Texture Segmentation of Natural Images Based on Active Contour Model

Zhan Wang; Yun Hui Yan; De Wei Dong; Ke Chen Song

doi:10.4028/www.scientific.net/AMR.546-547.553

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HomeAdvanced Materials ResearchAdvanced Materials Research Vols. 546-547Texture Segmentation of Natural Images Based on...

Texture Segmentation of Natural Images Based on Active Contour Model

Abstract:

To segment complex texture natural environment images; the first, the texture features of natural images should be analysed and the texture features should be extracted; The second, texture images segmengtation can be achieved by using Mumford-Shah active contour model, this segmentation model can better process fuzzy, default boundary, and this model can be solved by level set method. This method can express well complex texture signal features of natural images. Through making texture segmentation experiment for standard texture synthesis image and natural environmental image, its results show that the texture segmentation based on Mumford-Shah active contour model can segment natural images.

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Periodical:

Advanced Materials Research (Volumes 546-547)

Pages:

553-558

DOI:

https://doi.org/10.4028/www.scientific.net/AMR.546-547.553

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Online since:

July 2012

Authors:

Zhan Wang, Yun Hui Yan, De Wei Dong, Ke Chen Song

Keywords:

Active Contour, Feature Extraction, Level Set, Texture Segmentation

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References

[1] S. Chen, A. Nir, Y. Yehoshua: Integrated Active Contour for Texture Segmentation,. IEEE Transaction on Image Processing, 15, 6. 1633-1646, (2006).

Google Scholar

[2] B. Sandberg, T. Chan, V. Vese. A level-set and Gabor-based active contour algorithm for segmenting textured images, , IEEE transactions on Image Processing, 15, 6. 02-39, (2002).

Google Scholar

[3] M. Rousson, T. Brox, R. Deriche. Active unsupervised texture segmentation on a diffusion based feature space, IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2. 699-704. (2003).

DOI: 10.1109/cvpr.2003.1211535

Google Scholar

[4] You Jianjie, Wang Pingan, Xia Deshen. Active Contour Model Combined Unsupervised Texture Segmentation ,. Journal of Computer-Aided Design & Computer Graphics, 18, 12. 1897-1903, (2006).

Google Scholar

[5] He Yuan, Luo Yupin, Hu Dongcheng. Unsupervised Texture Segmentation Based on Geodesic Active Regions, Journal of Software, 18, 17. 592-599, (2007).

DOI: 10.1360/jos180592

Google Scholar

[6] Tai. Sing. Lee. Image Representation Using 2D Gabor Wavelets,. IEEE Transation on Pattern Analysis And Machine Intelligence, 18, 10. 959-971, (1996).

DOI: 10.1109/34.541406

Google Scholar

[7] R. Malladi, J.A. Sethian, B.C. Vemuri. Shape modeling with front propagation: A level set approach,. IEEE Transation on Pattern Analysis And Machine Intelligence, 17, 2. 158-175, (1995).

DOI: 10.1109/34.368173

Google Scholar

[8] V. Caselles, R. Kimmel, G. Sapiro. Geodesic active contours,. International Journal of Computer Vision, 22. 61-79, (1997).

DOI: 10.1109/iccv.1995.466871

Google Scholar

[9] S. Osher, J.A. Sethian. Fronts propagating with curvature-dependent speed: algorithms based on Hamilton-Jacobi formulations,. Journal of computational physics, 79. 12-49, (1988).

DOI: 10.1016/0021-9991(88)90002-2

Google Scholar