What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Computer vision
- Machine learning
Huazhu Fu mainly focuses on Artificial intelligence, Pattern recognition, Computer vision, Segmentation and Image segmentation.
His work on Deep learning, Feature extraction, Salient and Cluster analysis as part of general Artificial intelligence study is frequently connected to Constraint, therefore bridging the gap between diverse disciplines of science and establishing a new relationship between them.
As a part of the same scientific study, Huazhu Fu usually deals with the Pattern recognition, concentrating on RGB color model and frequently concerns with Saliency map, Feature matching and Mutual exclusion.
His Computer vision research is multidisciplinary, relying on both Representation and Identification.
The various areas that Huazhu Fu examines in his Segmentation study include Object and Object based.
His work focuses on many connections between Image segmentation and other disciplines, such as Glaucoma, that overlap with his field of interest in Fundus and Optical imaging.
His most cited work include:
- Cluster-Based Co-Saliency Detection (295 citations)
- Joint Optic Disc and Cup Segmentation Based on Multi-Label Deep Network and Polar Transformation (293 citations)
- CE-Net: Context Encoder Network for 2D Medical Image Segmentation (251 citations)
What are the main themes of his work throughout his whole career to date?
Huazhu Fu mainly investigates Artificial intelligence, Pattern recognition, Computer vision, Segmentation and Deep learning.
His Glaucoma research extends to the thematically linked field of Artificial intelligence.
His Pattern recognition study combines topics in areas such as RGB color model, Focus, Leverage and Cluster analysis.
His work in the fields of Image overlaps with other areas such as Constraint.
His Segmentation study integrates concerns from other disciplines, such as Pixel and Fundus, Optic disc, Optic cup.
His Deep learning research incorporates themes from Image processing, Robustness and Color space.
He most often published in these fields:
- Artificial intelligence (86.86%)
- Pattern recognition (44.57%)
- Computer vision (36.57%)
What were the highlights of his more recent work (between 2020-2021)?
- Artificial intelligence (86.86%)
- Segmentation (34.29%)
- Pattern recognition (44.57%)
In recent papers he was focusing on the following fields of study:
His main research concerns Artificial intelligence, Segmentation, Pattern recognition, Deep learning and Machine learning.
His work carried out in the field of Artificial intelligence brings together such families of science as Fundus and Computer vision.
His Computer vision study incorporates themes from Salient, Code and Salience.
His Segmentation research integrates issues from Discriminative model and Similarity.
He combines subjects such as Image, Feature, Medical diagnosis and Hash function with his study of Pattern recognition.
His Image segmentation research includes elements of Retinal and Optical coherence tomography.
Between 2020 and 2021, his most popular works were:
- ASIF-Net: Attention Steered Interweave Fusion Network for RGB-D Salient Object Detection (31 citations)
- Salient Object Detection in the Deep Learning Era: An In-depth Survey. (22 citations)
- Re-thinking Co-Salient Object Detection. (9 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Machine learning
- Computer vision
Huazhu Fu focuses on Artificial intelligence, Machine learning, Field, Deep learning and Feature extraction.
The Artificial intelligence study combines topics in areas such as Fundus and Computer vision.
His research in Computer vision intersects with topics in Salience and Code.
His work deals with themes such as Salient object detection, Robustness and Taxonomy, which intersect with Machine learning.
The study incorporates disciplines such as Depth map, Salient, Convolutional neural network and Object detection in addition to Feature extraction.
Huazhu Fu has researched Image segmentation in several fields, including Pixel, Optical coherence tomography and Retinal.
This overview was generated by a machine learning system which analysed the scientist’s
body of work. If you have any feedback, you can
contact us
here.
