Signal-walking-driven active contour model

Ming Hong Pi; Jun Ma; Wei Zhang; Zhigang Zhou

Signal-walking-driven active contour model

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Author(s): Ming Hong Pi ¹ ; Jun Ma ¹ ; Wei Zhang ² ; Zhigang Zhou ¹
- Affiliations: 1: School of Computing Science, Wuhan Textile University, 1 Fangzhi Road, Wuhan, Hubei 430073, People's Republic of China ;
  2: School of Mechanical and Materials Engineering, JiuJiang University, 551 Qianjin E Road, Lushan, JiuJiang, JiangXi, People's Republic of China
Source: Volume 9, Issue 12, December 2015, p. 1101 – 1106
DOI: 10.1049/iet-ipr.2014.0932 , Print ISSN 1751-9659, Online ISSN 1751-9667

Received 17/08/2014, Accepted 22/06/2015, Revised 12/06/2015, Published 01/12/2015

Active contour models are effective image segmentation methods. However, they are very time-consuming, and their convergence depends upon the choice of initial contour. To overcome the two drawbacks, in the study, the authors suggest a signal-walking-driven active contour model. By walking a signal, they construct a forest of object evolution. Each tree grows from a root object, and child node contains its shrunk or/and split version. The merit value of an object is a composite metric from the colour, edge, or/and shape properties. The merit function plays an important role in tree construction and the goodness of object evolution. The objects are selected and added to the tree in the levels the merit function reaches the local maxima. After the forest of object evolution is constructed, by traversing each tree branch in post-order, the objects corresponding to maximum merit values are extracted as the final segmentation. Experimental results on a set of oil-sand images indicate the proposed signal-walking-driven active contour model outperforms Chan and Vese's model and adaptive thresholding.

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