Name
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. |
Year | DOI | Venue |
|---|---|---|
2010 | 10.1109/ICIP.2010.5649396 | ICIP |
Keywords | Field | DocType |
split-and-merge,adaptive mcmc sampling,stochastic search scheme,stochastic processes,stochastic approximation monte carlo,adaptive signal processing,energy minimization framework,polygonal approximation,global optimization,digital curve,adaptive mcmc,monte carlo methods,computer vision,split-and-merge process,monte carlo sampling,digital planar curve,local search,energy minimization,approximation algorithms,optimization,merging,algorithm design and analysis,stochastic approximation,monte carlo,sampling methods | Approximation algorithm,Monte Carlo method,Mathematical optimization,Markov chain Monte Carlo,Global optimization,Computer science,Sampling (statistics),Adaptive filter,Local search (optimization),Stochastic approximation | Conference |
ISSN | ISBN | Citations |
1522-4880 E-ISBN : 978-1-4244-7993-1 | 978-1-4244-7993-1 | 1 |
PageRank | References | Authors |
0.36 | 4 | 2 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Xiuzhuang Zhou | 1 | 380 | 20.26 |
| Yao Lu | 2 | 98 | 19.25 |