Work place: Department of Computer Science and Engineering, Chaitanya Institute of Science and Technology Rajahmundry, AP, India
Research Interests: Engineering, Computational Engineering, Computational Science and Engineering
Dr.V.Venkata Krishna received B.Tech. (ECE) degree from Sri Venkateswara University. He completed his M. Tech. (Computer Science) from JNT University. He received his Ph.D in Computer Science from JNT University in 2004. He worked as Professor and Head for ten years in Mahatma Gandhi Institute of Technology, Hyderabad. Later he worked as a principal at VVCE, Hyderabad and CIST Kakinada. Presently he is working as Principal for Chaitanya Institute of Engineering and Technology, Rajahmundry. He is an advisory member for many Engineering colleges. He has published 30 research articles. Presently he is guiding 10 research scholars. He is a life member of ISTE and CSI.
DOI: https://doi.org/10.5815/ijigsp.2018.03.07, Pub. Date: 8 Mar. 2018
To extract local features efficiently Jhanwar et al. proposed Motif Co-occurrence Matrix (MCM)  in the literature. The Motifs or Peano Scan Motifs (PSM) is derived only on a 2*2 grid. The PSM are derived by fixing the initial position and this has resulted only six PSM’s on the 2*2 grid. This paper extended this ap-proach by deriving Motifs on a 3*3 neighborhood. This paper divided the 3*3 neighborhood into cross and diag-onal neighborhoods of 2*2 pixels. And on this cross and diagonal neighborhood complete Motifs are derived. The complete Motifs are different from initial Motifs, where the initial PSM positions are not fixed. This complete Motifs results 24 different Motifs on a 2*2 gird. This paper derived cross diagonal complete Motifs matrix (CD-CMM) that has relative frequencies of cross and diagonal complete Motifs. The GLCM features are de-rived on cross diagonal complete Motifs texture matrix for efficient face recognition. The proposed CD-CMM is evaluated face recognition rate on four popular face recognition databases and the face recognition rate is compared with other popular local feature based methods. The experimental results indicate the efficacy of the proposed method over the other existing methods.[...] Read more.
DOI: https://doi.org/10.5815/ijigsp.2012.09.05, Pub. Date: 8 Sep. 2012
Digital images make up a large component in the multimedia information. Hence Image authentication has attained a great importance and lead to the development of several image authentication algorithms. This paper proposes a block based watermarking scheme for image authentication based on the edge information extracted from each block. A signature is calculated from each edge block of the image using simple hash function and inserted in the same block. The proposed local edge based content hash (LECH) scheme extracts the original image without any distortion from the marked image after the hidden data have been extracted. It can also detect and localize tampered areas of the watermarked image. Experimental results demonstrate the validity of the proposed scheme.[...] Read more.
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