H-Index & Metrics Top Publications

H-Index & Metrics

Discipline name H-index Citations Publications World Ranking National Ranking
Computer Science H-index 54 Citations 9,894 179 World Ranking 2327 National Ranking 136

Overview

What is he best known for?

The fields of study he is best known for:

  • Artificial intelligence
  • Computer vision
  • Machine learning

The scientist’s investigation covers issues in Artificial intelligence, Computer vision, Pattern recognition, Pose and Discriminative model. Artificial intelligence is often connected to Machine learning in his work. He has included themes like Point and Invariant in his Computer vision study.

The study incorporates disciplines such as Facial recognition system and Robustness in addition to Pattern recognition. His work on 3D pose estimation as part of his general Pose study is frequently connected to Hierarchy, thereby bridging the divide between different branches of science. His study in Discriminative model is interdisciplinary in nature, drawing from both Codebook, Image segmentation, Particle swarm optimization, Iterative refinement and Convolutional neural network.

His most cited work include:

  • Discriminative Learning and Recognition of Image Set Classes Using Canonical Correlations (535 citations)
  • Latent Regression Forest: Structured Estimation of 3D Articulated Hand Posture (283 citations)
  • Locally linear discriminant analysis for multimodally distributed classes for face recognition with a single model image (259 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, Pose and Object. His Artificial intelligence study frequently draws connections to adjacent fields such as Machine learning. Many of his studies involve connections with topics such as Point and Computer vision.

His research links Random forest with Pattern recognition. He combines subjects such as RGB color model, Object detection, Feature learning and Benchmark with his study of Pose. When carried out as part of a general Object research project, his work on Minimum bounding box, Video tracking and Object model is frequently linked to work in Field and Clutter, therefore connecting diverse disciplines of study.

He most often published in these fields:

  • Artificial intelligence (77.32%)
  • Computer vision (45.05%)
  • Pattern recognition (37.70%)

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

  • Artificial intelligence (77.32%)
  • Computer vision (45.05%)
  • Pose (23.32%)

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

His primary areas of investigation include Artificial intelligence, Computer vision, Pose, Object and RGB color model. His study connects Pattern recognition and Artificial intelligence. His biological study deals with issues like Polygon mesh, which deal with fields such as Decimation, Vertex, Upsampling, Graphics and Aggregate.

His Pose research integrates issues from Artificial neural network, Augmented reality and Reinforcement learning. His Object detection study in the realm of Object connects with subjects such as Scale. His study focuses on the intersection of RGB color model and fields such as Bounding overwatch with connections in the field of Intrinsics and Image plane.

Between 2019 and 2021, his most popular works were:

  • A review on object pose recovery: From 3D bounding box detectors to full 6D pose estimators (13 citations)
  • Measuring Generalisation to Unseen Viewpoints, Articulations, Shapes and Objects for 3D Hand Pose Estimation under Hand-Object Interaction (9 citations)
  • Measuring Generalisation to Unseen Viewpoints, Articulations, Shapes and Objects for 3D Hand Pose Estimation under Hand-Object Interaction (9 citations)

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

  • Artificial intelligence
  • Computer vision
  • Machine learning

Tae-Kyun Kim mainly investigates Artificial intelligence, Computer vision, Pose, Reinforcement learning and Object. His Convolutional neural network, Synthetic data and Face study in the realm of Artificial intelligence interacts with subjects such as Graph and Generator. Tae-Kyun Kim has researched Synthetic data in several fields, including Discriminator, Margin, Image, Categorical variable and Pattern recognition.

His Computer vision research is multidisciplinary, relying on both Pipeline and Asynchronous communication. His Pose study combines topics from a wide range of disciplines, such as RGB color model, Polygon mesh and Rendering. His studies in Object integrate themes in fields like Augmented reality and Viewpoints.

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.

Top Publications

Discriminative Learning and Recognition of Image Set Classes Using Canonical Correlations

Tae-Kyun Kim;J. Kittler;R. Cipolla.
IEEE Transactions on Pattern Analysis and Machine Intelligence (2007)

639 Citations

Multiple object tracking: A literature review

Wenhan Luo;Wenhan Luo;Junliang Xing;Anton Milan;Xiaoqin Zhang.
Artificial Intelligence (2021)

397 Citations

Locally linear discriminant analysis for multimodally distributed classes for face recognition with a single model image

Tae-Kyun Kim;J. Kittler.
IEEE Transactions on Pattern Analysis and Machine Intelligence (2005)

368 Citations

Latent Regression Forest: Structured Estimation of 3D Articulated Hand Posture

Danhang Tang;Hyung Jin Chang;Alykhan Tejani;Tae-Kyun Kim.
computer vision and pattern recognition (2014)

357 Citations

Tensor Canonical Correlation Analysis for Action Classification

Tae-Kyun Kim;Shu-Fai Wong;R. Cipolla.
computer vision and pattern recognition (2007)

351 Citations

Canonical Correlation Analysis of Video Volume Tensors for Action Categorization and Detection

Tae-Kyun Kim;R. Cipolla.
IEEE Transactions on Pattern Analysis and Machine Intelligence (2009)

306 Citations

Learning Motion Categories using both Semantic and Structural Information

Shu-Fai Wong;Tae-Kyun Kim;R. Cipolla.
computer vision and pattern recognition (2007)

282 Citations

Real-Time Articulated Hand Pose Estimation Using Semi-supervised Transductive Regression Forests

Danhang Tang;Tsz-Ho Yu;Tae-Kyun Kim.
international conference on computer vision (2013)

257 Citations

Latent-Class Hough Forests for 3D Object Detection and Pose Estimation

Alykhan Tejani;Danhang Tang;Rigas Kouskouridas;Tae-Kyun Kim.
european conference on computer vision (2014)

251 Citations

Multiple Object Tracking: A Literature Review

Wenhan Luo;Junliang Xing;Anton Milan;Xiaoqin Zhang.
arXiv: Computer Vision and Pattern Recognition (2014)

218 Citations

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

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