What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Computer vision
- Operating system
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.
His most cited work include:
- A Regression Approach to Music Emotion Recognition (315 citations)
- Programmable aperture photography: multiplexed light field acquisition (260 citations)
- Machine Recognition of Music Emotion: A Review (237 citations)
What are the main themes of his work throughout his whole career to date?
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.
He most often published in these fields:
- Artificial intelligence (59.63%)
- Computer vision (50.76%)
- Speech recognition (13.15%)
What were the highlights of his more recent work (between 2014-2021)?
- Artificial intelligence (59.63%)
- Computer vision (50.76%)
- Autofocus (4.59%)
In recent papers he was focusing on the following fields of study:
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.
Between 2014 and 2021, his most popular works were:
- The AMG1608 dataset for music emotion recognition (25 citations)
- Extracting Blood Vessels From Full-Field OCT Data of Human Skin by Short-Time RPCA (16 citations)
- IMAGE PROCESSING SYSTEM AND METHOD (14 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Computer vision
- Operating system
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
- Kernel which intersects with area such as Stereo matching, Aperture, Gaussian noise and Gaussian network model,
- Image noise that intertwine with fields like Photon, Gaussian filter and Focus.
His work carried out in the field of Natural language processing brings together such families of science as Representation, Speech recognition and Personalization.
His Speech recognition research incorporates elements of Mixture model, Tying, Adaptation and Feature vector.
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