Texture Image Segmentation Using Without Re-initialization Geodesic Active Contour Model

DOI

10.2991/iske.2007.68How to use a DOI?

Keywords

texture image segmentation; local binary pattern; geodesic active contour; level set.

Abstract

A novel method of texture image segmentation is proposed, which has three advantages compared to other active contours. First, by combining the gray levels of pixels and texture information of an image, this method can be used for segmentation of a texture image or a non-texture image. Second, the method has low computation complexity because local binary pattern (LBP) is employed to extract texture features. And last, the proposed algorithm can avoid the additional computation problem without re-initialization of signal distance function repeatedly. The segmentation tests show that the proposed segmentation method is efficient, accurate, fast and robust.

Copyright

© 2007, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - CONF
AU  - Kaibin Wang
AU  - Bianzhang Yu
PY  - 2007/10
DA  - 2007/10
TI  - Texture Image Segmentation Using Without Re-initialization Geodesic Active Contour Model
BT  - Proceedings of the 2007 International Conference on Intelligent Systems and Knowledge Engineering (ISKE 2007)
PB  - Atlantis Press
SP  - 404
EP  - 408
SN  - 1951-6851
UR  - https://doi.org/10.2991/iske.2007.68
DO  - 10.2991/iske.2007.68
ID  - Wang2007/10
ER  -