M.Tech, B-Tech

Jyotsna C serves as Assistant Professor(Sr.Grade)  at department of Computer Science,Amrita School of Engineering. 


Publication Type: Conference Paper

Year of Publication Title


Sowmyasri, .Ravalika, R., Jyotsna C, and J, A., “An Online Platform for Diagnosis of Children with Reading Disability”, in 3rd International Conference on Computational Vision and Bio Inspired Computing, (ICCVBIC 2019), RVS Technical Campus,Coimbatore, 2019.


T. Shravani, RamyaSai,, M. Shree, V., J, A., and Jyotsna C, “Assistive Communication Application for Amyotrophic Lateral Sclerosis Patients”, in 3rd International Conference on Computational Vision and Bio Inspired Computing, (ICCVBIC 2019),, RVS Technical Campus,Coimbatore, 2019.


Y. Navya, SriDevi, S., Akhila, P., J., A., and Jyotsna C, “Third Eye : Assistance for Reading Disability”, in International Conference on Soft Computing and Signal Processing, Hyderabad, 2019.


Jyotsna C, SaiMounica, M., Manvita, M., and Amudha, J., “Low Cost Eye Gaze Tracker Using Web Camera”, in 3rd International Conference on Computing Methodologies and Communication [ICCMC 2019], Surya Engineering College, Erode , 2019.


J. Amudha and Jyotsna C, “Eye Tracking Enabled User Interface for Amyotrophic Lateral Sclerosis Patients”, in Grace Hopper Celebration India 2017 ,(GHCI-2017), 2017.

Publication Type: Journal Article

Year of Publication Title


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.

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