Texture Segmentation: Different Methods
Vaijinath V. Bhosle1, Vrushsen P. Pawar2
1Mr. Vaijinath V. Bhosle, Computer Science, College Of Computer Science And Information Technology (COCSIT), Latur, Maharashtra, India.
2Dr. Vrushsen. P. Pawar, Department Of Computational Science, SRTM University Nanded, Maharashtra, India.

Manuscript received on October 20, 2013. | Revised Manuscript received on November 04, 2013. | Manuscript published on November 05, 2013. | PP: 69-74 | Volume-3 Issue-5, November 2013. | Retrieval Number: E1893113513/2013©BEIESP
Open Access | Ethics and Policies | Cite
© The Authors. Published By: Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)

Abstract: Image Segmentation is an important pixel base measurement of image processing, which often has a large impact on quantitative image analysis results. The texture is most important attribute in many image analysis or computer vision applications. The procedures developed for texture problem can be subdivided into four categories: structural approach, statistical approach, model based approach and filter based approach. Different definitions of texture are described, but more importance is given to filter based methods. Such as Fourier transform, Gabor, Thresholding, Histogram and wavelet transforms. These filters are used to VisTex images and Brodatz Textures Database. The main objective of this paper is to study different methods for texture segmentation.
Keywords: Texture segmentation, Gabor Filter, Thresholding, VisTex, Brodatz.