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	Unsupervised texture segmentation based on geodesic active regions
	He Yuan ; Luo Yu-Pin ; Hu Dong-Cheng
	2010-05-06 ; 2010-05-06
关键词	Practical Theoretical or Mathematical/ differential geometry feature extraction Gabor filters Gaussian processes image segmentation image texture/ unsupervised texture segmentation geodesic active regions curve evolution multidimensional feature space Gabor filter bank Gaussian mixture model/ B6135 Optical, image and video signal processing B6140B Filtering methods in signal processing B0240Z Other topics in statistics C5260B Computer vision and image processing techniques C1140Z Other topics in statistics
中文摘要	This paper proposes an algorithm based on curve evolution for unsupervised texture segmentation. A multidimensional feature space is achieved by using a Gabor filter bank to extract texture features. To avoid deforming contours directly in a vector-valued space, a Gaussian mixture model (GMM) is used to describe the statistical distribution of the space and get the boundary and region probabilities. Then a framework of geodesic active regions is applied based on them to get final results. In the end, the experimental results demonstrate that this method can obtain satisfied boundaries between different texture regions.
语种	中文 ; 中文
出版者	Science Press ; China
内容类型	期刊论文
源URL	[http://hdl.handle.net/123456789/9364]
专题	清华大学
推荐引用方式 GB/T 7714	He Yuan,Luo Yu-Pin,Hu Dong-Cheng. Unsupervised texture segmentation based on geodesic active regions[J],2010, 2010.
APA	He Yuan,Luo Yu-Pin,&Hu Dong-Cheng.(2010).Unsupervised texture segmentation based on geodesic active regions..
MLA	He Yuan,et al."Unsupervised texture segmentation based on geodesic active regions".(2010).

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