What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Computer vision
- Algorithm
His main research concerns Motion estimation, Artificial intelligence, Computer vision, Computer hardware and Real-time computing.
His Motion estimation study incorporates themes from Data compression, Motion compensation, Memory bandwidth and Search algorithm.
His work carried out in the field of Data compression brings together such families of science as Image processing and Transform coding.
In Computer vision, he works on issues like Interpolation, which are connected to Electronic engineering, Enhanced Data Rates for GSM Evolution, Quality, Depth filter and Motion.
The Computer hardware study combines topics in areas such as Macroblock, JPEG 2000, Hardware architecture and Context-adaptive binary arithmetic coding.
His study explores the link between Real-time computing and topics such as Chip that cross with problems in Minimax and Signal compression.
His most cited work include:
- Efficient moving object segmentation algorithm using background registration technique (387 citations)
- Analysis, fast algorithm, and VLSI architecture design for H.264/AVC intra frame coder (299 citations)
- Analysis and architecture design of an HDTV720p 30 frames/s H.264/AVC encoder (263 citations)
What are the main themes of his work throughout his whole career to date?
Liang-Gee Chen mostly deals with Artificial intelligence, Computer vision, Computer hardware, Motion estimation and Algorithm.
His Artificial intelligence research includes themes of Computer graphics and Pattern recognition.
His Computer hardware research incorporates themes from Data compression, Real-time computing, Embedded system and Hardware architecture.
He interconnects Transform coding, Image processing, Image compression, JPEG 2000 and Codec in the investigation of issues within Data compression.
His work in Motion estimation addresses subjects such as Parallel computing, which are connected to disciplines such as Auxiliary memory.
His Algorithm research incorporates elements of Discrete wavelet transform and Very-large-scale integration.
He most often published in these fields:
- Artificial intelligence (31.83%)
- Computer vision (27.33%)
- Computer hardware (25.50%)
What were the highlights of his more recent work (between 2010-2021)?
- Artificial intelligence (31.83%)
- Computer vision (27.33%)
- Feature extraction (4.83%)
In recent papers he was focusing on the following fields of study:
Artificial intelligence, Computer vision, Feature extraction, Computer hardware and Pattern recognition are his primary areas of study.
The study incorporates disciplines such as Machine learning, Computer graphics and Perception in addition to Artificial intelligence.
His Feature extraction study also includes fields such as
- Feature that connect with fields like Bandwidth and Frame,
- Histogram that intertwine with fields like Kanade–Lucas–Tomasi feature tracker and Hardware architecture,
- System on a chip, which have a strong connection to Memory bandwidth and Process.
His study in Computer hardware is interdisciplinary in nature, drawing from both Algorithm design, Embedded system and Speedup.
The concepts of his Embedded system study are interwoven with issues in Digital signal processing, Stream processing and Parallel processing.
His Memory management study combines topics in areas such as Memory architecture and Parallel computing.
Between 2010 and 2021, his most popular works were:
- Hardware-Efficient Belief Propagation (59 citations)
- Network on chip processor with multiple cores and routing method thereof (58 citations)
- Stereo-Matching Processor Using Belief Propagation (46 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Computer vision
- Programming language
His primary areas of investigation include Artificial intelligence, Computer vision, Computer hardware, Depth map and Pixel.
His Artificial intelligence course of study focuses on Speech recognition and Phonocardiogram.
His Computer vision study frequently draws connections between adjacent fields such as Real-time computing.
His Computer hardware research is multidisciplinary, incorporating elements of Voltage spike, Algorithm design, Graphics processing unit, Embedded system and Context-adaptive binary arithmetic coding.
His Depth map study combines topics from a wide range of disciplines, such as Image based, Image conversion and Enhanced Data Rates for GSM Evolution.
His work deals with themes such as Set top box, Virtual reality, Computer graphics and View synthesis, which intersect with Pixel.
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