Minimum Near-Convex Shape Decomposition

Abstract

Shape decomposition is a fundamental problem for part-based shape representation. We propose the minimum near-convex decomposition (MNCD) to decompose arbitrary shapes into minimum number of "near-convex" parts. The near-convex shape decomposition is formulated as a discrete optimization problem by minimizing the number of nonintersecting cuts. Two perception rules are imposed as constraints into our objective function to improve the visual naturalness of the decomposition. With the degree of near-convexity a user-specified parameter, our decomposition is robust to local distortions and shape deformation. The optimization can be efficiently solved via binary integer linear programming. Both theoretical analysis and experiment results show that our approach outperforms the state-of-the-art results without introducing redundant parts and thus leads to robust shape representation.

Hand gesture recognition algorithm combining hand-type adaptive algorithm and effective-area ratio for efficient edge computing

“…Other algorithms consider fingers as the features and they are detected on the basis of ridge detection, [25][26][27][28][29][30] a circle drawn on the hand centroid, 31,32 or convex decomposition. 33 However, the method in Ref. 34 is time-consuming, while the others [28][29][30][31][32] are not effective to handle fingers with distortion.…”

Convex Decomposition And Efficient Shape Representation Using Deformable Convex Polytopes

“…Lien and Amato in [7] presented one of the first significant work in the area of ACD, by recursively resolving the most concave features until the concavity of every component is below some user specified threshold. Recently, in [8] and [10] mutax pairs are used to create fewer and more natural nearly convex shapes. What is common to most of the ACD papers in the literature is that they depend on computation and use of some form of convexity measure for their decomposition.…”