPTION AND RECOGNITION

Ilya A. Rybak*, Alexander V. Golovan** and Valentina I. Gusakova**

*  University of Pennsylvania, Philadelphia, PA, USA
          ilya at cheme.seas.upenn.edu
** A.B. Kogan Research Institute for Neurocybernetics
   Rostov State University, Rostov-on-Don, Russia

In the processes of visual perception and recognition human eyes actively
select essential information by way of successive fixation at the most
informative points of the image. So, perception and recognition are not only
resuls or neural computations, but are also behavioral processes. A
behavioral program defining a scanpath of the image is formed at the stage
of learning (object memorizing) and consists of sequential motor actions,
which are shifts of attention from one to another point of fixation, and
sensory signals expected to arrive in response to each shift of attention.

In the modern view of the problem, invariant object recognition is provided
by the following: 
   (i) separated processing of "what" (object features) and "where" (spatial
       features) information at high levels of the visual system;
  (ii) mechanisms of visual attention using "where" information;
 (iii) representation of "what" information in an object-based frame of
       reference (OFR).
However, most recent models of vision based on OFR have demonstrated the
ability of invariant recognition of only simple objects like letters or
binary objects without background, i.e. objects to which a frame of reference
is easily attached. In contrast, we use not OFR, but a "feature-based frame
of reference" (FFR), connected with the basic feature (edge) at the fixation
point. This has provided for our model, the ability for invariant
representation of complex objects in gray-level images, but demands realization
of behavioral aspects of vision described above.

The developed model contains a neural network subsystem of low-level vision
which extracts a set of primary features (edges) in each fixation, and high-
level subsystem consisting of "what" (Sensory Memory) and "where" (Motor
Memory) modules. The resolution of primary features extraction decreases with
distances from the point of fixation. FFR provides both the invariant
representation of object features in Sensory Memory and shifts of attention in
Motor Memory. Object recognition consists in successive recall (from Motor
Memory) and execution of shifts of attention and successive verification of
the expected sets of features (stored in Sensory Memory). The model shows the
ability of recognition of complex objects (such as faces) in gray-level
images invariant with respect to shift, rotation, and scale.
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