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
Engineering and Technology D-index 30 Citations 3,386 115 World Ranking 6799 National Ranking 301

Overview

What is he best known for?

The fields of study he is best known for:

  • Artificial intelligence
  • Computer vision
  • Algorithm

His primary scientific interests are in Artificial intelligence, Algorithm, Data modeling, Computer vision and Outlier. His research in Artificial intelligence intersects with topics in Algorithm design, Machine learning and Pattern recognition. His studies in Machine learning integrate themes in fields like Tree and Search problem, Mathematical optimization, Heuristic.

He mostly deals with Time complexity in his studies of Algorithm. The Feature, Image stitching, RANSAC and Multiple hypothesis tracker research Tat-Jun Chin does as part of his general Computer vision study is frequently linked to other disciplines of science, such as Thin plate spline, therefore creating a link between diverse domains of science. His work carried out in the field of Outlier brings together such families of science as Linear programming, Image registration, Point cloud and Reduction.

His most cited work include:

  • As-Projective-As-Possible Image Stitching with Moving DLT (231 citations)
  • Incremental Kernel Principal Component Analysis (128 citations)
  • Dynamic and hierarchical multi-structure geometric model fitting (103 citations)

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

The scientist’s investigation covers issues in Artificial intelligence, Computer vision, Algorithm, Pattern recognition and Mathematical optimization. His Machine learning research extends to the thematically linked field of Artificial intelligence. Tat-Jun Chin focuses mostly in the field of Computer vision, narrowing it down to topics relating to Visual odometry and, in certain cases, Monocular.

His Matching study in the realm of Algorithm interacts with subjects such as Geometric modeling. His Pattern recognition research incorporates elements of Facial recognition system, Image and Subspace topology. His Mathematical optimization study integrates concerns from other disciplines, such as Tree and Randomized algorithm.

He most often published in these fields:

  • Artificial intelligence (69.03%)
  • Computer vision (31.61%)
  • Algorithm (22.58%)

What were the highlights of his more recent work (between 2019-2021)?

  • Artificial intelligence (69.03%)
  • Algorithm (22.58%)
  • Computer vision (31.61%)

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

Tat-Jun Chin mainly investigates Artificial intelligence, Algorithm, Computer vision, Pattern recognition and Rotation. His Artificial intelligence study frequently involves adjacent topics like Machine learning. His Algorithm research is multidisciplinary, incorporating elements of Structure and Connection.

His research links Pattern recognition with Computer vision. Within one scientific family, he focuses on topics pertaining to Image under Pattern recognition, and may sometimes address concerns connected to Margin. The various areas that Tat-Jun Chin examines in his Rotation study include Semidefinite programming, Strong duality, Coordinate descent, Translation and Relaxation.

Between 2019 and 2021, his most popular works were:

  • Robust Fitting in Computer Vision: Easy or Hard? (25 citations)
  • Unsupervised Depth Learning in Challenging Indoor Video: Weak Rectification to Rescue. (11 citations)
  • Deterministic Approximate Methods for Maximum Consensus Robust Fitting (11 citations)

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

  • Artificial intelligence
  • Computer vision
  • Algorithm

His scientific interests lie mostly in Artificial intelligence, Computer vision, Pattern recognition, Robotics and Deep learning. Tat-Jun Chin interconnects Time complexity and Least squares in the investigation of issues within Artificial intelligence. His work on Tracking, BitTorrent tracker and Motion estimation as part of general Computer vision study is frequently linked to Asynchronous operation and Event, bridging the gap between disciplines.

He has researched Pattern recognition in several fields, including Image, Noise and Translation. His Robotics study combines topics from a wide range of disciplines, such as Ontology and Semantics. His Deep learning research integrates issues from Intrinsics, Optimization problem, Solver and Pose.

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

As-Projective-As-Possible Image Stitching with Moving DLT

Julio Zaragoza;Tat-Jun Chin;Quoc-Huy Tran;Michael S. Brown.
computer vision and pattern recognition (2013)

436 Citations

Incremental Kernel Principal Component Analysis

Tat-Jun Chin;D. Suter.
IEEE Transactions on Image Processing (2007)

167 Citations

The Random Cluster Model for robust geometric fitting

Trung Thanh Pham;Tat-Jun Chin;Jin Yu;David Suter.
computer vision and pattern recognition (2012)

131 Citations

Dynamic and hierarchical multi-structure geometric model fitting

Hoi Sim Wong;Tat-Jun Chin;Jin Yu;David Suter.
international conference on computer vision (2011)

124 Citations

Robust fitting of multiple structures: The statistical learning approach

Tat-Jun Chin;Hanzi Wang;David Suter.
international conference on computer vision (2009)

122 Citations

Accelerated Hypothesis Generation for Multistructure Data via Preference Analysis

Tat-Jun Chin;Jin Yu;D. Suter.
IEEE Transactions on Pattern Analysis and Machine Intelligence (2012)

117 Citations

Simultaneously Fitting and Segmenting Multiple-Structure Data with Outliers

Hanzi Wang;Tat-Jun Chin;D. Suter.
IEEE Transactions on Pattern Analysis and Machine Intelligence (2012)

112 Citations

Clustering with Hypergraphs: The Case for Large Hyperedges

Pulak Purkait;Tat-Jun Chin;Hanno Ackermann;David Suter.
european conference on computer vision (2014)

94 Citations

Seam-Driven Image Stitching

Junhong Gao;Yu Li;Tat-Jun Chin;Michael S. Brown.
eurographics (2013)

93 Citations

A Multiple Hypothesis Tracker for Multitarget Tracking With Multiple Simultaneous Measurements

Thuraiappah Sathyan;Tat-Jun Chin;Sanjeev Arulampalam;David Suter.
IEEE Journal of Selected Topics in Signal Processing (2013)

93 Citations

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