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 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.
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.
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.
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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)
Multiple object tracking: A literature review
Wenhan Luo;Wenhan Luo;Junliang Xing;Anton Milan;Xiaoqin Zhang.
Artificial Intelligence (2021)
Tensor Canonical Correlation Analysis for Action Classification
Tae-Kyun Kim;Shu-Fai Wong;R. Cipolla.
computer vision and pattern recognition (2007)
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)
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)
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)
Learning Motion Categories using both Semantic and Structural Information
Shu-Fai Wong;Tae-Kyun Kim;R. Cipolla.
computer vision and pattern recognition (2007)
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)
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)
First-Person Hand Action Benchmark with RGB-D Videos and 3D Hand Pose Annotations
Guillermo Garcia-Hernando;Shanxin Yuan;Seungryul Baek;Tae-Kyun Kim.
computer vision and pattern recognition (2018)
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