Please use this identifier to cite or link to this item: http://hdl.handle.net/2440/98789
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Type: Journal article
Title: A computational model of the short-cut rule for 2D shape decomposition
Author: Luo, L.
Shen, C.
Liu, X.
Zhang, C.
Citation: IEEE Transactions on Image Processing, 2015; 24(1):273-283
Publisher: Institute of Electrical and Electronics Engineers
Issue Date: 2015
ISSN: 1057-7149
1941-0042
Statement of
Responsibility: 
Lei Luo, Chunhua Shen, Xinwang Liu and Chunyuan Zhang
Abstract: We propose a new 2D shape decomposition method based on the short-cut rule. The short-cut rule originates from cognition research, and states that the human visual system prefers to partition an object into parts using the shortest possible cuts. We propose and implement a computational model for the short-cut rule and apply it to the problem of shape decomposition. The model we proposed generates a set of cut hypotheses passing through the points on the silhouette, which represent the negative minima of curvature. We then show that most part-cut hypotheses can be eliminated by analysis of local properties of each. Finally, the remaining hypotheses are evaluated in ascending length order, which guarantees that of any pair of conflicting cuts only the shortest will be accepted. We demonstrate that, compared with state-of-the-art shape decomposition methods, the proposed approach achieves decomposition results, which better correspond to human intuition as revealed in psychological experiments.
Keywords: Short-cut rule; D shape decomposition; minima rule
Rights: © 2014 I EEE
RMID: 0030021568
DOI: 10.1109/TIP.2014.2376188
Grant ID: http://purl.org/au-research/grants/arc/FT120100969
Appears in Collections:Computer Science publications

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