Unsupervised Joint Image Denoising and Active Contour Segmentation in Multidimensional Feature Space

Mathematical Problems in Engineering. 2016;2016 DOI 10.1155/2016/3909645

 

Journal Homepage

Journal Title: Mathematical Problems in Engineering

ISSN: 1024-123X (Print); 1563-5147 (Online)

Publisher: Hindawi Limited

LCC Subject Category: Technology: Engineering (General). Civil engineering (General) | Science: Mathematics

Country of publisher: United Kingdom

Language of fulltext: English

Full-text formats available: PDF, HTML, ePUB, XML

 

AUTHORS


Qi Ge (College of Telecommunications and Information Engineering, Nanjing University of Posts and Telecommunications, Nanjing 210003, China)

Xiao-Yuan Jing (College of Automation, Nanjing University of Posts and Telecommunications, Nanjing 210003, China)

Fei Wu (College of Automation, Nanjing University of Posts and Telecommunications, Nanjing 210003, China)

Jingjie Yan (College of Telecommunications and Information Engineering, Nanjing University of Posts and Telecommunications, Nanjing 210003, China)

Hai-Bo Li (College of Telecommunications and Information Engineering, Nanjing University of Posts and Telecommunications, Nanjing 210003, China)

EDITORIAL INFORMATION

Blind peer review

Editorial Board

Instructions for authors

Time From Submission to Publication: 26 weeks

 

Abstract | Full Text

We describe a new method for simultaneous image denoising and level set-based active contour segmentation using multidimensional features. We consider an image to be a surface embedded in a Riemannian manifold. By defining a metric in the embedded space, which in our case includes multidimensional image features as well as a level set-based active contour model, a minimization problem in the image space can be obtained through the Polyakov action framework. The resulting minimization problem is solved with a dual algorithm for efficiency. Benefits of this new method include the fact that it is independent of any artificial “running” parameters, and experiments using both synthetic and real images show that the method is robust with respect to noise and blurry object boundaries.