Bayesian range segmentation using focus cues
The objective of range segmentation is to partition a scene into regions with different depth ranges. We first perform range classification by combining two paradigms: focus cues and Bayesian estimation. A criterion function from focus cues provides a basic rule for measuring the ranges of a region in images. A Bayesian estimation is obtained by modeling the class field as a Markov random field (MRF). To combine these two paradigms, we define a combined energy function in terms of the cost function using the criterion function values for focus measure and the energy function of the Gibbs distribution of the class field. Then the combined energy function is minimized by a modified simulated annealing method to obtain range classification. The range classification is based on quantized ranges, and it provides an initial range segmentation. For range segmentation, we obtain interpolated range values, and perform a merging process by modeling the field of ranges as a Gaussian Markov random field. The range segmentation result gives a description of the 3-D structure of a scene.