Document Type

Journal Article

Department/Unit

Department of Computer Science

Title

Active contours with a joint and region-scalable distribution metric for interactive natural image segmentation

Language

English

Abstract

© The Institution of Engineering and Technology 2014. 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.

Publication Date

2014

Source Publication Title

IET Image Processing

Volume

8

Issue

12

Start Page

824

End Page

832

Publisher

Institution of Engineering and Technology

DOI

10.1049/iet-ipr.2013.0594

Link to Publisher's Edition

http://dx.doi.org/10.1049/iet-ipr.2013.0594

ISSN (print)

17519659

ISSN (electronic)

17519667

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