Regular Articles

Stochastic contour approach for automatic image segmentation

[+] Author Affiliations
Zhong Li

The University of North Carolina at Charlotte, Department of Computer Science, Charlotte, North Carolina, 28223

Jianping Fan

The University of North Carolina at Charlotte, Department of Computer Science, Charlotte, North Carolina, 28223

J. Electron. Imaging. 18(4), 043004 (November 11, 2009). doi:10.1117/1.3257933
History: Received January 07, 2009; Revised August 13, 2009; Accepted September 10, 2009; Published November 11, 2009; Online November 11, 2009
Text Size: A A A

Automatic image segmentation is a fundamental and challenging work in image analysis. We present a stochastic contour approach that draws the contour by multiple agents stochastically, each driven by a simple policy. A contour confidence map is formed, and the image is partitioned hierarchically according to the probability of being surrounded by an average contour. The segmentation is formed by truncating the hierarchical tree based on the dissimilarity increment. The average contour formed in the stochastic contour approach no longer depends on the initial conditions and tolerates less guaranteed convergence. The stochastic contour evolution provides perturbation to jump out of local minima, while the average contour handles model uncertainty naturally. No training process is involved in this approach. The experimental evaluation on a large amount of images with diverse visual properties has shown robustness and good performance of our technique.

Figures in this Article
© 2009 SPIE and IS&T

Citation

Zhong Li and Jianping Fan
"Stochastic contour approach for automatic image segmentation", J. Electron. Imaging. 18(4), 043004 (November 11, 2009). ; http://dx.doi.org/10.1117/1.3257933


Access This Article
Sign in or Create a personal account to Buy this article ($20 for members, $25 for non-members).

Some tools below are only available to our subscribers or users with an online account.

Related Content

Customize your page view by dragging & repositioning the boxes below.

Related Book Chapters

Topic Collections

PubMed Articles
Convolutional Oriented Boundaries: From Image Segmentation to High-Level Tasks. IEEE Trans Pattern Anal Mach Intell Published online May 02, 2017;
Advertisement
  • Don't have an account?
  • Subscribe to the SPIE Digital Library
  • Create a FREE account to sign up for Digital Library content alerts and gain access to institutional subscriptions remotely.
Access This Article
Sign in or Create a personal account to Buy this article ($20 for members, $25 for non-members).
Access This Proceeding
Sign in or Create a personal account to Buy this article ($15 for members, $18 for non-members).
Access This Chapter

Access to SPIE eBooks is limited to subscribing institutions and is not available as part of a personal subscription. Print or electronic versions of individual SPIE books may be purchased via SPIE.org.