Qualification: 
M.Tech, BE
d_radha@blr.amrita.edu

Radha D. currently serves as Assistant Professor at Department of Computer Science, Amrita School of Engineering, Bengaluru. She is currently pursuing Ph.D in the area of High Performance computing in Image Processing.

Education

Year of Passing Degree Name of the Board/University
2007 M. Tech. in CSE Dr. MGR University
1997 B. E. in CSE Madurai Kamaraj Univeristy

Publications

Publication Type: Conference Paper

Year of Publication Publication Type Title

2017

Conference Paper

J. Amudha and Radha D., “Optimization of Rules in Neuro-Fuzzy Inference Systems”, in International Conference on Computational Vision and Bio-inspired Computing (ICCVBIC 2017), Inventive Research Organization and RVS Technical Campus, Coimbatore, 2017.

2017

Conference Paper

Radha D. and Kavikuil, K., “Centrality Measures to Analyze Transport Network for Congestion Free Shipment ”, in 2nd International Conference on Computational Systems and Information Technology for Sustainable Solution (CSITSS-2017), R.V. College of Engineering, Bengaluru, 2017.

2017

Conference Paper

Radha D. and Kulkarni, S., “A Social Network Analysis of World Cities Network ”, in the 2nd International Conference on Computational Systems and Information Technology for Sustainable Solution (CSITSS-2017), R.V. College of Engineering, Bengaluru, 2017.

2017

Conference Paper

Radha D. and Nithia, K. P. T., “A Case Study on Social Network Analysis: Thesaurus Book ”, in 2nd International Conference on Computational Systems and Information Technology for Sustainable Solution (CSITSS-2017), R.V. College of Engineering, Bengaluru, 2017.

2017

Conference Paper

, Radha D., and Amudha, J., “Effectual Training For Object Detection Using Eye Tracking Data Set”, in 2nd International conference on Inventive Computation Technologies(ICICT-2017), Coimbatore, 2017.

Publication Type: Journal Article

Year of Publication Publication Type Title

2015

Journal Article

A. J., Radha D., and S., S., “Analysis of fuzzy rule optimization models”, International Journal of Engineering and Technology, vol. 7, pp. 1564-1570, 2015.[Abstract]


Optimization without losing the accuracy and interpretability of rules is a major concern in rule based system. Fuzzy Inference system characterized by uncertainty tolerance is the best way to represent a knowledge based system. Optimization of rule based systems starts by incorporating selflearning ability to a fuzzy inference system. This can be achieved by neural networks, there by developing a neuro fuzzy inference system. This paper analyses different neuro fuzzy inference systems.The analysis has been performed in different types of datasets in terms of dimensionality and noises. Analysis results concludes that the neuro fuzzy model DENFIS (Dynamically Evolving Neuro Fuzzy Inference System) shows an improved performance when handling with high dimensional data. Simulation results on low dimensional data exhibits similar performance in ANFIS (Adaptive Neuro Fuzzy Inference System) and Denfis.

More »»

2014

Journal Article

Radha D., Amudha, J., and Jyotsna C, “Study of Measuring Dissimilarity between Nodes to Optimize the Saliency Map”, Int.J.Computer Technology & Applications, vol. 5, no. 3, pp. 993-1000, 2014.[Abstract]


An analytical conclusion based on eye tracking data sets has shown that Graph Based Visual Saliency (GBVS) measures saliency in a better way. GBVS promotes higher saliency at the center of the image plane and strongly highlights salient regions even for the locations that are far-away from object borders. It predicts human fixations more consistently than the standard algorithms. Every pixel in an image is mapped as an individual graph node in the activation map. This in turn increases the computational time. Hence the objective of this paper is to analyze the performance of saliency measure in GBVS by modeling different grouping strategies to represent a node. Here, we concentrate on finding the dissimilarity between the nodes by grouping pixels as a node with overlapping or non-overlapping pixels in the surrounding nodes which optimize the saliency closer to the Eye-Tracker’s saliency. The different grouping strategies of GBVS are analyzed across several performance measures like Normalized Scanpath Saliency the Linear Correlation Coefficient, Area Under Curve, , Similarity, Kullback – Leibler Divergence to prove its efficiency. Key terms – Visual Attention Models, Saliency maps, Eye-Tracking, Grouping pixels.

More »»

2013

Journal Article

Radha D. and Amudha, J., “Detection of Unauthor- ized Human Entity in Surveillance Video”, International Journal of Engineering and Technology (IJET), vol. 5, no. 3, pp. 3101-3108, 2013.[Abstract]


With the ever growing need for video surveillance in various fields, it has become very important to automate the entire process in order to save time, cost and achieve accuracy. In this paper we propose a novel and rapid approach to detect unauthorized human entity for the video surveillance system. The approach is based on bottom-up visual attention model using extended Itti Koch saliency model. Our approach includes three modules- Key frame extraction module, Visual attention model module, Human detection module. This approach permits detection and separation of the unauthorized human entity with higher accuracy than the existing Itti Koch saliency model.
Keywords—Video surveillance, Histogram, Key frame extraction, Visual Attention Model, Saliency map, Connected component, Aspect ratio.

More »»
207
PROGRAMS
OFFERED
5
AMRITA
CAMPUSES
15
CONSTITUENT
SCHOOLS
A
GRADE BY
NAAC, MHRD
8th
RANK(INDIA):
NIRF 2018
150+
INTERNATIONAL
PARTNERS
  • Amrita on Social Media

  • Contact us

    Amrita Vishwa Vidyapeetham
    Amritanagar, Coimbatore - 641 112
    Tamilnadu, India
    • Fax: +91-422-2686274
    • Coimbatore : +91 (422) 2685000
    • Amritapuri   : +91 (476) 280 1280
    • Bengaluru    : +91 (080) 251 83700
    • Kochi              : +91 (484) 280 1234
    • Mysuru          : +91 (821) 234 3479
    • Contact Details »