Qualification: 
Ph.D, MCA
Email: 
p_latha@cb.amrita.edu

Dr. Latha Parameswaran served as a Professor in Department of computer science and Engineering till March 2021.She is currently serving as  Honorary Distinguished Professor with Department of Computer science and Engineering.

Born, brought up and settled as Coimbatorean, Dr. Latha Parameswaran joined the Department of Computer Science and Engineering at the Coimbatore campus of Amrita Vishwa Vidhyapeetham in 1999. She is currently the professor and Chairperson of the Department of Computer Science and Engineering. She was in the software industry for ten years prior to joining Amrita. She completed her Master's Degree from PSG College of Technology and her  Ph. D. from Bharathiar University. Her Ph.D. thesis titled “Digital Watermarking for Image Security” was highly commended by the experts. Her areas of research include Image Processing, Information Retrieval, Image Mining, Information Security and Theoretical Computer Science.

She has published papers in international journals and at conferences where she has won several Best Paper Awards.  She has also authored a chapter in a book published by the Idea Group.

In addition to teaching and serving as Chair of the CSE department, she is currently guiding  Ph. D. research scholars in Amrita, Bharathiar and Anna Universities. She is in the doctoral committee for many Ph. D. scholars in various Universities. She is also a member in Board of Studies at various premier academic institutions in India. She also holds positions in academic council in premier institutions. She is in the faculty recruitment board, faculty appraisal committee, funded project review committee, recommendations for infrastructure setting up committee at various institutions.

She also works on projects funded by DST. Her Special Interests include encouraging more and more women in to work in Engineering, Technology and Computing.  Also she conducts workshops for school children in the classes 8-12 to encourage them to pursue computer science as their field of study. In connection with this she has conducted two conferences on Women in Computing at Amrita Vishwa Vidyapeetham in 2010 and 2013. The next in this series is planned in 2016.

Publications

Publication Type: Journal Article

Year of Publication Title

2020

K. Gautam, Dr. Latha Parameswaran, and Dr. Senthil Kumar T., “Computer Vision Based Asset Surveillance for Smart Buildings”, Journal of Computational and Theoretical Nanoscience, vol. 17, no. 1, pp. 456 - 463, 2020.[Abstract]


Unraveling meaningful pattern form the video offers a solution to many real-world problems, especially surveillance and security. Detecting and tracking an object under the area of video surveillance, not only automates the security but also leverages smart nature of the buildings. The objective of the manuscript is to detect and track assets inside the building using vision system. In this manuscript, the strategies involved in asset detection and tracking are discussed with their pros and cons. In addition to it, a novel approach has been proposed that detects and tracks the object of interest across all the frames using correlation coefficient. The proposed approach is said to be significant since the user has an option to select the object of interest from any two frames in the video and correlation coefficient is calculated for the region of interest. Based on the arrived correlation coefficient the object of interest is tracked across the rest of the frames. Experimentation is carried out using the 10 videos acquired from IP camera inside the building.

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2018

Bagyammal T., Dr. Latha Parameswaran, and Vaiapury, K., “Visual-based Change Detection in Scene Regions using Statistical-based Approaches”, Journal of Electronic Imaging, vol. 27, pp. 1 – 11, 2018.[Abstract]


Detecting changes of the same scene taken at different time instances is crucial and demanding for medical, remote sensing, infrastructure, agriculture, and planogram compliance applications. We propose a statistical-based approach by exploiting the linear relationship. Initially, region of interest is identified using a graph-cut-based technique followed by geometrical alignment via area-based registration. To perform statistical correlation, we adopt features such as block-wise average coefficient value of the first level of the discrete wavelet transform (DWT-LL1) and the map obtained using hybrid saliency approaches. In the former approach, Pearson’s correlation measure is calculated for the DWT-LL1, and in the latter, PCC has been calculated using the saliency value. Change has been detected using optimal PCC value while minimizing the error rate. Experimental results on datasets reveal that saliency feature and DWT-LL1 perform better for normal and noise corrupted images, respectively. The efficiency of the proposed method is validated by user study with average mean opinion score of 70%. Hybrid saliency-based change detection gives 92.9% of correct classification and hence useful for the vision-based applications like damage detection in a car.

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2018

M. Padmashini, Manjusha R., and Dr. Latha Parameswaran, “Vision Based Algorithm for People Counting using Deep Learning”, International Journal of Engineering and Technology(UAE), vol. 7, pp. 74-80, 2018.[Abstract]


Estimating the number of people in a particular scene has always been an important topic of research in computer vision and digital image processing. People counting has wide applications in scenario ranging from analyzing the customer's choice and improving the quality of service in retail stores, supermarkets and shopping malls to managing human resources and optimizing the energy usage in office buildings. While there exists algorithms for counting people in a scene, some algorithm have set their benchmark in performance with respect to efficiency, flexibility and accuracy. In this paper, an attempt has been made to perform people counting using Deep Neural Networks (DNN) on comparison with existing image processing based algorithms like Histogram of Oriented Gradients with Support Vector Machine (HoG with SVM), Local Binary Pattern (LBP) based Adaboost classifier and contour based people detection. The proposed DNN based approach has higher accuracy at 90% and less false negatives. © 2018 Authors.

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2015

S. L. Nair, Manjusha R., and Dr. Latha Parameswaran, “A survey on context based image annotation techniques”, International Journal of Applied Engineering Research, vol. 10, pp. 29845-29856, 2015.[Abstract]


The importance of image acquisition and then analysing them for various purposes is increasing everyday.Image annotation and retrieval is a vital process for analysis of large data. Context based annotation systems labels the images based on the context of the scene and provides accurate results for automatic annotation compared to the earlier Content based systems and thus has become a very important research domain in image processing. Many approaches and representations are proposed and developed for context based image annotation.This paper provides anoverview of some of the important approaches and representations of objects and their relationship used widely for context based image annotation. © Research India Publications.

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Publication Type: Conference Proceedings

Year of Publication Title

2019

Manjusha R. and Dr. Latha Parameswaran, “Design of an image skeletonization based algorithm for overcrowd detection in smart building”, Lecture Notes in Computational Vision and Biomechanics, vol. 30. Springer Netherlands, pp. 615-629, 2019.[Abstract]


Crowd analysis has found its significance in varied applications from security purposes to commercial use. This proposed algorithm aims at contour extraction from skeleton of the foreground image for identifying and counting people and for providing crowd alert in the given scene. The proposed algorithm is also compared with other conventional algorithms like HoG with SVM classifier, Haar cascade and Morphological Operator. Experimental results show that the proposed method aids better crowd analysis than the other three algorithms on varied datasets with varied illumination and varied concentration of people. © Springer Nature Switzerland AG 2019.

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2018

S. L. Nair, Manjusha R., and Dr. Latha Parameswaran, “Kernel Based Approaches for Context Based Image Annotatıon”, Computational Vision and Bio Inspired Computing. Springer International Publishing, Cham, 2018.[Abstract]


The Exploration of contextual information is very important for any automatic image annotation system. In this work a method based on kernels and keyword propagation technique is proposed. Automatic annotation with a set of keywords for each image is carried out by learning the image semantics. The similarity between the images is calculated by Hellinger's kernel and Radial Bias Function kernel(RBF)kernel. The images are labelled with multiple keywords using contextual keyword propagation. The results of using the two kernels on the set of features extracted are analysed. The annotation results obtained were validated based on confusion matrix and were found to have a good accuracy. The main advantage of this method is that it can propagate multiple keywords and no definite structure for the annotation keywords has to be considered

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2017

Dr. Shyamala C. K., Dr. Shunmuga Velayutham C., and Dr. Latha Parameswaran, “Teaching computational thinking to entry-level undergraduate engineering students at Amrita”, IEEE Global Engineering Education Conference, EDUCON. IEEE Computer Society, pp. 1731-1734, 2017.[Abstract]


This paper is about various aspects of the Computational Thinking and Problem Solving course offered to entry-level undergraduate engineering students across 7 engineering disciplines at Amrita University, India. The various aspects include the motivations for offering the course, aims and objectives of the course, course design as well as the delivery and assessment of the course. The paper also shares the experience of conducting the course to a very large number of students and the lessons learnt during the process. © 2017 IEEE.

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2016

S. Athira, Manjusha R., and Dr. Latha Parameswaran, “Scene understanding in images”, Advances in Intelligent Systems and Computing, vol. 530. pp. 261-271, 2016.[Abstract]


Scene understanding targets on the automatic identification of thoughts, opinion, emotions, and sentiment of the scene with polarity. The sole aim of scene understanding is to build a system which infer and understand the image or a video just like how humans do. In the paper, we propose two algorithms- Eigenfaces and Bezier Curve based algorithms for scene understanding in images. The work focuses on a group of people and thus, targets to perceive the sentiment of the group. The proposed algorithm consist of three different phases. In the first phase, face detection is performed. In the second phase, sentiment of each person in the image is identified and are combined to identify the overall sentiment in the third phase. Experimental results show Bezier curve approach gives better performance than Eigenfaces approach in recognizing the sentiments in multiple faces. © Springer International Publishing AG 2016.

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2016

R. Karthika, Dr. Latha Parameswaran, B.K., P., and L.P., S., “Study of Gabor wavelet for face recognition invariant to pose and orientation”, Proceedings of the International Conference on Soft Computing Systems, Advances in Intelligent Systems and Computing, vol. 397. Springer Verlag, pp. 501-509, 2016.[Abstract]


Gabor filters have achieved enormous success in texture analysis, feature extraction, segmentation, iris and face recognition. Face recognition is one of the most popular biometric modalities which has wide range of applications in biometric authentication. The most useful property of a Gabor filter is that it can achieve multi-resolution and multi-orientation analysis of an image. This paper presents an algorithm using Gabor wavelets in capturing discriminatory content, obtained by convolving a face image with coefficients of Gabor filter with different orientations and scales. Support vector machine (SVM) has been used to construct a robust classifier. This method has been tested with publicly available ORL dataset. This algorithm has been tested, cross-validated and the detailed results are presented. It is inferred that the proposed method offers a recognition rate (94%) with tenfold cross-validation. © Springer India 2016.

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2010

T. Arathi, Dr. Latha Parameswaran, and Dr. Soman K. P., “A study of reconstruction algorithms in computerized tomographic images”, Proceedings of the 1st Amrita ACM-W Celebration of Women in Computing in India, A2CWiC'10. ACM New York, NY, USA , Coimbatore, 2010.[Abstract]


Computerized tomography is extensively used in the medical imaging field. It has made a revolutionary impact in diagnostic medicine, helping doctors to view the internal organs of the human body to a very high precision, at the same time ensuring complete safety to the patient. This paper is a study of two such reconstruction algorithms, most commonly used in computerized tomography. They are the filtered backprojection algorithm and the fanbeam projection algorithm respectively. A comparison between the performances of the two methods has been carried out using a set of quality metrics. The experimental results and the conclusions drawn are also included. © 2010 ACM.

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Publication Type: Book Chapter

Year of Publication Title

2018

M. Muthugnanambika, Bagyammal T., Dr. Latha Parameswaran, and Vaiapury, K., “An automated vision based change detection method for planogram compliance in retail stores”, in Lecture Notes in Computational Vision and Biomechanics, vol. 28, Springer Netherlands, 2018, pp. 399-411.[Abstract]


Planogram are visual representations of a store’s products and services designed to help retailers ensure that the right merchandise is consistently on display, and that inventory is controlled at a level that guarantees that the right number of products are on each and every shelf. The main objective of this work is to propose an algorithm using image processing and machine learning as its base to find and detect the changes in the arrangement of objects present in the retail stores. The proposed algorithm is capable of identifying void space, count objects of similar type and thus helps in tracking the changes. Blob detection superseded by classification using a discriminative machine learning approach with the extracted statistical features of the objects has been used in this proposed algorithm. Experimental results are quite promising and hence this algorithm can be used to detect any changes occurring in a scene. © 2018, Springer International Publishing AG.

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