Grouping active contour fragments for object recognition
ACCV'12 Proceedings of the 11th Asian conference on Computer Vision - Volume Part I
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Contour Completion plays an important role in visual perception, where the goal is to group fragmented low-level edge elements into perceptually coherent and salient contours. This process is often considered as guided by some middle-level Gestalt principles. Most existing methods for contour completion have focused on utilizing rather local Gestalt laws such as good-continuity and proximity. In contrast, much fewer methods have addressed the global contour closure effect, despite that many psychological evidences have shown the usefulness of closure in perceptual grouping. This paper proposes a novel higher-order CRF model to address the contour closure effect, through local connectedness approximation. This leads to a simplified problem structure, where the higher-order inference can be formulated as an integer linear program (ILP) and solved by an efficient cutting-plane variant. Tested on the BSDS benchmark, our method achieves a comparable precision-recall performance, a superior contour grouping ability (measured by Rand index), and more visually pleasing results, compared with existing methods.