Work place: Jawaharlal Nehru Technological University Kakinada, Kakinada, A.P., India
Research Interests: Computer systems and computational processes, Computer Vision, Pattern Recognition, Computer Architecture and Organization
Chamundeswari G. completed her M.Tech from Narasaraopeta College of Engineering during 2007-2009. She registered for Ph.D in JNTUK, Kakinada in 2010. She worked as Assistant Professor in Vegesna Suryanarayana Raju (VSR) Institute of Computer Science, Eluru from 2001 to 2009. Then she worked as Assistant Professor in Helapuri Institute of Technology and Science, Eluru from 2009 to 2010. She worked as Associate Professor in Ramachandra College of Engineering, Eluru. She published six papers in various national and international journals and conferences. Her research interests include computer vision, pattern recognition and clustering techniques.
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.
Subscribe to receive issue release notifications and newsletters from MECS Press journals