Approximation of salient contours in cluttered scenes

This paper proposes a new approach to describe the salient contours in cluttered scenes. No need to do the preprocessing, such as edge detection, we directly use a set of random straight line segments, as the intermediate level vision tokens, to approximate the salient contours. This line set is modeled by a stochastic framework, marked point process, in which the point denotes the center of lines, and the marker denotes the orientation and length of lines. Generic Gastalt factors of proximity and collinear continuity are embedded to constraint the geometrical inter-relations between lines. Different data likelihoods are used on synthetic and real images. Optimization is done by simulated annealing using Reversible Jump Markov chain Monte Carlo. Our results not only have a good approximation to the salient contours, also make other post-processing application more robust.