M.Tech, BE

Bagyammal T. currently serves as Assistant Professor at the department of Computer Science and Engineering, School of Engineering, Coimbatore Campus. Her areas of research includes Image Processing, Image Retrieval and Video Processing.

Awards and Recognitions

  1. BRONZE partner faculty under Inspire- The Campus Connect Faculty Partnership Model - Faculty award by Infosys Technologies, Mysore @ 2018.


Publication Type: Book Chapter

Year of Publication Title


C. Mishra, Bagyammal T., and Parameswaran, L., “An Algorithm Design for Anomaly Detection in Thermal Images”, in Innovations in Electrical and Electronic Engineering, M. N. Favorskaya, Mekhilef, S., Pandey, R. Kumar, and Singh, N., Eds. Singapore: Springer Singapore, 2021.[Abstract]

Anomaly detection is useful in diverse domains including fault detection system, health monitoring, intrusion detection, fraud detection, emotion recognition, cancer detection, animal rescue, detecting ecosystem disturbances, and event detection in sensor networks. Thermal image is a widely used night vision technology. Anomaly detection using thermal image features has been proposed in this work. Three major classes of features, namely textural features, color features, and shape features, have been extracted. Correlation model has been used for detecting anomalies. Thermal image of perishable objects has been analyzed, and the evaluation result confirms the hypothesis. It is found that using a set of features while using correlation as similarity measure the achieved average recall is 76.06%.

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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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Publication Type: Journal Article

Year of Publication Title


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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M. K. Vineeth, Arafath, A. B. Yazir, Dhanya M. Dhanalakshmy, and Bagyammal T., “Blind watermarking for the images captured by android mobiles”, International Journal of Applied Engineering Research, vol. 10, no. 55, pp. 2733-2736, 2015.[Abstract]

In this paper we present a securable image watermarking process for the user authentication as an android application. This system is built to protect personal and official images of a person that are captured using his/her own phone. The user can choose the types of watermark that he/she wants. The proposed system requires less battery consumption and processing time. © 2015, Research India Publications.

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