MAP-Based Object Extraction from Uncalibrated Image Pair

In this paper, we propose a new MAP algorithm for foreground objects extraction from uncalibrated image pairs. The segmentation is performed in the MAP framework with MRF (Markov random field) modeling of images. The proposed algorithm estimates several spatial transformations between two images by corresponding SIFT (scale invariant feature transform) points and sequential RANSAC (random sample consensus) algorithm. The area-ratio criterion is applied to the each transformation so that we select the transformation of foreground object. Using these transformations, we compute the likelihood of color-segmented subregions. We model the prior information which is based on smoothness condition. Finally, the object extraction is performed by the Bayesian belief propagation. Experiments on various image pairs and video sequences show promising results in extracting the foreground objects from the backgrounds.