What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Computer network
- Algorithm
His primary areas of study are Artificial intelligence, Computer vision, Computer network, Algorithm and Real-time computing.
His research combines Pattern recognition and Artificial intelligence.
Pixel, Texture compression, Image texture, Rendering and Depth map are among the areas of Computer vision where the researcher is concentrating his efforts.
His Computer network study incorporates themes from Wireless lan, Distributed computing and Bitstream.
His Algorithm research incorporates themes from Graph, Coding, Sub-band coding and Laplacian matrix.
His Real-time computing research is multidisciplinary, incorporating perspectives in Distributed source coding, Mobile client, Multiple description, Base station and Server.
His most cited work include:
- Multiresolution Graph Fourier Transform for Compression of Piecewise Smooth Images (158 citations)
- Medium streaming distribution system (147 citations)
- Bit allocation for joint source/channel coding of scalable video (126 citations)
What are the main themes of his work throughout his whole career to date?
His main research concerns Artificial intelligence, Computer vision, Algorithm, Computer network and Graph.
As a part of the same scientific family, he mostly works in the field of Artificial intelligence, focusing on Decoding methods and, on occasion, Iterative reconstruction.
His Computer vision study frequently links to related topics such as Encoder.
His study explores the link between Algorithm and topics such as Transform coding that cross with problems in JPEG.
He has included themes like Wireless network, Real-time computing, Distributed computing and Wireless WAN in his Computer network study.
His Graph research includes elements of Gradient descent and Laplacian matrix, Graph.
He most often published in these fields:
- Artificial intelligence (40.27%)
- Computer vision (33.42%)
- Algorithm (22.47%)
What were the highlights of his more recent work (between 2016-2021)?
- Artificial intelligence (40.27%)
- Graph (15.07%)
- Algorithm (22.47%)
In recent papers he was focusing on the following fields of study:
His primary areas of investigation include Artificial intelligence, Graph, Algorithm, Laplacian matrix and Computer vision.
His research is interdisciplinary, bridging the disciplines of Pattern recognition and Artificial intelligence.
In his study, which falls under the umbrella issue of Graph, Artificial neural network is strongly linked to Graph.
His Algorithm study integrates concerns from other disciplines, such as Point cloud, Signal reconstruction, Transform coding, Sampling and Gradient descent.
His biological study spans a wide range of topics, including Regularization, Differentiable function, Gershgorin circle theorem and Signed graph.
His research in Computer vision intersects with topics in Markov model and Encoding.
Between 2016 and 2021, his most popular works were:
- Graph Laplacian Regularization for Image Denoising: Analysis in the Continuous Domain (100 citations)
- Graph Spectral Image Processing (94 citations)
- Random Walk Graph Laplacian-Based Smoothness Prior for Soft Decoding of JPEG Images (86 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Computer network
- Algorithm
Gene Cheung mainly investigates Laplacian matrix, Graph, Algorithm, Artificial intelligence and Regularization.
His work in Laplacian matrix tackles topics such as Positive-definite matrix which are related to areas like Diagonal and Schur complement.
His Graph research includes themes of Gradient descent and Gershgorin circle theorem.
His Algorithm research is multidisciplinary, relying on both Point cloud, Spectral graph theory, Signal reconstruction and Graph.
Gene Cheung has researched Artificial intelligence in several fields, including Computer vision and Pattern recognition.
His Regularization research incorporates elements of Anisotropic diffusion, Non-local means, Metric space and Topology.
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