A Probabilistic Multi-scale Model for Contour Completion Based on Image Statistics

We derive a probabilistic multi-scale model for contour completion based on image statistics. The boundaries of human segmented images are used as "ground truth". A probabilistic formulation of contours demands a prior model and a measurement model. From the image statistics of boundary contours, we derive both the prior model of contour shape and the local likelihood model of image measurements. We observe multi-scale phenomena in the data, and accordingly propose a higher-order Markov model over scales for the contour continuity prior. Various image cues derived from orientation energy are evaluated and incorporated into the measurement model. Based on these models, we have designed a multi-scale algorithm for contour completion, which exploits both contour continuity and texture. Experimental results are shown on a wide range of images.

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