A Kernel PCA Shape Prior and Edge Based MRF Image Segmentation

WANG Xili; ZHANG Wei; JI Qiang

doi:10.1049/cje.2016.08.017

Volume 25 Issue 5

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Article Navigation > Chinese Journal of Electronics > 2016 > 25(5): 892-900

WANG Xili, ZHANG Wei, JI Qiang, “A Kernel PCA Shape Prior and Edge Based MRF Image Segmentation,” Chinese Journal of Electronics, vol. 25, no. 5, pp. 892-900, 2016, doi: 10.1049/cje.2016.08.017

Citation:

WANG Xili, ZHANG Wei, JI Qiang, “A Kernel PCA Shape Prior and Edge Based MRF Image Segmentation,” Chinese Journal of Electronics, vol. 25, no. 5, pp. 892-900, 2016, doi: 10.1049/cje.2016.08.017

WANG Xili, ZHANG Wei, JI Qiang, “A Kernel PCA Shape Prior and Edge Based MRF Image Segmentation,” Chinese Journal of Electronics, vol. 25, no. 5, pp. 892-900, 2016, doi: 10.1049/cje.2016.08.017

Citation:

WANG Xili, ZHANG Wei, JI Qiang, “A Kernel PCA Shape Prior and Edge Based MRF Image Segmentation,” Chinese Journal of Electronics, vol. 25, no. 5, pp. 892-900, 2016, doi: 10.1049/cje.2016.08.017

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A Kernel PCA Shape Prior and Edge Based MRF Image Segmentation

doi: 10.1049/cje.2016.08.017

1.
School of Computer Science, Shaanxi Normal University, Xi'an 710062, China;
2.
Department of Electrical, Computer, and Systems Engineering, Rensselaer Polytechnic Institute, Troy 12180, USA

Funds: This work is supported by the National Natural Science Foundation of China (No.41171338, No.41471280), and partially supported by a visiting position with the Rensselaer Polytechnic Institute.

Received Date: 2014-11-05
Rev Recd Date: 2015-03-03
Publish Date: 2016-09-10

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.
- Kernel Principal component analysis (PCA),
- Shape prior,
- Edge,
- Markov random field (MRF),
- Image segmentation

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References(40)

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