D-Index & Metrics Best Publications

D-Index & Metrics

Discipline name D-index D-index (Discipline H-index) only includes papers and citation values for an examined discipline in contrast to General H-index which accounts for publications across all disciplines. Citations Publications World Ranking National Ranking
Computer Science D-index 44 Citations 10,675 201 World Ranking 3690 National Ranking 1890

Overview

What is he best known for?

The fields of study he is best known for:

  • Artificial intelligence
  • Computer vision
  • Machine learning

His scientific interests lie mostly in Artificial intelligence, Computer vision, Pattern recognition, Machine learning and Set. His work in Artificial intelligence is not limited to one particular discipline; it also encompasses Perception. His Computer vision research incorporates themes from Point and Detector.

His specific area of interest is Pattern recognition, where he studies Feature extraction. His study in the field of Bayesian network also crosses realms of Developmental change, Site monitoring, Exploit and Graph spectra. The various areas that Sudeep Sarkar examines in his Set study include Change detection and Feature.

His most cited work include:

  • The humanID gait challenge problem: data sets, performance, and analysis (922 citations)
  • Comparison and combination of ear and face images in appearance-based biometrics (495 citations)
  • A robust visual method for assessing the relative performance of edge-detection algorithms (449 citations)

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

His primary areas of study are Artificial intelligence, Computer vision, Pattern recognition, Segmentation and Machine learning. His studies examine the connections between Artificial intelligence and genetics, as well as such issues in Finite element method, with regards to Iterative method. Sudeep Sarkar works mostly in the field of Computer vision, limiting it down to concerns involving Facial expression and, occasionally, Robustness.

His research investigates the link between Pattern recognition and topics such as Gait that cross with problems in Silhouette. As part of the same scientific family, Sudeep Sarkar usually focuses on Segmentation, concentrating on Hidden Markov model and intersecting with Gesture. His Facial recognition system study combines topics in areas such as Principal component analysis and Biometrics.

He most often published in these fields:

  • Artificial intelligence (71.27%)
  • Computer vision (38.18%)
  • Pattern recognition (22.55%)

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

  • Artificial intelligence (71.27%)
  • Pattern recognition (22.55%)
  • Machine learning (9.45%)

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

Sudeep Sarkar spends much of his time researching Artificial intelligence, Pattern recognition, Machine learning, Deep learning and Pattern theory. The concepts of his Artificial intelligence study are interwoven with issues in Computer vision and Natural language processing. In general Pattern recognition, his work in Feature extraction and Pattern recognition is often linked to Frame linking many areas of study.

His study in Machine learning is interdisciplinary in nature, drawing from both Question answering, Training set and Common sense. His Deep learning research includes elements of Image resolution and Robustness. His Pattern theory study also includes fields such as

  • Inference which connect with Data set, Representation, Data mining and Parsing,
  • Annotation and Overfitting most often made with reference to Activity recognition.

Between 2014 and 2021, his most popular works were:

  • Hydra: An Ensemble of Convolutional Neural Networks for Geospatial Land Classification (42 citations)
  • Non-Boolean computing with nanomagnets for computer vision applications (39 citations)
  • Employing fusion of learned and handcrafted features for unconstrained ear recognition (37 citations)

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

  • Artificial intelligence
  • Computer vision
  • Machine learning

Sudeep Sarkar mainly focuses on Artificial intelligence, Computer vision, Pattern recognition, Pattern theory and Invariant. His Artificial intelligence and Convolutional neural network, Segmentation, Training set, Predictive learning and Supervised learning investigations all form part of his Artificial intelligence research activities. His study explores the link between Training set and topics such as Region of interest that cross with problems in Machine learning.

Sudeep Sarkar has researched Computer vision in several fields, including Gyroscope, Nanomagnet, Magnetic energy and Accelerometer. Particularly relevant to Pattern recognition is his body of work in Pattern recognition. His Pattern theory research is multidisciplinary, incorporating perspectives in Object, Inference, Data set and Overfitting.

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

The humanID gait challenge problem: data sets, performance, and analysis

S. Sarkar;P.J. Phillips;Z. Liu;I.R. Vega.
IEEE Transactions on Pattern Analysis and Machine Intelligence (2005)

1346 Citations

Comparison and combination of ear and face images in appearance-based biometrics

Kyong Chang;K.W. Bowyer;S. Sarkar;B. Victor.
IEEE Transactions on Pattern Analysis and Machine Intelligence (2003)

737 Citations

A robust visual method for assessing the relative performance of edge-detection algorithms

M.D. Heath;S. Sarkar;T. Sanocki;K.W. Bowyer.
IEEE Transactions on Pattern Analysis and Machine Intelligence (1997)

661 Citations

Comparison of edge detectors: a methodology and initial study

M. Heath;S. Sarkar;T. Sanocki;K. Bowyer.
computer vision and pattern recognition (1996)

527 Citations

Comparison of Edge Detectors

Mike Heath;Sudeep Sarkar;Thomas Sanocki;Kevin Bowyer.
Computer Vision and Image Understanding (1998)

409 Citations

Improved gait recognition by gait dynamics normalization

Zongyi Liu;S. Sarkar.
IEEE Transactions on Pattern Analysis and Machine Intelligence (2006)

395 Citations

Simplest representation yet for gait recognition: averaged silhouette

Zongyi Liu;S. Sarkar.
international conference on pattern recognition (2004)

345 Citations

Quantitative measures of change based on feature organization: eigenvalues and eigenvectors

S. Sarkar;K.L. Boyer.
computer vision and pattern recognition (1996)

309 Citations

Perceptual organization in computer vision: a review and a proposal for a classificatory structure

S. Sarkar;K.L. Boyer.
systems man and cybernetics (1993)

291 Citations

The gait identification challenge problem: data sets and baseline algorithm

P.J. Phillips;S. Sarkar;I. Robledo;P. Grother.
international conference on pattern recognition (2002)

282 Citations

Best Scientists Citing Sudeep Sarkar

Yasushi Yagi

Yasushi Yagi

Osaka University

Publications: 86

Mark S. Nixon

Mark S. Nixon

University of Southampton

Publications: 60

Edwin R. Hancock

Edwin R. Hancock

University of York

Publications: 47

Rama Chellappa

Rama Chellappa

Johns Hopkins University

Publications: 38

John N. Carter

John N. Carter

University of Southampton

Publications: 31

Kevin W. Bowyer

Kevin W. Bowyer

University of Notre Dame

Publications: 25

Guoying Zhao

Guoying Zhao

University of Oulu

Publications: 24

Bir Bhanu

Bir Bhanu

University of California, Riverside

Publications: 23

Xuelong Li

Xuelong Li

Northwestern Polytechnical University

Publications: 22

Dmitry B. Goldgof

Dmitry B. Goldgof

University of South Florida

Publications: 22

Tieniu Tan

Tieniu Tan

Chinese Academy of Sciences

Publications: 21

Song Wang

Song Wang

University of South Carolina

Publications: 19

Jiwen Lu

Jiwen Lu

Tsinghua University

Publications: 19

Dacheng Tao

Dacheng Tao

University of Sydney

Publications: 19

Konstantinos N. Plataniotis

Konstantinos N. Plataniotis

University of Toronto

Publications: 19

Profile was last updated on December 6th, 2021.
Research.com Ranking is based on data retrieved from the Microsoft Academic Graph (MAG).
The ranking d-index is inferred from publications deemed to belong to the considered discipline.

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