Combining Local and Global Cues for Closed Contour Extraction

Algorithms for computing closed contours are generally based upon local Gestalt cues relating pairs of oriented elements, and a Markov assumption to then group these elements into chains. Without additional global constraints, these algorithms generally do not perform well on general natural scenes. Such global cues could include symmetry, shape priors or global colour appearance. A key challenge is to combine these local and global cues in a statistically optimal way. Here we propose a novel, effective method for rigorously combining local and global cues, both at the stage of forming new closed contour hypotheses, and at the stage of evaluating and ranking these hypotheses. We also demonstrate the importance of promoting the diversity of hypotheses. We evaluate our results on a standard public dataset, and demonstrate a substantial performance improvement over prior methods.