Ilya A. Rybak

The model of a neural network visual preprocessor

Abstract The model of a neural network visual preprocessor and a system architecture for visual information processing are proposed. The model of the preprocessor is based on the model of a visual cortex iso-orientation domain which is considered as a neural network with retinotopically organized afferent inputs and anisotropic lateral inhibition formed by feedback connections via inhibitory interneurons. The high-level system uses the preprocessor to process image fragments with different resolutions and to represent the image as a set of contour segments of different sizes.

A visual cortex domain model and its use for visual information processing

Abstract A model of an iso-orientation domain in the visual cortex is developed. The iso-orientation domain is represented as a neural network with retinotopically organized afferent inputs and anisotropic lateral inhibition formed by feedbacks via inhibitory interneurons. Temporal dynamics of neuron responses to oriented stimuli is studied. The results of computer simulations are compared with those of neurophysiological experiments. It is shown that the later phase of neuron response has a more sharp orientation tuning than the initial one. It is suggested that the initial phase of neuron response encodes intensity parameters of visual stimulus, whereas the later phase encodes its orientation. The design of the neural network preprocessor and the architecture of the system for visual information processing, based on the idea of parallel-sequential processing, are proposed. The example of a test image processing is presented.

Approach to invariant object recognition on grey-level images by exploiting neural network models

A model of a neural network system for object recognition in grey-level images that is invariant with respect to position, rotation, and scale is developed. The model is based on the theory of D. Noton and L. Stark and on the concept of smart sensing. A method for visual image invariant representation is proposed. The method allows transformation of primary features into invariant ones which can be used as input signals for a classical neural network classifier of the high-level structure of the recognizing system.© (1991) COPYRIGHT SPIE--The International Society for Optical Engineering. Downloading of the abstract is permitted for personal use only.

A neural network system model for active perception and invariant recognition of grey-level images

A method for parallel-sequential processing of gray-level images and their representation which is invariant to position, rotation, and scale has been developed. The method is based on the idea that an image is memorized and recognized by way of consecutive fixations of moving eyes on the most informative image fragments. The method provides the invariant representation of the image in each fixation point and of spatial relations of features extracted in neighboring fixation points. A model of a neural network system for active visual perception and recognition of gray-level images has been developed based on this method. The experiments carried out with the model have shown that the system was able to recognize complex gray-level images in real time with invariance regarding position, rotation, and scale.<<ETX>>

Neural network modeling in studying neuron mechanisms of visual perception

An investigation including the development of realistic neural network models, computer experiments, and verification of their predictions in neurophysiological experiments made it possible to obtain a set of principally new facts on spatial and temporal dynamics of VC (visual cortex) neurons OS (orientation selectivity) and to develop a method of parallel-sequential processing of images. Attention was given to the temporal dynamics of the OS of the VC neurons, the spatial dynamics of the OS, and neural mechanisms of context description of visual object elements. Conformity of results of computer simulations and neurophysiological experiments supports the conceptual model of the iso-orientation domain described. The data obtained can be considered as an additional arguments in favor of the hypothesis that anisotropy of lateral inhibitory connections in the VC is one of the main mechanisms of formation of OS.<<ETX>>