Accession Number : ADP006785

Title :   Feature Extraction by a Self-Organizing Photorefractive System,

Corporate Author : COLORADO UNIV AT BOULDER DEPT OF PHYSICS

Personal Author(s) : Benkert, Claus ; Hebler, Verena ; Jang, Ju-Seog ; Rehman, Shakil ; Saffman, Mark

Report Date : 22 MAY 1992

Pagination or Media Count : 4

Abstract : An important feature of neural network processing lies in a network's ability to adapt to a given problem. The adaptation is accomplished by modifying its internal structure through some learning procedure. Neural network models may be classified in one of two types: The learning may be supervised by someone or something that indicates to the network what is expected of it, or the network may be governed by a self-organizing process in which it automatically develops an internal state that reflects the properties of its input environment. Self-organizing systems need no a priori knowledge supplied by a supervisor, and are particularly valuable when the task of the system depends only upon some property of the input data itself. We here describe a self-organizing photorefractive system that extracts features from a collection of input patterns. The features are extracted according to a similarity criterion, which in our case is defined by an inner product. For example, if the collection of patterns consists merely of two images with orthogonal electric field patterns, the system will recognize the orthogonality and the extracted features will be the images themselves. If instead the two pictures are not orthogonal, the system will find a pair of best features, which, in different linear combinations, make up the images. When the system is subsequently presented with another picture it can to what amount the learned features are present in the new input data.

Descriptors :   *OPTICAL IMAGES, *NEURAL NETS, *REFRACTION, *IMAGE PROCESSING, ADAPTATION, ELECTRIC FIELDS, IMAGES, INPUT, LEARNING, MODELS, ORTHOGONALITY, PATTERNS, PICTURES, PROCESSING, SELF ORGANIZING SYSTEMS, CRYSTALS, BARIUM TITANATES, OPTICAL PUMPING, SYMPOSIA, ARTIFICIAL INTELLIGENCE.

Subject Categories : Optics
      Cybernetics

Distribution Statement : APPROVED FOR PUBLIC RELEASE