Open AccessActive contours with a joint and region‐scalable distribution metric for interactive natural image segmentation
Author(s) -
Liu Xin,
Peng ShuJuan,
Cheung Yiuming,
Tang Yuan Yan,
Du JiXiang
Publication year - 2014
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.2013.0594
Subject(s) - segmentation , subspace topology , image segmentation , scale space segmentation , computer science , metric (unit) , artificial intelligence , segmentation based object categorization , homogeneity (statistics) , scalability , binary number , joint probability distribution , computer vision , filter (signal processing) , pattern recognition (psychology) , mathematics , machine learning , statistics , arithmetic , database , economics , operations management
In this study, we present an efficient active contour with a joint and region‐scalable distribution metric for interactive natural image segmentation. First, the authors project a red–green–blue image into the CIELab colour space and employ independent component analysis to select two subspace channels. Then, by initialising the evolving curve interactively in terms of a polygonal curve or multiple polygonal curves, they compute a joint probability distribution associated with a region‐scalable mask to model the regional statistics and propose a simple but effective distribution metric to regularise the active contours. Subsequently, they convert the resultant level set function into binary pattern and find the larger 8‐connected regions as the desired objects. Finally, the selected regions are smoothed with a circular averaging filter such that the final segmentation results can be obtained. The proposed approach not only can deal with the complex appearance and intensity in homogeneity, but also has the advantages of fast convergence and easy implementation. The experiments have shown the precise and reliable segmentation results in comparison with the state‐of‐the‐art competing approaches.