General Formulation and a New Algorithm of Stereo Matching Based on Bayesian Model
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In this paper, the general formula of disparity estimation based on Bayesian Maximum A Posteriori (MAP) algorithm is derived and implemented with simplified probabilistic models. The probabilistic models are independence and similarity among the neighboring disparities in the configuration. The independence probabilistic model guaranteed the discontinuity at the object boundary region, and the similarity model does the continuity or the high correlation of the disparity distribution. The formula is the generalized probabilistic diffusion equation based on Bayesian model, and can be implemented into the some different forms corresponding to the probabilistic models in the disparity neighborhood system or configuration. According to the experimental results, the proposed algorithm outperformed the other ones, such as sum of squared difference (SSD) based algorithm and Scharstein’s method. We can conclude that the derived formula generalizes the probabilistic diffusion based on Bayesian MAP algorithm for disparity estimation, and the proposed probabilistic models are reasonable and approximate the pure joint probability distribution very well with decreasing the computations to 0.01% calculations of the generalized formula.
Abstract
In this paper, the general formula of disparity estimation based on Bayesian Maximum A Posteriori (MAP) algorithm is derived and implemented with simplified probabilistic models. The probabilistic models are independence and similarity among the neighboring disparities in the configuration. The independence probabilistic model guaranteed the discontinuity at the object boundary region, and the similarity model does the continuity or the high correlation of the disparity distribution. The formula is the generalized probabilistic diffusion equation based on Bayesian model, and can be implemented into the some different forms corresponding to the probabilistic models in the disparity neighborhood system or configuration. According to the experimental results, the proposed algorithm outperformed the other ones, such as sum of squared difference (SSD) based algorithm and Scharstein’s method. We can conclude that the derived formula generalizes the probabilistic diffusion based on Bayesian MAP algorithm for disparity estimation, and the proposed probabilistic models are reasonable and approximate the pure joint probability distribution very well with decreasing the computations to 0.01% calculations of the generalized formula.