Sanghoon Lee focuses on Artificial intelligence, Computer vision, Electronic engineering, Human visual system model and Convolutional neural network. His work deals with themes such as Video quality and Pattern recognition, which intersect with Artificial intelligence. His Computer vision research is multidisciplinary, incorporating elements of Depth perception and Stereo display.
His Electronic engineering research includes themes of Audio signal flow, Electrical engineering and Noise figure. His Human visual system model research incorporates elements of Reduction, Motion estimation, Visual perception, Wavelet transform and Scene statistics. His Convolutional neural network study combines topics from a wide range of disciplines, such as Waveform, Intelligent sensor, Support vector machine and Gesture.
Sanghoon Lee mostly deals with Artificial intelligence, Computer vision, Electronic engineering, Electrical engineering and Optoelectronics. His research on Artificial intelligence often connects related topics like Pattern recognition. Computer vision is represented through his Stereoscopy and Image processing research.
He has researched Electronic engineering in several fields, including Computer network and Communication channel, Orthogonal frequency-division multiplexing. His research related to Voltage and Signal might be considered part of Electrical engineering. The study incorporates disciplines such as Layer and Substrate in addition to Optoelectronics.
Sanghoon Lee mainly investigates Artificial intelligence, Computer vision, Pattern recognition, Convolutional neural network and Visualization. His Artificial intelligence study frequently links to related topics such as Distortion. His Computer vision course of study focuses on Display size and Display resolution.
His studies in Convolutional neural network integrate themes in fields like Image quality and Visual perception. His biological study deals with issues like Stereoscopy, which deal with fields such as Salience. Sanghoon Lee interconnects Quality and Algorithm in the investigation of issues within Feature extraction.
His scientific interests lie mostly in Artificial intelligence, Computer vision, Convolutional neural network, Feature extraction and Visualization. The study of Artificial intelligence is intertwined with the study of Pattern recognition in a number of ways. The Computer vision study combines topics in areas such as Robustness, Virtual reality, High-definition video and Stereo display.
His work in Convolutional neural network covers topics such as Visual perception which are related to areas like Digital image and Vision science. In Feature extraction, Sanghoon Lee works on issues like Quality, which are connected to Nonlinear distortion and Transform coding. Sanghoon Lee has included themes like Monocular and Fixation in his Visualization study.
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.
Non-orthogonal Multiple Access in a Downlink Multiuser Beamforming System
Beomju Kim;Sungmook Lim;Hyungjong Kim;Sangwook Suh.
military communications conference (2013)
Method and apparatus for executing voice command in electronic device
Subhojit Chakladar;Sang-Hoon Lee;Hee-Woon Kim.
(2013)
Fully Deep Blind Image Quality Predictor
Jongyoo Kim;Sanghoon Lee.
IEEE Journal of Selected Topics in Signal Processing (2017)
Ensemble Deep Learning for Skeleton-Based Action Recognition Using Temporal Sliding LSTM Networks
Inwoong Lee;Doyoung Kim;Seoungyoon Kang;Sanghoon Lee.
international conference on computer vision (2017)
Foveated video compression with optimal rate control
Sanghoon Lee;M.S. Pattichis;A.C. Bovik.
IEEE Transactions on Image Processing (2001)
Foveated video quality assessment
Sanghoon Lee;M.S. Pattichis;A.C. Bovik.
IEEE Transactions on Multimedia (2002)
Deep Convolutional Neural Models for Picture-Quality Prediction: Challenges and Solutions to Data-Driven Image Quality Assessment
Jongyoo Kim;Hui Zeng;Deepti Ghadiyaram;Sanghoon Lee.
IEEE Signal Processing Magazine (2017)
An online identification method for both stator-and rotor resistances of induction motors without rotational transducers
In-Joong Ha;Sang-Hoon Lee.
IEEE Transactions on Industrial Electronics (2000)
A master and slave control strategy for parallel operation of three-phase UPS systems with different ratings
Woo-Cheol Lee;Taeck-Ki Lee;Sang-Hoon Lee;Kyung-Hwan Kim.
applied power electronics conference (2004)
Deep Learning of Human Visual Sensitivity in Image Quality Assessment Framework
Jongyoo Kim;Sanghoon Lee.
computer vision and pattern recognition (2017)
The University of Texas at Austin
Yonsei University
Chung-Ang University
Sungkyunkwan University
Seoul National University Hospital
Yonsei University
University of Southern California
Profile was last updated on December 6th, 2021.
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