Unsupervised Joint Image Denoising and Active Contour Segmentation in Multidimensional Feature Space. (20th July 2016)

• Record Type: Journal Article
• Authors: Ge, Qi; Jing, Xiao-Yuan; Wu, Fei; Yan, Jingjie; Li, Hai-Bo
• Other Names: Colicchio Giuseppina Academic Editor.
• Is Part Of: Mathematical problems in engineering. Volume 2016(2016)
• Publication Date: 2016-07-20
• Subjects: Engineering mathematics -- Periodicals; 510.2462
• DOI: 10.1155/2016/3909645
• Languages: English
• ISSNs: 1024-123X

Abstract

Abstract : 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.