A Kernel PCA Shape Prior and Edge Based MRF Image Segmentation. Issue 5 (1st September 2016)

• Record Type: Journal Article
• Authors: Wang, Xili; Zhang, Wei; Ji, Qiang
• Is Part Of: Chinese journal of electronics. Volume 25:Issue 5(2016)
• Page Start: 892
• Page End: 900
• Publication Date: 2016-09-01
• Subjects: Kernel Principal component analysis (PCA) -- Shape prior -- Edge -- Markov random field (MRF) -- Image segmentation; edge detection -- graph theory -- image representation -- image segmentation -- Markov processes -- object recognition -- principal component analysis -- shape recognition -- statistical analysis; kernel PCA shape prior -- edge based MRF image segmentation -- edge information -- Markov random field -- kernel principal component analysis -- statistical shape representation -- edge extraction -- MRF energy function -- integrated energy function -- graph cuts -- alignment procedure -- shape templates; Electronics -- Periodicals; Electronics -- China -- Periodicals; Electronics; China; Periodicals; 621.38105
• DOI: 10.1049/cje.2016.08.017
• Languages: English
• ISSNs: 1022-4653

Abstract

Abstract : We introduce both shape prior and edge information to Markov random field (MRF) to segment target of interest in images. Kernel Principal component analysis (PCA) is performed on a set of training shapes to obtain statistical shape representation. Edges are extracted directly from images. Both of them are added to the MRF energy function and the integrated energy function is minimized by graph cuts. An alignment procedure is presented to deal with variations between the target object and shape templates. Edge information makes the influence of inaccurate shape alignment not too severe, and brings result smoother. The experiments indicate that shape and edge play important roles for complete and robust foreground segmentation.