What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Computer vision
- Statistics
His scientific interests lie mostly in Artificial intelligence, Computer vision, Image quality, Pattern recognition and Image processing.
His research on Artificial intelligence frequently links to adjacent areas such as Metric.
While the research belongs to areas of Computer vision, he spends his time largely on the problem of Entropy, intersecting his research to questions surrounding Quality assessment.
The various areas that Weisi Lin examines in his Image quality study include Data mining, Pooling, Transform coding, Histogram and Image restoration.
His biological study spans a wide range of topics, including Image retrieval, Semantic gap, Relevance feedback and Feature detection.
His study in Image processing is interdisciplinary in nature, drawing from both Uncompressed video, Seam carving and Robustness.
His most cited work include:
- Perceptual visual quality metrics: A survey (706 citations)
- Image Quality Assessment Based on Gradient Similarity (484 citations)
- Saliency Detection in the Compressed Domain for Adaptive Image Retargeting (260 citations)
What are the main themes of his work throughout his whole career to date?
His primary areas of study are Artificial intelligence, Computer vision, Image quality, Pattern recognition and Human visual system model.
His research on Artificial intelligence frequently connects to adjacent areas such as Metric.
In Computer vision, Weisi Lin works on issues like Video quality, which are connected to Frame rate.
His Image quality study combines topics from a wide range of disciplines, such as JPEG, Data mining, Pooling, Machine learning and Image restoration.
His Pattern recognition research includes themes of Transform coding, Histogram and Feature detection.
His Human visual system model research incorporates elements of Orientation, Visual perception and Just-noticeable difference.
He most often published in these fields:
- Artificial intelligence (81.51%)
- Computer vision (57.55%)
- Image quality (33.21%)
What were the highlights of his more recent work (between 2016-2021)?
- Artificial intelligence (81.51%)
- Pattern recognition (31.51%)
- Computer vision (57.55%)
In recent papers he was focusing on the following fields of study:
Weisi Lin mainly focuses on Artificial intelligence, Pattern recognition, Computer vision, Image quality and Feature extraction.
His research in Visualization, Feature, Human visual system model, Deep learning and Pixel are components of Artificial intelligence.
His work is dedicated to discovering how Pattern recognition, Salience are connected with Visual saliency and other disciplines.
As part of one scientific family, Weisi Lin deals mainly with the area of Computer vision, narrowing it down to issues related to the Salient, and often Constraint.
Weisi Lin combines subjects such as Image processing, Transform coding, Visual perception, Machine learning and Rendering with his study of Image quality.
The study incorporates disciplines such as Normalization, Convolutional neural network, Data mining and Benchmark in addition to Feature extraction.
Between 2016 and 2021, his most popular works were:
- Learning a No-Reference Quality Assessment Model of Enhanced Images With Big Data (174 citations)
- No-Reference Quality Metric of Contrast-Distorted Images Based on Information Maximization (139 citations)
- A Fast Reliable Image Quality Predictor by Fusing Micro- and Macro-Structures (132 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Computer vision
- Statistics
His primary scientific interests are in Artificial intelligence, Computer vision, Image quality, Visualization and Pattern recognition.
His Artificial intelligence study often links to related topics such as Machine learning.
His work on Seam carving and Image processing as part of general Computer vision research is often related to Focus, thus linking different fields of science.
His Image quality research is multidisciplinary, relying on both Image warping, Data mining, Support vector machine and Metric.
His Visualization research includes elements of Similarity, Sparse approximation, Stereoscopy, Convolutional neural network and Rendering.
His Pattern recognition research integrates issues from Histogram, Luminance, Overfitting and Categorization.
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