H-Index & Metrics Best Publications

H-Index & Metrics

Discipline name H-index Citations Publications World Ranking National Ranking
Computer Science D-index 34 Citations 4,421 193 World Ranking 6286 National Ranking 183

Overview

What is he best known for?

The fields of study he is best known for:

  • Artificial intelligence
  • Computer vision
  • Statistics

Vinod Chandran mainly focuses on Artificial intelligence, Pattern recognition, Bispectrum, Feature extraction and Electroencephalography. His Artificial intelligence research is multidisciplinary, incorporating perspectives in Machine learning and Computer vision. His work is dedicated to discovering how Pattern recognition, Facial recognition system are connected with Fractal transform and other disciplines.

His Bispectrum study integrates concerns from other disciplines, such as Speech recognition, Invariant, Fourier transform and Signal processing. His studies in Feature extraction integrate themes in fields like Feature vector, Contextual image classification, Gaussian noise, Feature selection and Robustness. His Electroencephalography research is multidisciplinary, incorporating elements of Emotion classification and Epilepsy.

His most cited work include:

  • Application of higher order statistics/spectra in biomedical signals—A review (141 citations)
  • Pattern Recognition Using Invariants Defined From Higher Order Spectra- One Dimensional Inputs (137 citations)
  • Cardiac state diagnosis using higher order spectra of heart rate variability (113 citations)

What are the main themes of his work throughout his whole career to date?

Vinod Chandran mainly investigates Artificial intelligence, Pattern recognition, Computer vision, Feature extraction and Speech recognition. His works in Facial recognition system, Biometrics, Face, Segmentation and Robustness are all subjects of inquiry into Artificial intelligence. His research integrates issues of Image processing and Invariant in his study of Pattern recognition.

His Feature extraction study combines topics from a wide range of disciplines, such as Contextual image classification and Feature vector. As a part of the same scientific family, Vinod Chandran mostly works in the field of Speech recognition, focusing on Electroencephalography and, on occasion, Bicoherence. His research investigates the link between JPEG 2000 and topics such as Coding that cross with problems in Interpretability.

He most often published in these fields:

  • Artificial intelligence (72.17%)
  • Pattern recognition (44.66%)
  • Computer vision (37.22%)

What were the highlights of his more recent work (between 2014-2021)?

  • Artificial intelligence (72.17%)
  • Pattern recognition (44.66%)
  • Computer vision (37.22%)

In recent papers he was focusing on the following fields of study:

Artificial intelligence, Pattern recognition, Computer vision, Feature extraction and Segmentation are his primary areas of study. His studies in Biometrics, Support vector machine, Iris recognition, Facial expression and Image segmentation are all subfields of Artificial intelligence research. His work carried out in the field of Pattern recognition brings together such families of science as Facial recognition system, Invariant, Feature and Robustness.

His study of Stereo camera is a part of Computer vision. His study in Feature extraction is interdisciplinary in nature, drawing from both Discrete wavelet transform, Speech recognition, Local binary patterns, Deep learning and Signal processing. His Segmentation research includes elements of Closing and Boundary.

Between 2014 and 2021, his most popular works were:

  • Evolutionary computation algorithms for feature selection of EEG-based emotion recognition using mobile sensors (75 citations)
  • Application of wavelet techniques for cancer diagnosis using ultrasound images (46 citations)
  • Physical Activity Recognition Using Posterior-Adapted Class-Based Fusion of Multiaccelerometer Data (44 citations)

In his most recent research, the most cited papers focused on:

  • Artificial intelligence
  • Computer vision
  • Statistics

The scientist’s investigation covers issues in Artificial intelligence, Pattern recognition, Feature extraction, Support vector machine and Segmentation. Vinod Chandran has researched Artificial intelligence in several fields, including Machine learning and Computer vision. His Normalization, Biometrics, Dilation and Coordinate system study in the realm of Computer vision connects with subjects such as Biomechanical model.

In general Pattern recognition, his work in Feature vector is often linked to Multiplication linking many areas of study. His Feature extraction research incorporates themes from Bilinear interpolation, Discrete wavelet transform, Speech recognition, Signal processing and Feature selection. The various areas that he examines in his Support vector machine study include Local binary patterns and Linear discriminant analysis.

This overview was generated by a machine learning system which analysed the scientist’s body of work. If you have any feedback, you can contact us here.

Best Publications

Pattern Recognition Using Invariants Defined From Higher Order Spectra- One Dimensional Inputs

V. Chandran;S.L. Elgar.
IEEE Transactions on Signal Processing (1993)

236 Citations

Application of higher order statistics/spectra in biomedical signals—A review

Kuang Chua Chua;Vinod Chandran;U. Rajendra Acharya;Choo Min Lim.
Medical Engineering & Physics (2010)

192 Citations

Cardiac state diagnosis using higher order spectra of heart rate variability

K. C. Chua;V. Chandran;U. R. Acharya;C. M. Lim.
Journal of Medical Engineering & Technology (2008)

150 Citations

Face authentication test on the BANCA database

K. Messer;J. Kittler;M. Sadeghi;M. Hamouz.
international conference on pattern recognition (2004)

148 Citations

Application of Higher Order Spectra to Identify Epileptic EEG

Kuang Chua Chua;V. Chandran;U. Rajendra Acharya;C. M. Lim.
Journal of Medical Systems (2011)

146 Citations

Pattern recognition using invariants defined from higher order spectra: 2-D image inputs

V. Chandran;B. Carswell;B. Boashash;S. Elgar.
IEEE Transactions on Image Processing (1997)

128 Citations

Analysis of epileptic EEG signals using higher order spectra

K. C. Chua;V. Chandran;U. Rajendra Acharya;C. M. Lim.
Journal of Medical Engineering & Technology (2009)

123 Citations

Breast cancer detection from thermal images using bispectral invariant features

Mahnaz EtehadTavakol;Vinod Chandran;E.Y.K. Ng;Raheleh Kafieh.
International Journal of Thermal Sciences (2013)

119 Citations

Biometric Based Cryptographic Key Generation from Faces

B. Chen;V. Chandran.
digital image computing: techniques and applications (2007)

117 Citations

An adaptive optical flow technique for person tracking systems

Simon Denman;Vinod Chandran;Sridha Sridharan.
Pattern Recognition Letters (2007)

111 Citations

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