Aarthi R. currently serves as Assistant Professor at Department of Computer Science and Engineering, School of Engineering, Coimbatore Campus. Her areas of research include Image Processing and Computer Vision.


Publication Type: Journal Article
Year of Publication Publication Type Title
2016 Journal Article 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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2016 Journal Article 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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Publication Type: Conference Proceedings
Year of Publication Publication Type Title
2013 Conference Proceedings S. Padmavathi, 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 Conference Proceedings R. Aarthi, Arunkumar, C., and 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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Publication Type: Conference Paper
Year of Publication Publication Type Title
2011 Conference Paper R. Aarthi, Arunkumar, C., and Megalingam, R. K., “Automatic isolation and classification of vehicles in a traffic video”, in Proceedings of the 2011 World Congress on Information and Communication Technologies, WICT 2011, Mumbai, 2011, pp. 357-361.[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. © 2011 IEEE. More »»
2010 Conference Paper R. Aarthi, 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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