Innovative Segmentation Approach Based on LRTM
S.Kezia1, I.Shanti Prabha2, V.VijayaKumar3
1S.Kezia, Electronics & Communication Engineering, CIET, Rajahmundry, Andhra Pradesh, India.
2Dr.I.Shanti Prabha, Professor, ECE Dept, JNTUK,Kakinada, Andhra Pradesh, India.
3Dr.V.Vijaya Kumar Professor & Dean, CSE ,IT & MCA Depts., GIET Rajahmundry, Andhra Pradesh, India.
Manuscript received on November 01, 2012. | Revised Manuscript received on November 02, 2012. | Manuscript published on November 05, 2012. | PP: 229-233 | Volume-2 Issue-5, November 2012. | Retrieval Number: E1049102512/2012©BEIESP
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© 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: Texture refers to the variation of gray level tones in a local neighbourhood. The “local” texture information for a given pixel and its neighbourhood is characterized by the corresponding texture unit. This paper describes new statistical approaches for texture segmentation, based on minimum and maximum of fuzzy left and right texture unit matrix. In these methods, the “local” texture information for a given pixel and its neighbourhood is characterized by the corresponding fuzzy texture unit. The proposed Minimum Fuzzy Left and Right Texture Unit Matrix (MFLRTU) and Maximum Fuzzy Left and Right Texture Unit Matrix (MXFLRTU) segmentation methods overcome the computational complexity of Fuzzy Texture Unit (FTU) by reducing the texture unit from 2020 to 79. The proposed schemes are compared with the Wavelet Transform with Image Fusion (WTIF) in [20]. The results demonstrate the efficacy of the proposed methods.
Keywords: Fuzzy Texture unit, Left Right Texture Unit Matrix, Texture Spectrum, Texture.