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

Xin Liu, Shu Juan Peng, Yiu Ming CHEUNG, Yuan Yan Tang, Ji Xiang Du

Research output: Contribution to journalArticlepeer-review

2 Citations (Scopus)

Abstract

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.

Original languageEnglish
Pages (from-to)824-832
Number of pages9
JournalIET Image Processing
Volume8
Issue number12
DOIs
Publication statusPublished - 1 Dec 2014

Scopus Subject Areas

  • Software
  • Signal Processing
  • Computer Vision and Pattern Recognition
  • Electrical and Electronic Engineering

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