Fast and active texture segmentation based on orientation and local variance

Authors:
Qiang Chen;Jian Luo;Pheng Ann Heng;Xia De-shen
Affiliations:
The School of Computer Science and Technology, Nanjing University of Science and Technology, Nanjing, China;The School of Computer Science and Technology, Nanjing University of Science and Technology, Nanjing, China;Department of Computer Science and Engineering, The Chinese University of Hong Kong, Shatin, Hong Kong and Shun Hing Institute of Advanced Engineering, The Chinese University of Hong Kong, Shatin, ...;The School of Computer Science and Technology, Nanjing University of Science and Technology, Nanjing, China
Venue:
Journal of Visual Communication and Image Representation
Year:
2007

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Abstract

This paper describes a fast and active texture segmentation approach based on the orientation and the local variance. First, a set of feature images are extracted using the orientation and the local variance. To reduce the computational complexity, a separability measurement method, which is used for selecting four feature images with good separability in four orientations, is proposed in this paper. To improve the segmentation, we adopt a nonlinear diffusion filtering to smooth the four feature images. Finally, a variational framework incorporating these features in a level set based, unsupervised segmentation process is adopted. To improve the computational speed, instead of solving the Euler-Lagrange equation, we calculate the energy, with level set representation, to solve the variational framework. Segmentation results of various synthetic and real textured images has demonstrated that our method has good performance and efficiency.