Computer and Robot Vision (Volume 1) by Robert M. Haralick

By Robert M. Haralick

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Often these feedforward networks use the error of performance of feedback to learn. The nervous system also uses feedback to learn. Looping between groups or levels of neurons has inspired the development of resonating neural network computational approaches. The use of inhibition, as well as excitation, has guided the development of cooperative/competitive actions found in autoassociative neural network simulations. Promising areas of the brain to model and those that are currently areas of intense interest include the retina, audition, and olfaction.

Features such as edges, angles, and binocular disparity are developed in the cortical areas. These features appear to be processed in parallel systems. There are interconnections between some of the systems but most of these features are fused in association areas of the cortex. The visual system does not appear to identify a dog or other object at the occipital cortical level. This area of the cortex is the first to receive visual information through the most direct route to the cortex, however objects are not recognized here.

Recent publications by a growing circle of researchers working in an increasing number of universities and corporate laboratories have surfaced, and gradu­ ate programs dedicated to neural network technologies are emerging. The success of these activities, applications, and current research will determine the value and future of the neural network approach. REFERENCES Amari, S. (1967). "A theory of adaptive pattern classifiers," IEEE Trans, on Electronic Computers, EC-16, 299-307. Amari, S. (1972).

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