Erim International, Inc.

US Government As Represented by the Secretary of the Army

The United States of America As Represented by the Department of Health

Environmental Research Institute of Michigan

Kim T Blackwell

Daniel L Alkon

Thomas P Vogl

Garth S Barbour

An image recognition and classification system includes a preprocessor in which a 'top-down' method is used to extract features from an image; an associative learning neural network system, which groups the features into patterns and classifies the patterns: and a feedback mechanism which improves system performance by tuning preprocessor scale, feature detection, and feature selection.

Top down preprocessor for a machine vision system

Dynamically stable associative learning neural network system

A dynamically stable associative learning neural network system includes, in its basic architectural unit, at least one each of a conditioned signal input, an unconditioned signal input and an output. Interposed between input and output elements are 'patches,' or storage areas of dynamic interaction between conditioned and unconditioned signals which process information to achieve associative learning locally under rules designed for application-related goals of the system. Patches may be fixed or variable in size. Adjustments to a patch radius may be by 'pruning' or 'budding.' The neural network is taught by successive application of training sets of input signals to the input terminals until a dynamic equilibrium is reached. Enhancements and expansions of the basic unit result in multilayered (multi-subnetworked) systems having increased capabilities for complex pattern classification and feature recognition.

Growing community of inventors

Wheaton, MD, United States of America

Kim T Blackwell