Robust hand gesture recognition based on finger-earth mover's distance with a commodity depth camera
MM '11 Proceedings of the 19th ACM international conference on Multimedia
Robust hand gesture recognition with kinect sensor
MM '11 Proceedings of the 19th ACM international conference on Multimedia
SMI 2012: Full α-Decomposition of polygons
Computers and Graphics
Fast approximate convex decomposition using relative concavity
Computer-Aided Design
A clustering-based ensemble technique for shape decomposition
SSPR'12/SPR'12 Proceedings of the 2012 Joint IAPR international conference on Structural, Syntactic, and Statistical Pattern Recognition
Perceptually friendly shape decomposition by resolving segmentation points with minimum cost
Journal of Visual Communication and Image Representation
Toward perception-based shape decomposition
ACCV'12 Proceedings of the 11th Asian conference on Computer Vision - Volume Part II
Weak convex decomposition by lines-of-sight
SGP '13 Proceedings of the Eleventh Eurographics/ACMSIGGRAPH Symposium on Geometry Processing
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Shape decomposition is a fundamental problem for part-based shape representation. We propose a novel shape decomposition method called Minimum Near-Convex Decomposition (MNCD), which decomposes 2D and 3D arbitrary shapes into minimum number of "near-convex" parts. With the degree of near-convexity a user specified parameter, our decomposition is robust to large local distortions and shape deformation. The shape decomposition is formulated as a combinatorial optimization problem by minimizing the number of non-intersection cuts. Two major perception rules are also imposed into our scheme to improve the visual naturalness of the decomposition. The global optimal solution of this challenging discrete optimization problem is obtained by a dynamic subgradient-based branch-and-bound search. Both theoretical analysis and experiment results show that our approach outperforms the state-of-the-art results without introducing redundant parts. Finally we also show the superiority of our method in the application of hand gesture recognition.