What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Computer vision
- Machine learning
Yuming Fang mainly focuses on Artificial intelligence, Computer vision, Pattern recognition, Feature extraction and Image quality.
His studies in Visualization, Feature, Histogram, Seam carving and Human visual system model are all subfields of Artificial intelligence research.
His biological study spans a wide range of topics, including Salient and Salience.
His Pattern recognition study integrates concerns from other disciplines, such as Image processing, Feature detection and Entropy.
The Feature extraction study combines topics in areas such as Luminance, Support vector machine and Image texture.
As a member of one scientific family, Yuming Fang mostly works in the field of Image quality, focusing on Local binary patterns and, on occasion, Normalization.
His most cited work include:
- Saliency Detection in the Compressed Domain for Adaptive Image Retargeting (260 citations)
- A Saliency Detection Model Using Low-Level Features Based on Wavelet Transform (190 citations)
- No-Reference Quality Assessment of Contrast-Distorted Images Based on Natural Scene Statistics (184 citations)
What are the main themes of his work throughout his whole career to date?
Yuming Fang focuses on Artificial intelligence, Computer vision, Pattern recognition, Image quality and Feature extraction.
His work in the fields of Artificial intelligence, such as Image, Visualization, Feature and Human visual system model, overlaps with other areas such as Distortion.
The various areas that Yuming Fang examines in his Computer vision study include Visual attention and Salience.
His Pattern recognition research includes themes of Histogram and Saliency map.
His research in Image quality intersects with topics in Transform coding, Machine learning, Data mining and Image fusion.
His work deals with themes such as Feature detection, Object detection, Kadir–Brady saliency detector and Image texture, which intersect with Feature extraction.
He most often published in these fields:
- Artificial intelligence (88.44%)
- Computer vision (60.69%)
- Pattern recognition (42.20%)
What were the highlights of his more recent work (between 2019-2021)?
- Artificial intelligence (88.44%)
- Computer vision (60.69%)
- Pattern recognition (42.20%)
In recent papers he was focusing on the following fields of study:
Artificial intelligence, Computer vision, Pattern recognition, Feature extraction and Image are his primary areas of study.
Yuming Fang carries out multidisciplinary research, doing studies in Artificial intelligence and Distortion.
His research in Computer vision is mostly concerned with View synthesis.
The concepts of his Pattern recognition study are interwoven with issues in Pixel and Feature.
His Feature extraction study combines topics in areas such as Object detection, Anomaly detection and Human visual system model.
His Image research incorporates elements of Algorithm, Compressed sensing, Chaotic and Light field.
Between 2019 and 2021, his most popular works were:
- Single Image Deraining: From Model-Based to Data-Driven and Beyond. (32 citations)
- Deep Guided Learning for Fast Multi-Exposure Image Fusion (18 citations)
- From Fidelity to Perceptual Quality: A Semi-Supervised Approach for Low-Light Image Enhancement (17 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Computer vision
- Machine learning
Yuming Fang mainly focuses on Artificial intelligence, Feature extraction, Computer vision, Pattern recognition and Visualization.
Yuming Fang incorporates Artificial intelligence and Distortion in his studies.
Yuming Fang combines subjects such as RGB color model, Object detection and Benchmark with his study of Feature extraction.
His study in Computer vision is interdisciplinary in nature, drawing from both Scrambling and Compressed sensing.
His Pattern recognition research is multidisciplinary, incorporating elements of Contrast, Noise, Image restoration and Norm.
His Visualization study combines topics from a wide range of disciplines, such as Recurrent neural network, Feature, Pyramid and Salience.
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