Simultaneous object extraction and disparity estimation using stochastic diffusion

Sang Hwa Lee, Nam Ik Cho, Yasuaki Kanatsugu, Jong-Il Park

Abstract: This paper proposes a Bayesian maximum a posteriori (MAP) method to estimate disparity field and to extract objects in the stereoscopic images. The disparity and segmentation fields are modelled as the Markov random fields (MRFs), and are estimated by a stochastic approach called stochastic diffusion. The stochastic diffusion is a new energy minimization method to search for the solution fields in the MAP estimation. A clustering method utilizes the disparity field and color information to classify the regions into foreground objects or background. The line field is also included to improve the detection of the object boundaries. According to the some experiments, the proposed approach shows good performances in the foreground objects extraction even in the complex background.