Polygonal approximation of digital curves using adaptive MCMC sampling
Abstract: Polygonal approximation (PA) of the digital planar curves is an important topic in computer vision community. In this paper, we address this problem in the energy-minimization framework. We present a novel stochastic search scheme, which combines a split-and-merge process and a stochastic approximation Monte Carlo (SAMC) sampling procedure for global optimization. The SAMC sampling method can effectively handle the local-trap problem suffered by many local search methods, while the split-and-merge process is used to construct a more informative proposal distribution, and thus further improves the overall sampling efficiency. Experimental results on various benchmarks show that the proposed algorithm can achieve high-quality solutions and comparable results to those of state-of-the-art methods.
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