Local- and Global-Statistics-Based Active Contour Model for Image Segmentation
Joint Authors
Source
Mathematical Problems in Engineering
Issue
Vol. 2012, Issue 2012 (31 Dec. 2012), pp.1-16, 16 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2012-04-12
Country of Publication
Egypt
No. of Pages
16
Main Subjects
Abstract EN
This paper presents a local- and global-statistics-based active contour model for image segmentation by applying the globally convex segmentation method.
We first propose a convex energy functional with a local-Gaussian-distribution-fitting term with spatially varying means and variances and an auxiliary global-intensity-fitting term.
A weight function that varies dynamically with the location of the image is applied to adjust the weight of the global-intensity-fitting term dynamically.
The weighted total variation norm is incorporated into the energy functional to detect boundaries easily.
The split Bregman method is then applied to minimize the proposed energy functional more efficiently.
Our model has been applied to synthetic and real images with promising results.
With the local-Gaussian-distribution-fitting term, our model can also handle some texture images.
Comparisons with other models show the advantages of our model.
American Psychological Association (APA)
Wu, Boying& Yang, Yunyun. 2012. Local- and Global-Statistics-Based Active Contour Model for Image Segmentation. Mathematical Problems in Engineering،Vol. 2012, no. 2012, pp.1-16.
https://search.emarefa.net/detail/BIM-1001923
Modern Language Association (MLA)
Wu, Boying& Yang, Yunyun. Local- and Global-Statistics-Based Active Contour Model for Image Segmentation. Mathematical Problems in Engineering No. 2012 (2012), pp.1-16.
https://search.emarefa.net/detail/BIM-1001923
American Medical Association (AMA)
Wu, Boying& Yang, Yunyun. Local- and Global-Statistics-Based Active Contour Model for Image Segmentation. Mathematical Problems in Engineering. 2012. Vol. 2012, no. 2012, pp.1-16.
https://search.emarefa.net/detail/BIM-1001923
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1001923