Open AccessSignal‐walking‐driven active contour model
Author(s) -
Pi Ming Hong,
Ma Jun,
Zhang Wei,
Zhou Zhigang
Publication year - 2015
Publication title -
iet image processing
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.401
H-Index - 45
eISSN - 1751-9667
pISSN - 1751-9659
DOI - 10.1049/iet-ipr.2014.0932
Subject(s) - active contour model , artificial intelligence , computer vision , computer science , tree (set theory) , segmentation , figure of merit , thresholding , image segmentation , level set (data structures) , traverse , maxima and minima , object (grammar) , tree structure , metric (unit) , mathematics , pattern recognition (psychology) , binary tree , image (mathematics) , algorithm , mathematical analysis , geodesy , geography , operations management , economics
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.