Qualification: 
Ph.D, M.E, BE
athinarayanans@am.amrita.edu

Dr. Athi Narayanan currently serves as Assistant Professor (Selection Grade) at the Department of Computer Science and Engineering, Amrita School of Engineering, Amritapuri.

Dr. Athi earned his Ph. D. (Computer Science Engineering) in 2016 from Amrita Vishwa Vidyapeetham under the guidance of Dr. Kaimal. He received the B. E. degree (Electronics and Communication Engineering) in 2005 from Anna University, Chennai, and the M. E. degree (Embedded System Technologies) in 2010 from Anna University of Technology, Coimbatore.

In Amrita E- learning Research Lab, as a Senior Research Associate, he led the Image Recognition Team of the A- VIEW project. In Manatec Electronics, Puducherry, as an R & D team leader, he has developed real- time computer vision and pattern recognition algorithms for the India’s first indigenous 3D wheel alignment system. In Nihon Technology, Chennai, as a Project Leader, he has led and developed ARIB standard color quantization algorithms for digital broadcasting in Japan. In Jasmin Infotech, Chennai, as a digital signal processing (DSP) Engineer, he has developed embedded DSP multimedia codecs.

Dr. Athi holds high rank in the MATLAB central exchange. He has won the second place, in the Algorithm submission contest conducted by MATLAB Central Exchange for Artificial Intelligence based 2048 Solver. His biography has been selected and published in the 28th Edition of Marquis Who’s Who in the World for his research work in digital broadcasting. He was selected for the Indian National Mathematics Olympiad 2000.

EDUCATION

  • 2016: Ph. D. Computer Science Engineering
    Amrita Vishwa Vidyapeetham
  • 2010: M. E. Embedded System Technologies
    Anna University of Technology, Coimbatore
  • 2005: B. E. Electronics and Communication Engineering
    Anna University, Chennai
     

Publications

Publication Type: Patent

Year of Publication Publication Type Title

2017

Patent

Dr. Athinarayanan S. and Bijlani, K., “Systems and Methods for Yaw Estimation”, U.S. Patent US 14/728,7952017.[Abstract]


Systems and methods of automatic detection of a facial feature are disclosed. Moreover, methods and systems of yaw estimation of a human head based on a geometrical model are also disclosed.

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2017

Patent

Dr. Athinarayanan S. and Bijlani, K., “Systems and Methods for Yaw Estimation”, U.S. Patent US 15/373,8502017.[Abstract]


Systems and methods of automatic detection of a facial feature are disclosed. Moreover, methods and systems of yaw estimation of a human head based on a geometrical model are also disclosed.

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Publication Type: Journal Article

Year of Publication Publication Type Title

2016

Journal Article

Dr. Athinarayanan S., Kaimal, R. M., and Bijlani, K., “Estimation of driver head yaw angle using a generic geometric model”, IEEE Transactions on Intelligent Transportation Systems, vol. 17, no. 12, pp. 3446-3460, 2016.[Abstract]


Head yaw angle is an important indicator of a driver's distraction. Forward collision warning systems incorporate head yaw angle estimators for detecting driver's head-off-road glances. This paper presents a generic geometric model for head yaw estimation. The generic model is customized into 12 different models. The performance of the cylindrical and ellipsoidal models is improved by introducing nose projection and truncation. The yaw estimation problem is formulated using a single variable. This formulation provides better visualization of the cylindrical and ellipsoidal models. It is shown that the ellipsoidal model in literature is indeed a cylindrical model with nose projection. We have also proved that, under the ellipsoidal/cylindrical framework, the estimated yaw angle is independent of shifting the center of rotation along depth. Four previous works are shown as the customization of the proposed generic model. The performance of the proposed models are evaluated using four standard head pose datasets and an ADAS dataset. The proposed nose-projected truncated ellipsoidal model outperforms the state-of-the-art geometric models. Further, we have studied the impact of model parameters and inaccurate facial feature localization through simulation experiments. © 2000-2011 IEEE.

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2014

Journal Article

Dr. Athinarayanan S., Kaimal, R. Mb, and Bijlani, Kc, “Yaw estimation using cylindrical and ellipsoidal face models”, IEEE Transactions on Intelligent Transportation Systems, vol. 15, no. 5, pp. 2308-2320, 2014.[Abstract]


Accurate head yaw estimation is necessary for detecting driver inattention in forward collision warning systems. In this paper, we propose three geometric models under the ellipsoidal framework for accurate head yaw estimation. We present theoretical analysis of the cylindrical and ellipsoidal face models used for yaw angle estimation of head rotation. The relationship between cylindrical, ellipsoidal, and proposed models is derived. We provide error functions for all models. Furthermore, for each model, over/under estimation of angle, zero crossings of error, bounds on yaw angle estimate, and bounds on error are presented. Experimental results of the proposed models on four standard head pose data sets yielded a mean absolute error between 4° and 8° demonstrating the efficacy of the proposed models over the state-of-the-art methods.

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2011

Journal Article

P. S. Periasamy, Dr. Athinarayanan S., and Duraiswamy, K., “An adaptive thresholding-based color reduction algorithm and its applications”, International Journal of Image and Graphics, vol. 11, no. 1, pp. 83–101, 2011.[Abstract]


In this paper, we present a novel adaptive thresholding-based color reduction algorithm. The proposed algorithm supports creation of a common palette for multiple images and transparent alpha images. This method was extensively tested for a large set of images and the results are reported here. The applications of proposed algorithm like qualitative image description, digital broadcasting, and bandwidth reduction are discussed in detail. The quality metric values of the experimental results show that the proposed method produces excellent results and outperforms existing state-of-the-art color reduction methods.

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207
PROGRAMS
OFFERED
6
AMRITA
CAMPUSES
15
CONSTITUENT
SCHOOLS
A
GRADE BY
NAAC, MHRD
8th
RANK(INDIA):
NIRF 2018
150+
INTERNATIONAL
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