Qualification: 
Ph.D, M.Tech, B-Tech
Email: 
jyothishaj@am.amrita.edu

Dr. Jyothisha J. Nair currently serves as Associate Professor and Vice Chairperson at the Department of Computer Science and Engineering at Amrita School of Engineering, Amritapuri.

She has completed her Ph. D. from National Institute of Technology (NIT), Calicut.

Publications

Publication Type: Conference Proceedings

Year of Conference Publication Type Title

2016

Conference Proceedings

R. G. Gayathri, Jyothisha J. Nair, and Kaimal, M. R., “Extending Full Transitive Closure to Rank Removable Edges in GN Algorithm ”, Proceedings of the 6th International Conference on Advances in Computing and Communications, vol. 93. pp. 995–1002, 2016.[Abstract]


Most of the real-world networks exhibit community structure, a property that reveals the existence of natural vertex clusters whose inter-edge density is lower than intra-edge density between various groups. Despite providing a better understanding of network structure and characteristics, community detection has many practical applications in diverse domains. Communities obtained from the telephone network provides many useful information that can be used for churn prediction, budget control in organizations etc. Detecting communities is a fundamental need in the area of networks, yet challenging. In this paper, we propose an extension to the Girvan-Newman algorithm for finding the betweenness using the transitive closure property and the greedy technique in Dijkstra's single source shortest path method. More »»

2014

Conference Proceedings

Jyothisha J. Nair and Bhadran, B., “Denoising of SAR Images Using Maximum Likelihood Estimation”, proceedings of the IEEE International Conference on Communication and Signal processing, ICCSP 2014. 2014.[Abstract]


Image denoising is an important problem in image processing because noise may interfere with visual interpretation. This may create problems in certain applications like classification problem, pattern matching, etc. This paper presents a new approach for image denoising in the case of speckle noise models. The proposed method is a modification of Non Local Means filter method using Maximum Likelihood Estimation. The Non Local Means algorithm performs a weighted average of the similar pixels. Here we introduce a method that performs weighted average on restricted local neighborhoods. More over the method performs weight calculation using Geman-McClure estimation function rather than the exponential function because of the fact that Geman-McClure estimator is better in preserving edge details than the exponential function. Experiments at various noise levels based on PSNR values and SSIM values show that the proposed method outperforms the existing methods and thereby increasing the accuracy of further processing for synthetic aperture radar (SAR) images.

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2011

Conference Proceedings

V. K. Govindan and Jyothisha J. Nair, “Automatic Segmentation of MR Brain Images”, In Proceedings of the ICCCS International Conference on Communication, Computing and Security (ACM), NIT Rourkela. 2011.

Publication Type: Conference Paper

Year of Conference Publication Type Title

2016

Conference Paper

B. Bipin and Jyothisha J. Nair, “Image convolution optimization using sparse matrix vector multiplication technique”, in 2016 International Conference on Advances in Computing, Communications and Informatics (ICACCI), 2016.[Abstract]


Image convolution is an integral operator in the field of digital image processing. For any operation to be processed in images say whether it is edge detection, image smoothing, image blurring, etc. process of convolution comes into picture. Generally in image processing the convolution is done by using a mask known as the kernel. As the values of the kernel is changed the operation on image also changes. For each operation, the kernel will be different. In the conventional way of image convolution, the number of multiplications are very high. Thereby the time complexity is also high. In this paper, a new and efficient method is proposed to do convolution on the images with lesser time complexity. We exploit the sub matrix structure of the kernel matrix and systematically assign the values to a new H matrix. Since the produced H matrix is a spare matrix, the output is realized here by using Sparse Matrix Vector Multiplication technique. Compressed Row Storage format (CSR) is the format that is used here for the Sparse Matrix Vector Multiplication (SMVM) technique. Using the CSR format with Sparse Matrix Vector Multiplication technique, convolution processes achieves 3.4 times and 2.4 times faster than conventional methods for image smoothing and edge detection operations respectively. More »»

2016

Conference Paper

H. N and Jyothisha J. Nair, “Interactive Learning System for the Hearing Impaired and the Vocally Challenged”, in International Conference on Advances in Computing, Communications and Informatics (ICACCI-2016), 2016.[Abstract]


In our existing education system, teachers primarily engage students verbally in what we call ‘chalk and talk’ approach. Occasionally, certain learning models are also made use of for the purpose of teaching specific concepts. Smart classroom systems employ PowerPoint presentations, videos and the like. However, lack of sufficient self-interactive models and/or inadequate interaction with them, cause students lose focus. Young children, particularly with disabilities such as those with hearing impairment and vocal dysfunction are prone to it. Our studies showed that students experienced enhanced attentiveness in an environment conducive to self-interactive learning. The word interaction here does not refer to just teacher-student communication rather; it places greater emphasis on interactive self-learning. The student is utmost comfortable when he/she feels to be the center of attention or the teaching is exclusive to him/her. We propose a novel learning system in order to kindle the innate curiosity of students. This article presents an application of the ongoing research on interactive learning. Our system employs both Virtual Reality (VR) and Augmented Reality (AR) to bring about a deeper immersive and effective interactive learning experience to the students. This Interactive VR-AR Learning System (IVRARLS) provides a learning environment with each student being able to independently interact to learn with his or her own virtual learning models in real time. In our scheme, Microsoft Kinect is used for the extraction of interactive gestures of the participant(s). This approach is better suited particularly for the hearing impaired and/or vocally challenged children nevertheless it does not exclusively target them. More »»

2016

Conference Paper

S. Thomas and Jyothisha J. Nair, “A Survey on extracting Frequent Subgraphs”, in International Conference on Advances in Computing, Communications and Informatics (ICACCI-2016), 2016.[Abstract]


Mining on graphs has become quiet popular because of the increasing use of graphs in real world applications. Considering the importance of graph applications, the problem of finding frequent itemsets on transactional databases can be transformed to the mining of frequent subgraphs present in a single or set of graphs. The objective of frequent subgraph mining is to extract interesting and meaningful subgraphs which have occurred frequently. The research goals in the discovery of frequent subgraphs are (i) mechanisms that can effectively generate candidate subgraphs excluding duplicates and (ii) mechanisms that find best processing techniques that generate only necessary candidate subgraphs in order to discover the useful and desired frequent subgraphs. In this paper, our prime focus is to give an overview about the state of the art methods in the area of frequent subgraph mining. More »»

2015

Conference Paper

B. Bhadran and Jyothisha J. Nair, “Classification of patterns on high resolution SAR images”, in 2015 International Conference on Computing and Network Communications, CoCoNet 2015, 2015, pp. 784-792.[Abstract]


Synthetic Aperture Radar being an all weather adaptive and deeply penetrating, forms an inevitable part of all processes of investigation. Classifying different patterns like rivers, buildings, land areas, farm land etc has got prominent role in remote sensing applications, military applications etc and hence has been actively researched in recent years. This paper presents a novel approach for classifying high resolution SAR images. Image denoising is the first step in certain applications like classification problem, pattern matching etc. Here a modified Non Local Means filter method is used for denoising and also explores the possibility of using Artificial Neural Networks (ANN) for classifying different patterns on high resolution SAR images based on a fusion method. The proposed method uses the features of Local Binary Patterns (LBP), features in RGB color space and features in HSV color space. The experiments on high resolution SAR images obtained from Quickbird and Ikonos satellites shows that the proposed method outperforms the other widely used feature extracting methods in SAR image classification. © 2015 IEEE.

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2015

Conference Paper

D. Francis and Jyothisha J. Nair, “Robust Non-Local Total Variation Image Inpainting”, in International Conference on Computational Intelligence and Communication Networks, 2015.[Abstract]


Image inpainting is the process of removing selected object and restoring dead pixel from an image based on the background information. Various method have been proposed to tackle the inpainting problem where they need related information from other images and use only neighboring data to recover the lost part of image. To overcome this, an efficient inpainting technique called Robust Non-Local Total variation Method (RNLTV) is used. For filling lost portion, the proposed method uses information from the image itself and also superiority of the local and non-local methods are put together here. The local method which is efficient for recovering image edges and the textured region is recovered using nonlocal method. A Bregman operator splitting algorithm is employed here to avoid the loss of signal in each iteration of the total variation. The efficiency of the Robust Non-Local Total Variation method was tested and compared with existing methods and found superior. More »»

2015

Conference Paper

N. Gopinath, k, A., J Shankar, A., and Jyothisha J. Nair, “Complex Diffusion Based Image Inpainting”, in 1st International Conference on Next Generation Computing Technologies (NGCT-2015) , 2015.[Abstract]


Image inpainting is the meaningful reconstruction of lost, damaged or unwanted portions of an image by using the information from the proper undamaged portions of the same image. Image inpainting is an important process in image processing and has numerous applications in heritage conservation, restoration of old photographs, removal of occlusions, special effects in photos and so on. Here we replace the unwanted object by the information available from its neighbourhood. We present an algorithm that improves both the clarity and speed using a Complex-diffusion based approach. Complex-diffusion based approach for inpainting overcomes the shortcomings such as staircase effect and excessive blurring caused by Partial Differential Equation based approaches. Our method outperforms the existing methods in terms of PSNR, SSIM and UIQI values. keywords: Image inpainting, Complex Diffusion, Exemplar Based inpainting, Partial Differential Equation Based inpainting More »»

2014

Conference Paper

U. Ganesha, D., A. J., S., L. S., S., H., R., R., Jyothisha J. Nair, and N., S. K., “A Cost Effective Form Tester for Evaluating Cam and Circularity Error Profiles”, in International Colloquium on Materials,Manufacturing and Metrology-ICMMM2014, IIT Madras,Chennai, 2014.

2014

Conference Paper

Jyothisha J. Nair and Mohan, N., “A robust non local means maximum likelihood estimation method for Rician noise reduction in MR images”, in Communications and Signal Processing (ICCSP), 2014 International Conference on, 2014.[Abstract]


Denoising is one of the most important preprocessing task in medical image analysis. It has a great role in the clinical diagnosis and computerized analysis. When SNR is low, medical images follows a Rician noise distribution which is signal dependent. In the literature, only few works focus on the edge preserving quality of MR images. Our aim is to estimate the noise free signal from MR magnitude images by focusing on preserving edges and tissue boundaries. The proposed method is an improvisation over non local means maximum likelihood approach for Rician noise reduction in MR images. Our method focus on a robust estimator function (Geman-McClure function) for weight calculation, and is compared with the existing methods in terms of PSNR ratio, visual quality comparison and by SSIM values. The proposed method outperforms the state-of-the art methods in preserving fine structural details and edge boundaries.

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Publication Type: Journal Article

Year of Conference Publication Type Title

2016

Journal Article

Jyothisha J. Nair and Thomas, S., “Improvised Apriori with Frequent Subgraph Tree for Extracting Frequent Subgraphs”, Journal of Intelligent and Fuzzy Systems, IOS Press ( Accepted), 2016.

2015

Journal Article

R. G. Gayathri and Jyothisha J. Nair, “Towards efficient analysis of massive networks”, International Journal of Applied Engineering Research, vol. 10, pp. 222-227, 2015.[Abstract]


Graph algorithms are very useful in solving many problems in all major domains like social networking, stock market analysis etc. Increasing demands of such kind of problems grow in scale and reveal the need of parallel computing resources to meet the computational and memory needs. Graph processing systems have to deal with the three Vs of big data - variety, velocity and volume. Loading the entire graph into the memory of a single machine seems to be impossible. In such cases, parallel processing is a solution to tackle the resource limitations posed by single processors. In this paper, we present the requirement for graph partitioning and the issues in designing partitioning technique for real world graphs. The paper also talks about the temporal metrics that provide a more effective analysis of real-world networks compared to their static counterparts. Finally, we aim at the reachability queries which are indispensable in networks and their usage in the dynamic graphs which evolve over time. We focus on the current challenges in this area and feature some future research recommendations. © Research India Publications.

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2015

Journal Article

S. U. Priya and Jyothisha J. Nair, “Denoising of DT-MR Images with an Iterative PCA”, Procedia Computer Science, vol. 58, pp. 603 - 613, 2015.[Abstract]


Nowadays most of the clinical applications uses Magnetic Resonance Images(MRI) for diagnosing neurological abnormalities. During MR image acquisition the emitted energy is converted to image by using some mathematical models, and this may cause addition of noise. Therefore we need to denoise the image. Currently most of the clinical application uses Diffusion Tensor-MR Images for tracking neural fibres by extracting features from the images. Noise in DT-MR Images make fibre tracking and disease diagnosing tougher. So our work aims to denoise the Diffusion Tensor MR images with better visual quality. In this paper, we propose a denoising technique that uses Structural Similarity Index Matrix (SSIM) for grouping similar patches and performs Iterative Principal Component Analysis on each group. By performing the weighted average on Principal Component, we have obtained the denoised DT-MR Image. For getting better visual quality of the denoised images we employ Iterative Principal component Analysis technique. More »»

2014

Journal Article

Jyothisha J. Nair and Govindan, V. Kb, “Intensity inhomogeneity correction using modified homomorphic unsharp masking”, Journal of Medical Imaging and Health Informatics, vol. 4, pp. 285-290, 2014.[Abstract]


Intensity inhomogeneity refers to the smooth intensity change inside the originally homogeneous regions. Filter-based inhomogeneity correction methods have been commonly used in literature. In this paper, a new filter-based inhomogeneity correction method is proposed based on fourth order complex diffusion. The effectiveness of the proposed method is compared with other filter-based Inhomogeneity correction methods. The results demonstrate that the proposed method outperforms the other methods in terms of coefficient of variation and coefficient of joint variation. Copyright © 2014 American Scientific Publishers All rights reserved.

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2013

Journal Article

Jyothisha J. Nair and Govindan, V. K., “Multi-Scale Segmentation Based on Nonlinear Complex Diffusion”, Journal of Medical Imaging and Health Informatics, vol. 3, pp. 242–245, 2013.[Abstract]


Intensity based and edge based segmentation methods have their own limitations. Multiscale representation employing Gradient dependent diffusion and wavelet decomposition, based on edge and intensity information, has been proposed in the literature to get rid of these limitations to some extent. In this work, a fully automatic segmentation method based on complex diffusion is proposed. A multiscale representation of the image is formed based on the nonlinear complex diffusion technique. Intensity based linking model is used to group pixels into a number of segments. This approach is employed successfully to segment MR Brain images into White Matter (WM), Gray Matter (GM), and Cerebral Spinal Fluid (CSF) with no user interaction. Better segmentation performances were observed when compared to Gradient dependent diffusion and a'trous based methods proposed in the literature. More »»

2012

Journal Article

Jyothisha J. Nair and Govindan, V. K., “Automatic Segmentation Employing Fuzzy Connectedness”, International Review on Computers and Software, vol. 7, 2012.

Publication Type: Book Chapter

Year of Conference Publication Type Title

2012

Book Chapter

Jyothisha J. Nair and Govindan, V. K., “Speckle Noise Reduction Using Fourth Order Complex Diffusion Based Homomorphic Filter”, in Advances in Computing and Information Technology, Springer, 2012, pp. 895–903.[Abstract]


Filtering out speckle noise is essential in many imaging applications. Speckle noise creates a grainy appearance that leads to the masking of diagnostically significant image features and consequent reduction in the accuracy of segmentation and pattern recognition algorithms. For low contrast images, speckle noise is multiplicative in nature. The approach suggested in this paper makes use of fourth order complex diffusion technique to perform homomorphic filtering for speckle noise reduction. Both quantitative and qualitative evaluation is carried out for different noise variances and found that the proposed approach out performs the existing methods in terms of root means square error (RMSE) value and peak signal to noise ratio (PSNR). More »»

Faculty Research Interest: 
207
PROGRAMS
OFFERED
5
AMRITA
CAMPUSES
15
CONSTITUENT
SCHOOLS
A
GRADE BY
NAAC, MHRD
8th
RANK(INDIA):
NIRF 2018
150+
INTERNATIONAL
PARTNERS