Curve registration problems in data analysis and computer vision can often be reduced to the problem of matching two functions defined on an interval. Dynamic Programming (DP) is an effective approach to solve this problem. In this paper, we propose a DP algorithm that runs in O(N ) time to compute optimal diffeomorphisms for elastic registration of curves with N nodes. This algorithm contrasts favorably with other DP algorithms used for this problem: the commonly used algorithm of quadratic

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... e complexity, and the algorithm that guarantees a globally optimal solution with O(N 4 ) time complexity. Key to our computational efficiency is the savings achieved by reducing our search space, focusing on thin strips around graphs of estimates of optimal diffeomorphism. Estimates and strips are obtained with a multigrid approach: an optimal diffeomorphism obtained from a lower resolution grid using DP is progressively projected to ones of higher resolution until full resolution is attained. Additionally, our DP algorithm is designed so that it can handle nonuniformly discretized curves. This enables us to realize further savings in computations, since in the case of complicated curves requiring large numbers of nodes for a high-fidelity representation, we can distribute curve nodes adaptively, focusing nodes in parts of high variation. We demonstrate effectiveness of our DP algorithm on several registration problems in elastic shape analysis, and functional data analysis.