What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Computer vision
- Machine learning
Ke Gu mainly focuses on Artificial intelligence, Computer vision, Image quality, Visualization and Distortion.
His Artificial intelligence research is multidisciplinary, incorporating perspectives in Machine learning and Graphics.
The concepts of his Computer vision study are interwoven with issues in Entropy and Brightness.
His Image quality study combines topics in areas such as Similarity, Data mining, Transform coding and Feature extraction, Pattern recognition.
The Pattern recognition study combines topics in areas such as Iterative reconstruction and Semantic gap.
His Visualization research incorporates elements of Mixed reality, Virtual reality and Rendering.
His most cited work include:
- Using free energy principle for blind image quality assessment (361 citations)
- The Analysis of Image Contrast: From Quality Assessment to Automatic Enhancement (211 citations)
- No-Reference Image Sharpness Assessment in Autoregressive Parameter Space (179 citations)
What are the main themes of his work throughout his whole career to date?
His primary areas of study are Artificial intelligence, Image quality, Computer vision, Pattern recognition and Distortion.
Visualization, Human visual system model, Feature extraction, Image and Histogram are the subjects of his Artificial intelligence studies.
He has included themes like Artificial neural network and Normalization in his Feature extraction study.
The Image quality study which covers Transform coding that intersects with JPEG.
His Computer vision study frequently draws connections between adjacent fields such as Entropy.
In the field of Pattern recognition, his study on Sparse approximation overlaps with subjects such as Scene statistics.
He most often published in these fields:
- Artificial intelligence (82.95%)
- Image quality (65.34%)
- Computer vision (60.23%)
What were the highlights of his more recent work (between 2020-2021)?
- Artificial intelligence (82.95%)
- Computer vision (60.23%)
- Image quality (65.34%)
In recent papers he was focusing on the following fields of study:
His primary areas of investigation include Artificial intelligence, Computer vision, Image quality, Distortion and Pattern recognition.
His work carried out in the field of Artificial intelligence brings together such families of science as Data modeling and Soot.
His work on Retargeting as part of general Computer vision research is often related to Depth perception and Underwater acoustic communication, thus linking different fields of science.
His Image quality research is multidisciplinary, relying on both Feature extraction, Colorfulness and Dynamic range.
The various areas that he examines in his Feature extraction study include Ensemble learning, Visible spectrum, Sonar, Robustness and Penetration depth.
Ke Gu has researched Pattern recognition in several fields, including Range, Aggregate and Air quality index.
Between 2020 and 2021, his most popular works were:
- Ensemble Meta-Learning for Few-Shot Soot Density Recognition (10 citations)
- Dynamic Backlight Scaling Considering Ambient Luminance for Mobile Videos on LCD Displays (1 citations)
- Semi-Reference Sonar Image Quality Assessment Based on Task and Visual Perception (1 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Computer vision
- Machine learning
His primary scientific interests are in Artificial intelligence, Backlight scaling, Computer graphics, Liquid-crystal display and Luminance.
The study incorporates disciplines such as Visible spectrum and Computer vision in addition to Artificial intelligence.
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