To complete or not to complete: Gap completion in real images

Gap Completion is a key step in the process of linking of edges into contours towards generating meaningful boundaries. The likelihood of completing a gap between two edges has been approached either indirectly through studying optimal completion contours such as Elastica and Euler Spiral, or through the statistics of co-occurrence. These studies do not address the issue of finding appropriate candidates for gap completion and do not typically address the role of appearance and contextual contours. The paper´s contributions are twofold. First, we introduce a Gap Completion Ground-Truth Dataset (GCGD) which annotates human subjects´ completions using a local window around a potential gap when contours only or contours and appearance are presented. This dataset is proposed as a mechanism for evaluating gap completion strategies. Second, we show that a shock-based identification scheme identifies gap candidates effectively. We then introduce a scheme for ranking these candidates based on a likelihood measure. The results compare favorably to CDT-based gap completion when evaluated on the GCGD and on the Berkeley Segmentation Dataset.