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<p>The authors describe a neural network approach for combining processing of multiple early vision modules. Energy functions for coupling the computation of intensity contours, optical flow, and stereo disparity are defined. Hopfield neural networks are used for function minimization with deterministic annealing to avoid spurious local minima. Vision integration schemes are developed by extending the work of T.A. Poggio et al. (1988) to include cooperative interactions between different vision modules and the Hebbian adaptation of vision module coupling on a massively parallel computer consisting of 4096 processing elements operated in a single-instruction-multiple-data mode. Simple experiments assess the performance of various integration approaches. The resulting algorithms facilitate fast, robust image segmentation.</p>
cooperative vision integration; Hopfield neural networks; data-parallel neural computations; neural network approach; multiple early vision modules; intensity contours; optical flow; stereo disparity; function minimization; deterministic annealing; Hebbian adaptation; massively parallel computer; single-instruction-multiple-data mode; image segmentation; computer vision; computerised picture processing; neural nets.
S.T. Toborg, K. Hwang, "Cooperative Vision Integration Through Data-Parallel Neural Computations", IEEE Transactions on Computers, vol. 40, no. , pp. 1368-1379, December 1991, doi:10.1109/12.106222
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