Qualification: 
Ph.D
b_rajathilagam@cb.amrita.edu

Dr. B. Rajathilagam is an Associate Professor, Department of Computer Science and Engineering, Amrita Vishwa Vidyapeetham, Coimbatore campus. She completed her Ph.D in 2013 under Centre for Excellence in Computational Engineering, Amrita Vishwa Vidyapeetham, Coimbatore campus. She has been at Amrita School of Engineering since 2000. Her research interests include Signal Processing, representation of signals, filter design, feature operators, pattern recognition, medical signal processing, thermal imaging and computer vision. She has been a research scholar at University of California, Riverside, USA under Prof. Sathish Tripathi  and  Prof. Michalis in 2004. She has also visited Birmingham University, UK in 2007. She completed a funded project for ISRO in 2009 and worked as a full time research scholar in Centre for Excellence in Computational Engineering during 2007-2009. She received the ‘Senior Woman Educator and Scholar’ award from the organization ‘National Foundation for Entrepreneurship Development (NFED), 2014, on the occasion of Women’s day. 

Currently she is working on applications of signal processing and is a reviewer for Pattern Recognition Elsevier Journal. She has been part of organising international conferences including ADCOM 2006, ICONNIAC 2014 and ACM Women in Computing conducted by Department of Computer Science and Engineering. She has conducted two national level workshops MADCA 2k14 and MAGS3D 2k14 in 2014 in collaboration with industry. She is working on parallelizing algorithms that develop filters, feature descriptors, segmentation and detection of objects and human and compression for Multimedia signals. The feature descriptors are being tested for Deep learning in identifying human aging.  She has also attended the Professors Meet of International Conference on High Performance Computing (HiPC), 2016 as part of a collaboration with NVIDIA.

Publications

Publication Type: Conference Paper

Year of Publication Publication Type Title

2017

Conference Paper

Dr. Rajathilagam B. and Rangarajan, M., “Spectral representation of principal components in signals and images using G-lets decomposition of subbands”, in IEEE Region 10 Annual International Conference, Proceedings/TENCON, 2017, pp. 3809-3812.[Abstract]


This paper presents a spectral subband decomposition using G-lets in time-domain for 1-D and 2-D signals. The decomposition is achieved through successive filtering and decimation steps ending up in a decomposition tree. At each node of the tree, the parameters of the corresponding subband signal are estimated using high gradients obtained at the first node. The resulting subbands are found to highlight the components of the signal. The proposed method using G-lets enables one to reduce the processing time and makes the choice of decomposition levels easier, comparatively to the case where the whole signal is processed at once. The advantage of G-lets based subbands is demonstrated using 1-D and 2-D signals. It is seen that a synthetic signal generated from a sine and cosine signal is separated into exactly the same two signals and the performance is good for monocomponent and multicomponent signals. © 2016 IEEE. More »»

Publication Type: Journal Article

Year of Publication Publication Type Title

2017

Journal Article

Dr. Rajathilagam B. and Rangarajan, M., “Edge detection using G-lets based on matrix factorization by group representations”, Pattern Recognition, vol. 67, pp. 1-15, 2017.[Abstract]


A new edge detection technique using transformation groups based G-lets filters is proposed in this paper. Discretizing gradients seem to produce discontinuity in classic edge detectors. No particular filter is capable of identifying meaningful edges at all scales and it increases computations with a multiscale approach. It is a challenge to get localized edges without spurious ones due to noise and integrate the obtained edges into meaningful object boundaries. Without breaking edge continuity and strictly localizing edges requires that filters do not blur the image during preprocessing. G-lets filters are found to be capable of performing well in most type of images including natural, noisy, low resolution and synthetic. In this paper, an edge detection algorithm using G-lets filters which are built by direct factorization of linear transformation matrices using irreducible representations is proposed. A multiresolution approach is shown to enhance the possibility of detecting faint edges. An edge tracing algorithm is presented to produce the edge image. The computational cost involved is comparatively lesser than existing filters. It is found that the geometries in the original image are preserved in the edge image. The edge tracing algorithm is capable of constructing object boundaries without the inner textures in a way that is not completely dependent on intensity thresholding. G-lets filters and the edge operator is found to be a promising algorithm for drastically bringing down the computations needed for realtime applications. The results are compared with BSDS500 boundary detection dataset using pb and global pb detectors. © 2017 Elsevier Ltd.

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2017

Journal Article

Dr. Rajathilagam B. and Dr. Murali Rangarajan, “Reducing the cold-user and cold-item problem in recommender system by reducing the Spectral representation of principal components in signals and images using G-lets decomposition of sub bands”, IEEE Region 10 Annual International Conference, Proceedings TENCON, pp. 3809 -3812., 2017.

2015

Journal Article

R. Shwetha and Dr. Rajathilagam B., “Super resolution of mammograms for breast cancer detection”, International Journal of Applied Engineering Research, vol. 10, pp. 21453-21465, 2015.[Abstract]


Mammography has been the most popular method for the early detection of the breast cancer. Due to low contrast of mammograms typical diagnostic signs such as masses and micro calcification are difficult to detect. So to create a high resolution mammogram super resolution (SR) technique can be used. This technique will make a high resolution image from a series of low resolution images of the same scene. A novel algorithm with interpolation for super resolution reconstruction has been proposed here. It has taken a interpolation technique that preserves edges without introducing any artifact. This also avoids pixilation, over smoothing and blurring of images. In our method we have used denoising, deblurring and registration technique to improve the quality of low resolution images and fused them to produce a higher resolution image. The proposed algorithm is a hybrid of bilinear interpolation and FCBI method with edge detecting criteria. © Research India Publications.

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2015

Journal Article

S. C, Sandeep, R., and Dr. Rajathilagam B., “Comparison of Segmentation Algorithms”, IJAER, 2015.

2012

Journal Article

Dr. Rajathilagam B., Rangarajan, M., and Soman, K. P., “Frequency analysis of signals and images using G-Lets”, International Journal of Imaging and Robotics, vol. 8, pp. 30-48, 2012.[Abstract]


This paper presents a method of frequency analysis for discrete signals using G-lets. From a group of transformations and representation theory, a finite basis of the signal space is obtained. The projections of the signal onto this basis are called G-lets. G-lets, due to the nature of transformations used, contain oscillations in such a manner that the difference between consecutive G-let coefficients is proportional to the local frequency. The signal frequency, in turn, is proportional to the difference in amplitude of the signal at any point. A dilation operation is defined to capture the frequencies without use of a windowing function, by highlighting the highest frequency of the existing signal in a G-let. Considering features of a signal as a combination of frequencies, feature extraction of 1-D signals and images are examined. The beginning and end of each feature are identified by the spread of low frequencies in the neighborhood of a high frequency. Results are demonstrated using dihedral groups, for simple 1-D signals, an ECG signal, and 'Lena' image. A qualitative comparison is provided with wavelets and Fourier analysis. © 2012 by IJIR (CESER Publications).

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2012

Journal Article

Dr. Rajathilagam B., Dr. Murali Rangarajan, and Soman, K. P., “G-Lets: Signal Processing Using Transformation Groups”, vol. arXiv:1201.2995v1, 2012.

2012

Journal Article

Dr. Rajathilagam B., Dr. Murali Rangarajan, and P, S. K., “G-Lets: A New Signal Processing Algorithm”, International Journal of Computer Applications, vol. 37 , no. 6, pp. 1-7, 2012.[Abstract]


Different signal processing transforms provide us with unique decomposition capabilities. Instead of using specific transformation for every type of signal, we propose in this paper a novel way of signal processing using a group of transformations within the limits of Group theory. For different types of signal different transformation combinations can be chosen. It is found that it is possible to process a signal at multiresolution and extend it to perform edge detection, denoising, face recognition, etc by filtering the local features. For a finite signal there should be a natural existence of basis in it’s vector space. Without any approximation using Group theory it is seen that one can get close to this finite basis from different viewpoints. Dihedral groups have been demonstrated for this purpose.

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Publication Type: Conference Proceedings

Year of Publication Publication Type Title

2015

Conference Proceedings

Dr. Rajathilagam B., Murali, R., and Balaji, B., “Dynamic Context-specific User Profiles by Regression Modelling”, Proceedings of GSTF CGAT. 2015.

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