K. Chandra Sekharaiah

Work place: JNTU SIT, Hyderabad, 500 085, India

E-mail: chandrasekharaiahk@gmail.com


Research Interests: Software Development Process, Software Engineering, Image Compression, Image Manipulation, Image Processing


Dr. KChandrasekharaiah received he B.Tech degree from JNTU Hyderabad, M.Tech degree from JNTU Hyderabad and he got his Ph.D in Computer Science from IIT Madras. He received PostDoc in Software Engineering from University of Finland. He published more than 70 research papers in various National and Inter National Conferences and Journals. His research interest includes image processing, Software Engineering. He has 18 years of experience in teaching.

Author Articles
Texture Analysis Based on Micro Primitive Descriptor (MPD)

By Rasigiri Venkata lakshmi E. Srinivasa Reddy K. Chandra Sekharaiah

DOI: https://doi.org/10.5815/ijmecs.2015.02.05, Pub. Date: 8 Feb. 2015

Texture classification is an important application in all the fields of image processing and computer vision. This paper proposes a simple and powerful feature set for texture classification, namely micro primitive descriptor (MPD). The MPD is derived from the 2×2 grid of a motif transformed image. The original image is divided into 2×2 pixel grids. Each 2×2 grid is replaced by a motif shape that minimizes the local ascent while traversing the 2×2 grid forming a motif transformed image. The proposed feature set extracts textural information of an image with a more detailed respect of texture characteristics. The results demonstrate that it is much more efficient and effective than representative feature descriptors, such as Random Threshold Vector Technique (RTV) features and Wavelet Transforms Based on Gaussian Markov Random Field (WTBGMF) approach for texture classification.

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