Quantum mechanics is intertwined with Asymmetry and Local symmetry in his research. His study in Quantum mechanics extends to Asymmetry with its themes. In his papers, Tat-Jen Cham integrates diverse fields, such as Computer vision and Speech recognition. Tat-Jen Cham integrates several fields in his works, including Speech recognition and Deep learning. He performs integrative Deep learning and Feature extraction research in his work. Many of his studies on Feature extraction apply to Cognitive neuroscience of visual object recognition as well. The study of Artificial intelligence is intertwined with the study of Preprocessor in a number of ways. His Preprocessor study frequently draws connections to adjacent fields such as Computer vision. In his works, Tat-Jen Cham performs multidisciplinary study on Geometry and Curvature.
Tat-Jen Cham is involved in relevant fields of research such as Feature (linguistics) and Regularization (linguistics) in the field of Linguistics. His research on Feature (linguistics) frequently connects to adjacent areas such as Linguistics. His study ties his expertise on Motion (physics) together with the subject of Artificial intelligence. In his papers, Tat-Jen Cham integrates diverse fields, such as Computer vision and Artificial neural network. By researching both Artificial neural network and Artificial intelligence, he produces research that crosses academic boundaries. Tat-Jen Cham combines topics linked to Facial recognition system with his work on Pattern recognition (psychology). Facial recognition system and Pattern recognition (psychology) are commonly linked in his work. His Image (mathematics) study frequently draws connections to other fields, such as Computer vision. His Affine transformation research extends to Geometry, which is thematically connected.
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.
A multiple hypothesis approach to figure tracking
Tat-Jen Cham;J.M. Rehg.
computer vision and pattern recognition (1999)
A dynamic Bayesian network approach to figure tracking using learned dynamic models
V. Pavlovic;J.M. Rehg;Tat-Jen Cham;K.P. Murphy.
international conference on computer vision (1999)
Pluralistic Image Completion
Chuanxia Zheng;Tat-Jen Cham;Jianfei Cai.
computer vision and pattern recognition (2019)
Exploiting Spatial-Temporal Relationships for 3D Pose Estimation via Graph Convolutional Networks
Yujun Cai;Liuhao Ge;Jun Liu;Jianfei Cai.
international conference on computer vision (2019)
Fast training and selection of Haar features using statistics in boosting-based face detection
Minh-Tri Pham;Tat-Jen Cham.
international conference on computer vision (2007)
Large-Margin Multi-Modal Deep Learning for RGB-D Object Recognition
Anran Wang;Jiwen Lu;Jianfei Cai;Tat-Jen Cham.
IEEE Transactions on Multimedia (2015)
Wireless multi-user multi-projector presentation system
Rahul Sukthankar;Tat-Jen Cham;Gita R. Sukthankar;James M. Rehg.
Method for efficiently tracking object models in video sequences via dynamic ordering of features
Tat-Jen Cham;James Matthew Rehg.
Dynamic shadow elimination for multi-projector displays
R. Sukthankar;Tat-Jen Cham;G. Sukthankar.
computer vision and pattern recognition (2001)
T2Net: Synthetic-to-Realistic Translation for Solving Single-Image Depth Estimation Tasks
Chuanxia Zheng;Tat Jen Cham;Jianfei Cai.
european conference on computer vision (2018)
If you think any of the details on this page are incorrect, let us know.
We appreciate your kind effort to assist us to improve this page, it would be helpful providing us with as much detail as possible in the text box below: