Dual-Space Decomposition of 2D Complex Shapes

Guilin Liu, Zhonghua Xi and Jyh-Ming Lien

While techniques that segment shapes into visually meaningful parts have generated impressive results, these techniques also
have only focused on relatively simple shapes, such as those composed of a single object either without holes or with few simple holes. In many applications, shapes created from images can contain many overlapping objects and holes. These holes may come from sensor noise or may have important part of the shape and arbitrarily complex. These complexities that appear in real-world 2D shapes can pose grand challenges to the existing part segmentation methods. In this paper, we propose a new decomposition method, called Dual-space Decomposition that handles complex 2D shapes by recognizing the importance of holes and classifying holes as either topological noise or structurally important features. Our method creates a nearly convex decomposition of a given shape by segmenting both positive and negative regions of the shape. We compare our results to segmentation produced by non-expert human subjects. Based on two evaluation methods, we show that this new decomposition method creates statistically similar and sometimes better segmentation comparing to those produced by human subjects.

Dual-Space Decomposition of 2D Complex Shapes, Guilin Liu and Zhonghua Xi and Jyh-Ming Lien, 27th IEEE Conference on Computer Vision and Pattern Recognition (CVPR), IEEE, Jun. 2014
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Related Links/Code

Human Cuts, Dual space decomposition(Dude2D) and Dude2D skeleton

Related Work/Links
Computer Science @ George Mason University