Volume 2, Issue 2
Coefficient of Variation Based Image Selective Segmentation Model Using Active Contours

Noor Badshah, Ke Chen, Haider Ali & Ghulam Murtaza

East Asian J. Appl. Math., 2 (2012), pp. 150-169.

Published online: 2018-02

Preview Full PDF 3 452
Export citation
  • Abstract

Most image segmentation techniques efficiently segment images with prominent edges, but are less efficient for some images with low frequencies and overlapping regions of homogeneous intensities. A recently proposed selective segmentation model often works well, but not for such challenging images. In this paper, we introduce a new model using the coefficient of variation as a fidelity term, and our test results show it performs much better in these challenging cases.

  • Keywords

Segmentation Coefficient of Variation (CoV) level set functional minimisiation Total Variation (TV)

  • AMS Subject Headings

68U10 62G30

  • Copyright

COPYRIGHT: © Global Science Press

  • Email address
  • References
  • Hide All
    View All

@Article{EAJAM-2-150, author = {Noor Badshah, Ke Chen, Haider Ali and Ghulam Murtaza}, title = {Coefficient of Variation Based Image Selective Segmentation Model Using Active Contours}, journal = {East Asian Journal on Applied Mathematics}, year = {2018}, volume = {2}, number = {2}, pages = {150--169}, abstract = {

Most image segmentation techniques efficiently segment images with prominent edges, but are less efficient for some images with low frequencies and overlapping regions of homogeneous intensities. A recently proposed selective segmentation model often works well, but not for such challenging images. In this paper, we introduce a new model using the coefficient of variation as a fidelity term, and our test results show it performs much better in these challenging cases.

}, issn = {2079-7370}, doi = {https://doi.org/10.4208/eajam.090312.190412a}, url = {http://global-sci.org/intro/article_detail/eajam/10872.html} }
Copy to clipboard
The citation has been copied to your clipboard