What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Computer vision
- Algorithm
His primary scientific interests are in Artificial intelligence, Computer vision, Algorithm, Motion estimation and Image processing.
His work in Information hiding, Data compression, Halftone, Pixel and Digital watermarking are all subfields of Artificial intelligence research.
Many of his studies involve connections with topics such as Frame and Computer vision.
His Algorithm research is multidisciplinary, incorporating elements of Function and Encoder.
His Motion estimation research is multidisciplinary, incorporating perspectives in Computer hardware, Motion compensation, Motion vector and Parallel computing.
His Image processing research integrates issues from Computational complexity theory, Scale-space segmentation, Cluster analysis and Human visual system model.
His most cited work include:
- Predictive motion vector field adaptive search technique (PMVFAST): enhancing block-based motion estimation (230 citations)
- Highly efficient predictive zonal algorithms for fast block-matching motion estimation (187 citations)
- Data hiding watermarking for halftone images (182 citations)
What are the main themes of his work throughout his whole career to date?
Artificial intelligence, Computer vision, Algorithm, Motion estimation and Pixel are his primary areas of study.
His work on Artificial intelligence is being expanded to include thematically relevant topics such as Pattern recognition.
His work in Computer vision covers topics such as Interpolation which are related to areas like Demosaicing.
His research in Algorithm focuses on subjects like Real-time computing, which are connected to Wireless.
The Motion estimation study combines topics in areas such as Motion vector, Block-matching algorithm and Search algorithm.
His Pixel research incorporates elements of Distortion, Filter and Chrominance.
He most often published in these fields:
- Artificial intelligence (59.92%)
- Computer vision (55.34%)
- Algorithm (31.68%)
What were the highlights of his more recent work (between 2013-2021)?
- Artificial intelligence (59.92%)
- Computer vision (55.34%)
- Wireless (6.49%)
In recent papers he was focusing on the following fields of study:
His primary areas of study are Artificial intelligence, Computer vision, Wireless, Communication channel and Object.
His studies in Artificial intelligence integrate themes in fields like Algorithm and Pattern recognition.
Oscar C. Au has researched Algorithm in several fields, including Signal, Coding and Discrete cosine transform.
The concepts of his Computer vision study are interwoven with issues in Distortion and Interpolation.
Oscar C. Au has included themes like Transmitter, Computer hardware and Real-time computing in his Wireless study.
His Image compression research includes elements of Data compression and Encryption.
Between 2013 and 2021, his most popular works were:
- Multiresolution Graph Fourier Transform for Compression of Piecewise Smooth Images (158 citations)
- Designing an Efficient Image Encryption-Then-Compression System via Prediction Error Clustering and Random Permutation (108 citations)
- Secure Reversible Image Data Hiding Over Encrypted Domain via Key Modulation (104 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Algorithm
- Computer vision
Artificial intelligence, Computer vision, Algorithm, Pixel and Wireless are his primary areas of study.
Artificial intelligence connects with themes related to Pattern recognition in his study.
His studies deal with areas such as Encryption and Interpolation as well as Computer vision.
His Algorithm research is multidisciplinary, incorporating elements of Coding, Theoretical computer science and Discrete cosine transform.
The Theoretical computer science study which covers Digital watermarking that intersects with Information hiding.
His work in Pixel addresses subjects such as Non-local means, which are connected to disciplines such as Laplacian matrix and Graph.
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