2002 - IAPR King-Sun Fu Prize
2001 - Jack S. Kilby Signal Processing Medal For pioneering and sustained contributions to image sequence processing and its applications to digital TV
1997 - SPIE Fellow
1994 - Fellow of the International Association for Pattern Recognition (IAPR) For contributions to computer vision and image processing, including motion analysis and image compression and service to the IAPR
1979 - IEEE Fellow For contributions to the theory and application of image processing and digital filtering.
1971 - Fellow of John Simon Guggenheim Memorial Foundation
His main research concerns Artificial intelligence, Pattern recognition, Computer vision, Feature extraction and Machine learning. His is doing research in Facial recognition system, Image processing, Contextual image classification, Support vector machine and Artificial neural network, both of which are found in Artificial intelligence. As a member of one scientific family, Thomas S. Huang mostly works in the field of Facial recognition system, focusing on Speech recognition and, on occasion, Gesture.
The Pattern recognition study combines topics in areas such as Kernel and Feature. His Computer vision study frequently intersects with other fields, such as Point. His research integrates issues of Training set and Robustness in his study of Machine learning.
His scientific interests lie mostly in Artificial intelligence, Computer vision, Pattern recognition, Machine learning and Facial recognition system. Feature extraction, Motion estimation, Image, Contextual image classification and Feature are the primary areas of interest in his Artificial intelligence study. Many of his studies on Motion estimation involve topics that are commonly interrelated, such as Motion analysis.
His work on Computer vision deals in particular with Image processing, Face, Video tracking, Image segmentation and Face detection. His Pattern recognition study frequently involves adjacent topics like Speech recognition. Machine learning is often connected to Image retrieval in his work.
Artificial intelligence, Pattern recognition, Machine learning, Computer vision and Image are his primary areas of study. His Deep learning, Segmentation, Artificial neural network, Discriminative model and Feature investigations are all subjects of Artificial intelligence research. Thomas S. Huang studied Deep learning and Neural coding that intersect with Sparse approximation.
His research in Pattern recognition intersects with topics in Pixel, Pascal and Object detection. His work carried out in the field of Machine learning brings together such families of science as Contextual image classification and Representation. His studies in Image integrate themes in fields like Domain and Recurrent neural network.
His primary areas of study are Artificial intelligence, Pattern recognition, Image, Deep learning and Computer vision. As part of his studies on Artificial intelligence, Thomas S. Huang often connects relevant subjects like Machine learning. Thomas S. Huang interconnects Contextual image classification, Rank and Neural coding in the investigation of issues within Machine learning.
His study in Pattern recognition is interdisciplinary in nature, drawing from both Pascal and Robustness. His Deep learning research is multidisciplinary, incorporating elements of Outlier, Range, Noise reduction, Convolutional neural network and Joint. His Feature extraction study combines topics from a wide range of disciplines, such as Image segmentation and Task analysis.
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.
T.S. Huang;W.F. Schreiber;O.J. Tretiak.
Proceedings of the IEEE (1971)
Image Super-Resolution Via Sparse Representation
Jianchao Yang;John Wright;Thomas S Huang;Yi Ma.
IEEE Transactions on Image Processing (2010)
Least-Squares Fitting of Two 3-D Point Sets
K. S. Arun;T. S. Huang;S. D. Blostein.
IEEE Transactions on Pattern Analysis and Machine Intelligence (1987)
Locality-constrained Linear Coding for image classification
Jinjun Wang;Jianchao Yang;Kai Yu;Fengjun Lv.
computer vision and pattern recognition (2010)
Linear spatial pyramid matching using sparse coding for image classification
Jianchao Yang;Kai Yu;Yihong Gong;Thomas Huang.
computer vision and pattern recognition (2009)
A Survey of Affect Recognition Methods: Audio, Visual, and Spontaneous Expressions
Zhihong Zeng;M. Pantic;G.I. Roisman;T.S. Huang.
IEEE Transactions on Pattern Analysis and Machine Intelligence (2009)
Visual interpretation of hand gestures for human-computer interaction: a review
V.I. Pavlovic;R. Sharma;T.S. Huang.
IEEE Transactions on Pattern Analysis and Machine Intelligence (1997)
Relevance feedback: a power tool for interactive content-based image retrieval
Yong Rui;T.S. Huang;M. Ortega;S. Mehrotra.
IEEE Transactions on Circuits and Systems for Video Technology (1998)
Sparse Representation for Computer Vision and Pattern Recognition
John Wright;Yi Ma;Julien Mairal;Guillermo Sapiro.
Proceedings of the IEEE (2010)
Image super-resolution as sparse representation of raw image patches
Jianchao Yang;J. Wright;T. Huang;Yi Ma.
computer vision and pattern recognition (2008)
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