Work place: SRKR Engineering College, Bhimavaram, A.P., India
Research Interests: Image Compression, Image Manipulation, Computer Networks, Image Processing, Information Retrieval
Dr. G. P. Saradhi Varma did his B.E. (CSE) from Manipal Institute of Technology Mangalore University, M.Tech from NIT (REC Warangal), Warangal and Ph.D (Specialized in Computer Science) from Andhra University, Visakhapatnam. He is presently Professor and Principal, SRKR Engineering College, Bhimavaram. He is an Educational member and consultant to various companies and Institutions in Andhra Pradesh. He has a total of 24 research publications at International/National Journals and Conferences. is areas of interest include Object Oriented Technologies, Information Retrieval, Algorithms, Computer Networks, Image Processing.
DOI: https://doi.org/10.5815/ijitcs.2018.05.03, Pub. Date: 8 May 2018
Recently, artificial neural networks are fund to be efficiently used in clustering algorithms. So, the present paper focuses on the development of a novel clustering method based on artificial neural networks. The present paper uses an enhancement filter to enhance the segments in the input image. After this, the various sub images are generated and features are computed for each sub and edge image. Finally, the Self Organizing Map (SOM) is used for clustering process. The proposed novel method is evaluated with a database of 795 leaf images. Further various Probability Distributed Functions (PDFs) are used to evaluate the efficacy of the proposed method. The performance measures of the proposed method indicate the efficiency of the extended clustering method with SOM.[...] Read more.
DOI: https://doi.org/10.5815/ijigsp.2018.03.02, Pub. Date: 8 Mar. 2018
Currently clustering techniques play a vital role in object recognition process. The clustering techniques are found to be efficient with neural networks. So, the present paper proposed a novel method for clustering the input objects with Self-Organizing Map (SOM). The proposed method considers the input object as a random closed set. The random set can be efficiently described with various features viz., volume fractions, co-variance and contact distributions etc. In the proposed method, the input object is described efficiently with spherical contact distribution. The proposed method is experimented with the leaf data set with 795 images. The performance of the proposed method is evaluated with various topologies of SOM and is measured with four measures viz., FNR, FPR, TPR and TNR. The results indicate the efficiency of the proposed method.[...] Read more.
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