B Sujatha

Work place: GIET Rajahmundry, A.P, INDIA

E-mail: birudusujatha@gmail.com


Research Interests: Image Processing, Pattern Recognition


B.Sujatha received the B.Tech. degree from JNT University, Kakinada in 1997 and received her M. Tech. (Computer Science & Engineering) from Andhra University in 2002. She is having 10 years of teaching experience. Presently she is working as an Assoc. Professor in GIET, Rajahmundary. She has published 15 research publications in Inter National Journal. She is a member of SRRF-GIET, Rajahmundry. She is pursuing her Ph.D from Mysore University in Computer Science under the guidance of Dr. V.Vijaya Kumar. Her research interest includes Image Processing and Pattern Recognition. She is a Life member of ISCA.

Author Articles
A New Approach for Texture Classification Based on Average Fuzzy Left Right Texture Unit Approach

By Y Venkateswarlu B Sujatha JVR Murthy

DOI: https://doi.org/10.5815/ijigsp.2012.12.08, Pub. Date: 8 Nov. 2012

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. Based on the concept of texture unit, this paper describes a new statistical approach to texture analysis, based on average of the both fuzzy left and right texture unit matrix. In this method the “local” texture information for a given pixel and its neighbourhood is characterized by the corresponding fuzzy texture unit. The proposed Average Fuzzy Left and Right Texture Unit (AFLRTU) matrices overcome the disadvantage of FTU by reducing the texture unit from 2020 to 79. The proposed scheme also overcomes the disadvantage of the left and right texture unit matrix (LRTM) by considering the texture unit numbers from all the 4 different LRTM’s instead of the minimum one as in the case of LRTM. The co-occurrence features extracted from the AFLRTU matrix provide complete texture information about an image, which is useful for texture classification. Classification performance is compared with the various fuzzy based texture classification methods. The results demonstrate that superior performance is achieved by the proposed method.

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Texture Classification Based on Texton Features

By U Ravi Babu V.Vijayakumar B Sujatha

DOI: https://doi.org/10.5815/ijigsp.2012.08.05, Pub. Date: 8 Aug. 2012

Texture Analysis plays an important role in the interpretation, understanding and recognition of terrain, biomedical or microscopic images. To achieve high accuracy in classification the present paper proposes a new method on textons. Each texture analysis method depends upon how the selected texture features characterizes image. Whenever a new texture feature is derived it is tested whether it precisely classifies the textures. Here not only the texture features are important but also the way in which they are applied is also important and significant for a crucial, precise and accurate texture classification and analysis. The present paper proposes a new method on textons, for an efficient rotationally invariant texture classification. The proposed Texton Features (TF) evaluates the relationship between the values of neighboring pixels. The proposed classification algorithm evaluates the histogram based techniques on TF for a precise classification. The experimental results on various stone textures indicate the efficacy of the proposed method when compared to other methods.

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