Parts of Visual Form: Computational Aspects
IEEE Transactions on Pattern Analysis and Machine Intelligence
Convexity rule for shape decomposition based on discrete contour evolution
Computer Vision and Image Understanding
Shape Classification Using the Inner-Distance
IEEE Transactions on Pattern Analysis and Machine Intelligence
Object Detection with Discriminatively Trained Part-Based Models
IEEE Transactions on Pattern Analysis and Machine Intelligence
Skeleton growing and pruning with bending potential ratio
Pattern Recognition
Articulation-invariant representation of non-planar shapes
ECCV'10 Proceedings of the 11th European conference on computer vision conference on Computer vision: Part III
Shape partitioning by convexity
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
Minimum near-convex decomposition for robust shape representation
ICCV '11 Proceedings of the 2011 International Conference on Computer Vision
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The aim of this work is to decompose shapes into parts which are consistent to human perception. We propose a novel shape decomposition method which utilizes the three perception rules suggested by psychological study: the Minima rule, the Short-cut rule and the Convexity rule. Unlike the previous work, we focus on improving the convexity of the decomposed parts while minimizing the cut length as much as possible. The problem is formulated as a combinatorial optimization problem and solved by a quadratic programming method. In addition, we consider the curved branches which introduce "false" concavity. To solve this problem, we straighten the curved branches before shape decomposition which makes the results more consistent with human perception. We test our approach on the MPEG-7 shape dataset, and the comparison results to previous work show that the proposed method can improve the part convexity while keeping the cuts short, and the decomposition is more consistent with human perception.