2003 - IEEE Fellow For contributions to the development of technology and standards for digital image and video coding.
His primary areas of investigation include Artificial intelligence, Speech recognition, Computer vision, Valence and Image processing. Homer H. Chen works in the field of Artificial intelligence, focusing on Rate–distortion optimization in particular. The concepts of his Speech recognition study are interwoven with issues in Feature extraction, Emotion classification, Semantic gap and Emotion perception.
He regularly links together related areas like Distortion in his Computer vision studies. His work in Valence addresses subjects such as Categorical variable, which are connected to disciplines such as Support vector machine and Regression analysis. His research in Image processing intersects with topics in Image quality, Focus measure and Low contrast.
Homer H. Chen mainly focuses on Artificial intelligence, Computer vision, Speech recognition, Algorithm and Image processing. Homer H. Chen studies Pixel, a branch of Artificial intelligence. His Computer vision study integrates concerns from other disciplines, such as Lens and Brightness.
His Speech recognition research includes themes of Valence, Feature extraction, Audio signal and Emotion perception. His Algorithm study combines topics from a wide range of disciplines, such as Real-time computing and Theoretical computer science. His work investigates the relationship between Motion estimation and topics such as Motion compensation that intersect with problems in Multiview Video Coding.
Homer H. Chen mostly deals with Artificial intelligence, Computer vision, Autofocus, Speech recognition and Optical coherence tomography. The various areas that Homer H. Chen examines in his Artificial intelligence study include Focus and Natural language processing. His study in Computer vision is interdisciplinary in nature, drawing from both Lens and Compensation.
His Autofocus research is multidisciplinary, incorporating elements of Image noise, Reinforcement learning, Kernel and Digital camera. The study incorporates disciplines such as Valence, Snapshot, Multiple signal classification, Emotion perception and Adaptation in addition to Speech recognition. His Optical coherence tomography study combines topics in areas such as Segmentation, Deep learning, Foveal and Medical imaging.
Homer H. Chen focuses on Artificial intelligence, Computer vision, Autofocus, Lens and Natural language processing. His research integrates issues of Optical coherence tomography and Focus in his study of Artificial intelligence. His study in Computer vision focuses on Color histogram, Color balance, RGB color model, Image processing and Image stitching.
His research on Autofocus also deals with topics like
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A Regression Approach to Music Emotion Recognition
Yi-Hsuan Yang;Yu-Ching Lin;Ya-Fan Su;H.H. Chen.
IEEE Transactions on Audio, Speech, and Language Processing (2008)
Programmable aperture photography: multiplexed light field acquisition
Chia-Kai Liang;Tai-Hsu Lin;Bing-Yi Wong;Chi Liu.
international conference on computer graphics and interactive techniques (2008)
Machine Recognition of Music Emotion: A Review
Yi-Hsuan Yang;Homer H. Chen.
ACM Transactions on Intelligent Systems and Technology (2012)
Popularity-based selective replication in content delivery network
A screw motion approach to uniqueness analysis of head-eye geometry
computer vision and pattern recognition (1991)
Analysis and Compensation of Rolling Shutter Effect
Chia-Kai Liang;Li-Wen Chang;H.H. Chen.
IEEE Transactions on Image Processing (2008)
Music emotion classification: a fuzzy approach
Yi-Hsuan Yang;Chia-Chu Liu;Homer H. Chen.
acm multimedia (2006)
Error-resilient coding in JPEG-2000 and MPEG-4
I. Moccagatta;S. Soudagar;J. Liang;H. Chen.
IEEE Journal on Selected Areas in Communications (2000)
A survey of construction and manipulation of octrees
Homer H. Chen;Thomas S. Huang.
Graphical Models /graphical Models and Image Processing /computer Vision, Graphics, and Image Processing (1988)
Pose determination from line-to-plane correspondences: existence condition and closed-form solutions
IEEE Transactions on Pattern Analysis and Machine Intelligence (1991)
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