What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Computer vision
- Computer network
C.-C. Jay Kuo spends much of his time researching Artificial intelligence, Computer vision, Algorithm, Pattern recognition and Speech recognition.
Artificial intelligence is frequently linked to Video quality in his study.
C.-C. Jay Kuo interconnects Coding, Orthographic projection and Surface in the investigation of issues within Computer vision.
The Algorithm study which covers MNIST database that intersects with Subspace topology and Backpropagation.
His Pattern recognition study deals with Boundary intersecting with Inpainting.
His work in Speech recognition addresses subjects such as Audio signal, which are connected to disciplines such as Audio mining.
His most cited work include:
- Perceptual visual quality metrics: A survey (706 citations)
- Image database TID2013 (516 citations)
- Audio content analysis for online audiovisual data segmentation and classification (401 citations)
What are the main themes of his work throughout his whole career to date?
His primary scientific interests are in Artificial intelligence, Computer vision, Algorithm, Pattern recognition and Computer network.
The study of Artificial intelligence is intertwined with the study of Machine learning in a number of ways.
His Image processing, Motion estimation, Motion compensation, Block-matching algorithm and Image compression investigations are all subjects of Computer vision research.
His Algorithm research is multidisciplinary, relying on both Coding, Wavelet, Theoretical computer science and Distortion.
His studies deal with areas such as Image, Feature and Cluster analysis as well as Pattern recognition.
The Computer network study combines topics in areas such as Wireless and Real-time computing.
He most often published in these fields:
- Artificial intelligence (47.73%)
- Computer vision (27.27%)
- Algorithm (20.66%)
What were the highlights of his more recent work (between 2015-2021)?
- Artificial intelligence (47.73%)
- Pattern recognition (18.18%)
- Computer vision (27.27%)
In recent papers he was focusing on the following fields of study:
C.-C. Jay Kuo focuses on Artificial intelligence, Pattern recognition, Computer vision, Convolutional neural network and Machine learning.
His is involved in several facets of Artificial intelligence study, as is seen by his studies on Segmentation, Feature extraction, Image, Subspace topology and Deep learning.
In his study, which falls under the umbrella issue of Subspace topology, Coding is strongly linked to Algorithm.
His studies in Pattern recognition integrate themes in fields like Visualization and Feature.
His work deals with themes such as Codec, Distortion and Video quality, which intersect with Computer vision.
C.-C. Jay Kuo has researched Convolutional neural network in several fields, including Artificial neural network, Backpropagation, MNIST database, Cluster analysis and Robustness.
Between 2015 and 2021, his most popular works were:
- Understanding convolutional neural networks with a mathematical model (145 citations)
- Contextual-Based Image Inpainting: Infer, Match, and Translate (91 citations)
- Image Splicing Localization using a Multi-task Fully Convolutional Network (MFCN) (89 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Computer vision
- Computer network
C.-C. Jay Kuo mostly deals with Artificial intelligence, Pattern recognition, Computer vision, Convolutional neural network and Segmentation.
His research on Artificial intelligence frequently links to adjacent areas such as Machine learning.
In his research on the topic of Pattern recognition, Focus and Process is strongly related with Boundary.
His Computer vision study integrates concerns from other disciplines, such as Codec, Coding, Video quality and Masking.
His Convolutional neural network research is multidisciplinary, incorporating elements of Backpropagation, MNIST database, Benchmark, Algorithm and Robustness.
His Segmentation course of study focuses on Pascal and Incremental learning and Contextual image classification.
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