What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Computer vision
- Computer graphics
His primary scientific interests are in Artificial intelligence, Computer vision, Computer graphics, Image processing and Pattern recognition.
In his work, Heung-Yeung Shum performs multidisciplinary research in Artificial intelligence and Set.
His is doing research in Rendering, Pixel, Image, Image-based modeling and rendering and Image restoration, both of which are found in Computer vision.
His work on 3D computer graphics and Three dimensional television as part of general Computer graphics study is frequently linked to Transformation matrix and Virtual travel, therefore connecting diverse disciplines of science.
His Image processing study also includes fields such as
- Image quality and related Deblurring,
- Motion estimation together with Motion.
His research in Pattern recognition intersects with topics in Kadir–Brady saliency detector, Object detection and Face detection.
His most cited work include:
- Learning to Detect a Salient Object (1449 citations)
- Stereo matching using belief propagation (1150 citations)
- Lazy snapping (1024 citations)
What are the main themes of his work throughout his whole career to date?
Heung-Yeung Shum mainly focuses on Artificial intelligence, Computer vision, Computer graphics, Rendering and Pattern recognition.
His Artificial intelligence study is mostly concerned with Image, Iterative reconstruction, Image texture, Image segmentation and Pixel.
His research on Computer vision often connects related areas such as Computer graphics.
His research ties Interpolation and Computer graphics together.
Heung-Yeung Shum has researched Rendering in several fields, including Concentric and Displacement mapping.
Heung-Yeung Shum interconnects Facial recognition system and Face detection in the investigation of issues within Pattern recognition.
He most often published in these fields:
- Artificial intelligence (71.46%)
- Computer vision (61.73%)
- Computer graphics (28.76%)
What were the highlights of his more recent work (between 2007-2020)?
- Artificial intelligence (71.46%)
- Computer vision (61.73%)
- Rendering (22.79%)
In recent papers he was focusing on the following fields of study:
His primary areas of investigation include Artificial intelligence, Computer vision, Rendering, Computer graphics and Information retrieval.
Heung-Yeung Shum has included themes like Natural language processing and Pattern recognition in his Artificial intelligence study.
His Image-based modeling and rendering, Image texture, Image retrieval, Image and Feature extraction investigations are all subjects of Computer vision research.
His Rendering study also includes
- Image segmentation and related Light field and Image quality,
- Depth map which connect with View synthesis and Segmentation.
His study in the field of Real-time rendering also crosses realms of Diffusion equation.
In general Information retrieval, his work in Web search query and Search engine is often linked to Set linking many areas of study.
Between 2007 and 2020, his most popular works were:
- Learning to Detect a Salient Object (1449 citations)
- Image super-resolution using gradient profile prior (784 citations)
- An Empirical Study on Learning to Rank of Tweets (244 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Computer vision
- Machine learning
His scientific interests lie mostly in Artificial intelligence, Computer vision, Information retrieval, Pattern recognition and Chatbot.
His study in Image gradient, Image restoration, Iterative reconstruction, Image texture and Image is done as part of Artificial intelligence.
His work carried out in the field of Image texture brings together such families of science as Algorithm and Texture mapping.
His research on Computer vision often connects related topics like Interpolation.
His biological study spans a wide range of topics, including Facial recognition system, Cognitive neuroscience of visual object recognition and Image retrieval.
His work deals with themes such as Markov decision process and Human–computer interaction, which intersect with Chatbot.
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