Abstract
Perceptual grouping of the complete boundaries of objects in natural images remains an unsolved problem in computer vision. The computational complexity of the problem and difficulties capturing global constraints limit the performance of current algorithms. In this paper we develop a coarse-to-fine Bayesian algorithm which addresses these constraints. Candidate contours are extracted at a coarse scale and then used to generate spatial priors on the location of possible contours at finer scales. In this way, a rough estimate of the shape of an object is progressively refined. The coarse estimate provides robustness to texture and clutter while the refinement process allows for the extraction of detailed object contours. The grouping algorithm is probabilistic and uses multiple grouping cues derived from natural scene statistics. We present a quantitative evaluation of grouping performance on the Berkeley Segmentation Database, and show that the multi-scale approach outperforms several single-scale contour extraction algorithms.
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