M. Srinivasa Rao

Work place: Dept of CSE, Sri Vasavi Institute of Engineering & Technology, pedana, Andhrapradesh, India

E-mail: srinu.mekala@gmail.com


Research Interests: Software Engineering, Computational Engineering, Computational Science and Engineering


M. Srinivasa Rao received the B.Tech Computer Science & Engineering from Nagarjuna University in 1998. He completed M.Tech in Software Engineering from JNT University, Masab Tank, and Hyderabad, India in 2001. He is having nearly 15 years of teaching and industrial experience. He is currently working as Asso ciate Professor, Dept of C.S.E, Sri Vasavi Institute of Engineering & Technology, pedana, Andhrapradesh, India. He is pursuing his Ph.D. from JNT University, Kakinada in Computer Science & Engineering under the guidance of Dr. V. Vijaya Kumar. He is a life member of ISTE and CSI. He published 5 papers in various conferences and journals.

Author Articles
Texture Classification based on Local Features Using Dual Neighborhood Approach

By M. Srinivasa Rao V.Vijaya Kumar MHM Krishna Prasad

DOI: https://doi.org/10.5815/ijigsp.2017.09.07, Pub. Date: 8 Sep. 2017

Texture classification and analysis are the most significant research topics in computer vision. Local binary pattern (LBP) derives distinctive features of textures. The robustness of LBP against gray-scale and monotonic variations and computational advantage have made it popular in various texture analysis applications. The histogram techniques based on LBP is complex task. Later uniform local binary pattern’s (ULBP) are derived on LBP based on bit wise transitions. The ULBP’s are rotationally invariant. The ULBP approach treated all non-uniform local binary pattern’s (NULBP) into one miscellaneous label. This paper presents a new texture classification method incorporating the properties of ULBP and grey-level co-occurrence matrix (GLCM). This paper derives ternary patterns on the ULBP and divides the 3 x 3 neighborhood in to dual neighborhood. The ternary pattern mitigates the noise problems particularly near uniform regions. The dual neighborhood reduces the range of texture unit from 0 to 6561 to 0 to 80. The GLCM features extracted from ULBP-dual texture matrix (ULBP-DTM) provide complete texture information about the image and reduce the texture unit range. Various machine learning classifiers are used for classification purpose. The performance of the proposed method is tested on Brodtaz, Outex and UIUC’s textures and compared with GLCM, texture spectrum (TS) and cross-diagonal texture matrix (CDTM) approaches.

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Texture Classification based on First Order Local Ternary Direction Patterns

By M. Srinivasa Rao V.Vijaya Kumar MHM Krishna Prasad

DOI: https://doi.org/10.5815/ijigsp.2017.02.06, Pub. Date: 8 Feb. 2017

The local binary pattern (LBP) and local ternary pattern (LTP) are basically gray scale invariant, and they encode the binary/ ternary relationship between the neighboring pixels and central pixel based on their grey level differences and derives a unique code. These traditional local patterns ignore the directional information. The proposed method encodes the relationship between the central pixel and two of its neighboring pixel located in different angles (α, β) with different directions. To estimate the directional patterns, the present paper derived variation in local direction patterns in between the two derivates of first order and derived a unique First order –Local Direction variation pattern (FO-LDVP) code. The FO-LDVP evaluated the possible direction variation pattern for central pixel by measuring the first order derivate relationship among the horizontal and vertical neighbors (0o Vs.90o; 90o Vs. 180o ; 180o Vs.270o ; 270o Vs. 0o) and derived a unique code. The performance of the proposed method is compared with LBP, LTP, LBPv, TS and CDTM using the benchmark texture databases viz. Brodtaz and MIT VisTex. The performance analysis shows the efficiency of the proposed method over the existing methods. 

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