Please use this identifier to cite or link to this item:
|Scopus||Web of Science®||Altmetric|
|Title:||A computational model of the short-cut rule for 2D shape decomposition|
|Citation:||IEEE Transactions on Image Processing, 2015; 24(1):273-283|
|Publisher:||Institute of Electrical and Electronics Engineers|
|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|
|Appears in Collections:||Computer Science publications|
Files in This Item:
There are no files associated with this item.
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.