Qualification: 
M.Tech
r_aarthi@cb.amrita.edu

Aarthi R. currently serves as Assistant Professor at the Department of Computer Science and Engineering, School of Engineering, Amrita Vishwa Vidyapeetham, Coimbatore Campus. She joined 2006 as faculty at Amrita Vishwa Vidyapeetham. She graduated her M.Tech. in Computer Vision and Image Processing from Amrita Vishwa Vidyapeetham, Coimbatore. She completed her B.Tech. in Information Technology from Bharathiar University, Coimbatore. She has more than 15 Scopus indexed papers. She has attended nearly 30 workshops across India. Her research interest are Image Processing, Computer Vision and visual Attention model. she Served as faculty in-charge of a student club called “Amrita Photography Club(APC)”, since its inception till 2015. She is delivering guest lectures in colleges at tamilnadu.her Student Project Team has Won ‘TCS best student project award’ for the academic year 2011-2012.

Publications

Publication Type: Journal Article

Year of Publication Title

2019

H. .S, Aarthi, R., and Prasad.V, H., “Hand Region Extraction by Saliency based Color Component Analysis”, International Journal of Recent Technology and Engineering, vol. 8, no. 1, pp. 227-230, 2019.

2019

D. Kumar Boddu, Buchipalli, S. Varun, and Aarthi, R., “Classification of T-shirts based on pattern”, International Journal of Recent Technology and Engineering (IJRTE), vol. 8, no. -1S4, 2019.[Abstract]


In online shopping the fabric is the one of the most demanded items. People tend to choose the item based on the categorization. As evident growth in day to day online market, the manual way of categorizing the object is a difficult task. Hence our idea is to classify the T-shirt based on the visual pattern present in it. People uses their visual cues to identifying the pattern in the t-shirt into categories like Striped, solid e.t.c. we have developed a methodology to extract the visual pattern for each categories and further used for automatic labeling .This study gives idea of classification of T-shirts based on pattern on it. For classification we use tree classification. We mainly classify into two types of T-shirts mainly Solid and Striped. For feature extraction of the solid and striped were done by combination of segmentation and statistical analysis on segments.

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2018

N. joshy and Aarthi, R., “Evaluation of Edge Detection Methods on Different Categories of Images”, International Journal of Engineering &Technology, vol. 7, pp. 69-73, 2018.

2018

H. .S and Aarthi, R., “A Survey of Deep Convolutional Neural Network Applications in Image Processing”, International journal of pure and applied mathematics , vol. 118, no. 7, pp. 185-189, 2018.

2018

L. Gopan and Aarthi, R., “A Vision based DCNN for Identify Bottle Object in Indoor Environment”, Lecture Notes in Computational Vision and Biomechanics, vol. 28, pp. 447-456, 2018.[Abstract]


Vision based detection and classification is an emerging area of research in the field of automation. Due to the demand in automation different fields artificial intelligent architectures plays vital role to address the issues. Conventional architectures used for dealing computer vision problems are heavily under control on user features. But the new deep learning techniques have provided a substitute of automatically learning problem related features. The classification problem can be designed based on feature learned from DCNN. The performance of the DCNN algorithm vary based on the training. In this paper the performance of Deep Convolutional Neural Network (DCNN) is analyzed in classifying categories of bottle object

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2016

R. Aarthi, Amudha, J., K., B., and Varrier, A., “Detection of Moving Objects in Surveillance Video by Integrating Bottom-up Approach with Knowledge Base”, Procedia Computer Science, vol. 78, pp. 160 - 164, 2016.[Abstract]


Abstract In the modern age, where every prominent and populous area of a city is continuously monitored, a lot of data in the form of video has to be analyzed. There is a need for an algorithm that helps in the demarcation of the abnormal activities, for ensuring better security. To decrease perceptual overload in \{CCTV\} monitoring, automation of focusing the attention on significant events happening in overpopulated public scenes is also necessary. The major challenge lies in differentiating detecting of salient motion and background motion. This paper discusses a saliency detection method that aims to discover and localize the moving regions for indoor and outdoor surveillance videos. This method does not require any prior knowledge of a scene and this has been verified with snippets of surveillance footages. More »»

2016

A. Sampath, Sivaramakrishnan, A., Narayan, K., Aarthi, R., and Panigrahi, B. K., “A study of household object recognition using SIFT-based bag-of-words dictionary and SVMs”, Advances in Intelligent Systems and Computing, vol. 397, pp. 573-580, 2016.[Abstract]


In the era of computational intelligence, computer vision-based techniques for robotic cognition have gained prominence. One of the important problems in computer vision is the recognition of objects in real-time environments. In this paper, we construct a SIFT-based SVM classifier and analyze its performance for real-time object recognition. Ten household objects from the CALTECH-101 dataset are chosen, and the optimal train-test ratio is identified by keeping other SVM parameters constant. The system achieves an overall accuracy of 85% by maintaining the ratio as 3:2. The difficulties faced in adapting such a classifier for real-time recognition are discussed. © Springer India 2016.

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2016

R. Aarthi, Anjana, K. P., and Amudha, J., “Sketch based Image Retrieval using Information Content of Orientation”, Indian Journal of Science and Technology, vol. 9, 2016.[Abstract]


Background/Objectives: This paper presents an image retrieval system using hand drawn sketches of images. Sketch is one of the convenient ways to represent the abstract shape of an object. The main objective is to perform retrieval of images using edge content by prioritizing the blocks based on information. Methods/Statistical Analysis: Entropy based Histogram of Gradients (HOG) method is proposed to prioritize the block. The method helps to pick the candidate blocks dynamically to compare with database images. Findings: The performance of the method has been evaluated using benchmark dataset of Sketch Based Image Retrieval (SBIR) with other methods like Indexable Oriented Chamfer Matching (IOCM), Context Aware Saliency (CAS-IOCM) and Histogram of Gradients (HOG). Comparing to these methods the number of relevant images retrieved is high for our approach.Application/Improvement: Knowledge based block selection method improves the performance of the existing method.

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2015

Niveditha, .Vimmadisetti, M., .Email, D., and Aarthi, R., “An Overview of Apache Mahout”, International journal of applied engineering research, vol. 10, no. 11, pp. 28749-28758, 2015.

Publication Type: Conference Proceedings

Year of Publication Title

2019

K. Hena, Amudha, J., and Aarthi, R., “A Dynamic Object Detection In Real-World Scenarios”, Proceedings of International Conference on Computational Intelligence and Data Engineering, vol. 28. Springer Singapore, Singapore, 2019.[Abstract]


The object recognition is one of the most challenging tasks in computer vision, especially in the case of real-time robotic object recognition scenes where it is difficult to predefine an object and its location.Hena, Kausar To address this challenge, we propose an object detection method that can be adaptive to learn objects independent of the environment, by enhancing the relevant features of the object and by suppressing the other irrelevant feature. The proposed method has been modeled to learn the association of features from the given training dataset.Amudha, J. Using dynamic evolution of neuro-fuzzy inference system (DENFIS) model has been used to generate number of rules from the cluster formed from the dataset. The validation of the model has been carried on various datasets created from the real-world scenario.Aarthi, R. The system is capable of locating the target regardless of scale, illumination variance, and background

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2018

R. Aarthi and subramaniam, A., “Segmentation of Tomoto plant leaf”, Advances in Intelligent Systems and Computing, vol. 518. pp. 149-156, 2018.

2013

Dr. Padmavathi S., Dr. Soman K. P., and Aarthi, R., “Image restoration using knowledge from the image”, Advances in Intelligent Systems and Computing, vol. 177 AISC. Chennai, pp. 19-25, 2013.[Abstract]


There are various real world situations where, a portion of the image is lost or damaged which needs an image restoration. A Prior knowledge of the image may not be available for restoring the image, which demands for a knowledge derivation from the image itself. Restoring the lost portions of the image based on the knowledge obtained from the image area surrounding the lost area is called as Digital Image Inpainting. The information content in the lost area could contain structural information like edges or textural information like repeating patterns. This knowledge is derived from the boundary area surrounding the lost area. Based on this, the lost area is restored by looking at similar information in the same image. Experimentation have been done on various images and observed that the algorithm restores the image in a visually plausible way. © 2013 Springer-Verlag.

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2012

R. Aarthi, Chinnaswamy, A., and Dr. Padmavathi S., “A Survey of Different Stages for Monitoring Traffic Rule Violation”, Communications in Computer and Information Science, vol. 270 CCIS. Vellore, pp. 566-573, 2012.[Abstract]


A traffic surveillance system is a controlled system that helps to monitor and regulate the traffic. In this paper, a method for extracting the license number of the vehicle that is exceeding the speed limit is proposed. A Study is conducted by covering various stages of monitoring system such as vehicle detection in the video, tracking the vehicle for speed calculation and extracting the vehicle number in the number plate that can be used in places with high public vicinity. © 2012 Springer-Verlag.

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2011

R. Aarthi, Chinnaswamy, A., and K. Raghesh Krishnan, “Automatic Isolation and Classification of Vehicles in a Traffic Video”, Proceedings of the 2011 World Congress on Information and Communication Technologies (WICT 2011). Mumbai, India., pp. 357-361, 2011.[Abstract]


Among the diverse applications of computer and communication technologies, Intelligent Transport System aids in simplifying transport problems. Its aim is to gather data and provide timely feedback to traffic managers (traffic policemen) and road users. The various problems involved in processing real-time traffic data has been addressed in several areas of research that includes vehicle detection, tracking and classification. This paper proposes a technique for isolation and classification of vehicles at an abstract level. The isolation technique aims at locating regions of interest (vehicles) within the image to be classified. Classification is performed in two categories. The first category is to identify the predominant color and the second is to classify the vehicle as light or heavy. The experimental results show an accuracy of 82% even for traffic video sequences involving complicated scenes.

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Publication Type: Conference Paper

Year of Publication Title

2019

L. R. Kambam and Aarthi, R., “Classification of plastic bottles based on visual and physical features for waste management”, in 2019 IEEE International Conference on Electrical, Computer and Communication Technologies (ICECCT), Coimbatore, India, India, 2019.[Abstract]


This paper presents an approach to identify the type of plastics depending upon it's material, it can be concluded whether the plastic can be recycled or not. In this approach, visual and physical properties were used to classify the plastic materials. It makes use of the fact that recycled plastics are having some similar features like weight, pressure and color. Given an image of a plastic object, these features will form a dataset to train different classifiers which will classify the given plastic into recycled or non-recycled. A color based segmentation algorithm is used to detect color and KNN classifier is used to predict the color of plastic. A tactile touch sensor is used to calculate the pressure that can be applied on the plastic object. Different types of plastics are having inconsistent set of features. Therefore, perceptively we are using four different classifiers for the classification namely SVM, KNN, Decision tree and Logistic Regression.

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2019

S. M. Sundara and Aarthi, R., “Segmentation and Evaluation of White Blood Cells using Segmentation Algorithms”, in 2019 3rd International Conference on Trends in Electronics and Informatics (ICOEI), Tirunelveli, India, India, 2019.[Abstract]


Image Segmentation is an important step in Image processing applications. Microscopic images of White Blood Cells (WBC) enable hematologists to predict the vulnerability to several diseases. Automatic segmentation of different types of WBCs is the most challenging task in the prediction of the presence of disease. Our objective is to segment the blood cells in microscopic images using segmentation algorithms followed by evaluation of their performance. Color is a major cue to discern segmented WBCs from microscopic images. This paper appraises the performance of various color based segmentation techniques, and compare the results against ground truth image. Analysis of the results using dice similarity, shows that saliency based segmentation is the most suitable model to segment WBC cells.

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2015

R. Aarthi and Amudha, J., “Saliency based Modified Chamfers Matching Method for Sketch based Image Retrieval”, in Innovations in Information, Embedded and Communication Systems (ICIIECS), 2015 International Conference on, Karpagam College of Engineering, Coimbatore, India, 2015.

2014

R. Aarthi, Amudha, J., and P, U., “A generic bio inspired framework for detecting Humans Based on saliency detection”, in International Conference on Artificial Intelligence and Evolutionary Algorithms in Engineering Systems-2014 (ICAEES-2014), Kumaracoil, Kanyakumari, Tamilnadu, 2014.

2010

R. Aarthi, Dr. Padmavathi S., and Amudha, J., “Vehicle detection in static images using color and corner map”, in ITC 2010 - 2010 International Conference on Recent Trends in Information, Telecommunication, and Computing, Kochi, Kerala, 2010, pp. 244-246.[Abstract]


This paper presents an approach to identify the vehicle in the static images using color and corner map. The detection of vehicles in a traffic scene can address wide range of traffic problems. Here an attempt has been made to reduce the search time to find the possible vehicle candidates thereby reducing the computation time without a full search. A color transformation is used to project all the colors of input pixels to a new feature space such that vehicle pixels can be easily distinguished from non-vehicle ones. Bayesian classifier is adopted for verifying the vehicle pixels from the background. Corner map is used for removing the false detections and to verify the vehicle candidates. © 2010 IEEE.

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