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Using Dynamic Programming for Solving Variational Problems in Vision
September 1990 (vol. 12 no. 9)
pp. 855-867

Early image understanding seeks to derive analytic representations from image intensities. The authors present steps towards this goal by considering the inference of surfaces from three-dimensional images. Only smooth surfaces are considered and the focus is on the coupled problems of inferring the trace points (the points through which the surface passes) and estimating the associated differential structure given by the principal curvature and direction fields over the estimated smooth surfaces. Computation of these fields is based on determining an atlas of local charts or parameterizations at estimated surface points. Algorithm robustness and the stability of results are essential for analyzing real images; to this end, the authors present a functional minimization algorithm utilizing overlapping local charts to refine surface points and curvature estimates, and develop an implementation as an iterative constraint satisfaction procedure based on local surface smoothness properties. Examples of the recovery of local structure are presented for synthetic images degraded by noise and for clinical magnetic resonance images.

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Index Terms:
machine vision; dynamic programming; variational problems; energy-minimizing active contours; optimization; discrete multistage decision process; computational costs; computer vision; dynamic programming; picture processing; variational techniques
A.A. Amini, T.E. Weymouth, R.C. Jain, "Using Dynamic Programming for Solving Variational Problems in Vision," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 12, no. 9, pp. 855-867, Sept. 1990, doi:10.1109/34.57681
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