Author_Institution :
Inst. of Inf. Sci., Acad. Sinica, Taipei, Taiwan
Abstract :
We describe a generalized shape-axis (SA) model for representing both open and closed planar curves. The SA model is an effective way to represent shapes by comparing their self-similarities. Given a 2D shape, whether it is closed or open, we use two different parametrizations for the curve. To study the self-similarity, the two parametrizations are matched to each other via a variational framework, where the self-similarity criterion is to be defined depending on the class of shapes and human perception factors. Useful self-similarity criteria include symmetry, parallelism and convexity, etc. A match is allowed to have discontinuities, and the optimal match can be computed by a dynamic programming algorithm in O(N4) time, where N is the size of the shape. We use a grouping process for the shape axis to construct a unique SA-tree, however, when a planar shape is open, it is possible to derive an SA-forest. The generalized SA model provides a compact and informative way for 2D shape representation
Keywords :
computational complexity; dynamic programming; image representation; pattern matching; trees (mathematics); 2D shape representation; dynamic programming; image representation; planar curves; self-similarity; shape-axis model; shape-axis trees; symmetry; time complexity; Clocks; Computational modeling; Computer vision; Dynamic programming; Equations; Heuristic algorithms; Humans; Optimal matching; Parallel processing; Shape;