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Constraint Networks in Vision
December 1991 (vol. 40 no. 12)
pp. 1359-1367

Applications in machine vision of constraint networks based on an augmented Lagrangian formulation are discussed. Only those applications that have a fundamental significance are addressed. The first of these provides a generalization of the Harris coupled depth-slope analog model of visual reconstruction. Because of the generality of the approach, one can derive many more alternative structures, and the mathematical setting places this approach within the bounds of mixed finite element theory. This offers many advantages in terms of the associated mathematical theory and implementation on digital machines. The second use is in data fusion, which is a crucial task for systems using multiple sensors or methods of analysis of data.

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Index Terms:
neural networks; machine vision; constraint networks; augmented Lagrangian formulation; Harris coupled depth-slope analog model; visual reconstruction; finite element theory; associated mathematical theory; data fusion; multiple sensors; computer vision; finite element analysis; neural nets.
D. Suter, "Constraint Networks in Vision," IEEE Transactions on Computers, vol. 40, no. 12, pp. 1359-1367, Dec. 1991, doi:10.1109/12.106221
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