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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...
Robust tracking of abrupt motion is a challenging task in computer vision due to the large motion uncertainty. In this paper, we propose a stochastic approximation Monte Carlo (SAMC) based tracking scheme for abrupt motion problem in Bayesian filtering framework. In our tracking scheme, the particle weight is dynamically estimated by learning the density of states in simulations, and thus the local-trap...
Tracking of human sperm cells is a challenging task in computer vision due to the motion uncertainty. In this paper, we propose an efficient and effective algorithm for sperm cells tracking which attempts to capture the motion uncertainty of the target object. The tracking problem is formulated within the Bayesian filter framework. To address this problem, we incorporate an orientation adaptive mean...
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