What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Statistics
- Machine learning
Zheng Liu mainly focuses on Artificial intelligence, Computer vision, Image fusion, Sensor fusion and Pattern recognition.
His Artificial intelligence study frequently involves adjacent topics like Engineering drawing.
His Computer vision study combines topics from a wide range of disciplines, such as Artificial neural network and Convolutional neural network.
The concepts of his Image fusion study are interwoven with issues in Algorithm and Medical imaging.
His research integrates issues of Reliability, Asset, Systems engineering and Forensic engineering in his study of Sensor fusion.
As a part of the same scientific study, he usually deals with the Phase congruency, concentrating on Feature and frequently concerns with Pixel.
His most cited work include:
- Objective Assessment of Multiresolution Image Fusion Algorithms for Context Enhancement in Night Vision: A Comparative Study (381 citations)
- Directive Contrast Based Multimodal Medical Image Fusion in NSCT Domain (246 citations)
- Multi-sensor image fusion and its applications (217 citations)
What are the main themes of his work throughout his whole career to date?
Zheng Liu spends much of his time researching Artificial intelligence, Computer vision, Image fusion, Pattern recognition and Sensor fusion.
His Artificial intelligence research focuses on Deep learning, Feature extraction, Pixel, Image and Convolutional neural network.
His work on Computer vision deals in particular with Image processing, Feature, Feature detection, Image quality and Phase congruency.
His Image fusion research integrates issues from Night vision and Medical imaging.
In most of his Pattern recognition studies, his work intersects topics such as Image resolution.
His Sensor fusion research includes themes of Eddy-current testing, Multiresolution analysis and Systems engineering.
He most often published in these fields:
- Artificial intelligence (49.00%)
- Computer vision (29.08%)
- Image fusion (19.52%)
What were the highlights of his more recent work (between 2018-2021)?
- Artificial intelligence (49.00%)
- Deep learning (9.56%)
- Pattern recognition (15.94%)
In recent papers he was focusing on the following fields of study:
Artificial intelligence, Deep learning, Pattern recognition, Image fusion and Computer vision are his primary areas of study.
His work in the fields of Artificial intelligence, such as Feature extraction, Convolutional neural network and Image, overlaps with other areas such as Block.
The Deep learning study combines topics in areas such as Recurrent neural network, Data mining and Code.
Zheng Liu combines subjects such as Diagnosis methods and Sensor fusion with his study of Pattern recognition.
His Image fusion research is multidisciplinary, relying on both Feature, Identification, Situation awareness, Real image and Image formation.
Zheng Liu regularly links together related areas like Translation in his Computer vision studies.
Between 2018 and 2021, his most popular works were:
- Feedback Network for Image Super-Resolution (151 citations)
- Feedback Network for Image Super-Resolution (24 citations)
- A Nonlinear Regression Application via Machine Learning Techniques for Geomagnetic Data Reconstruction Processing (19 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Machine learning
- Statistics
His primary areas of study are Artificial intelligence, Noise, Deep learning, Acoustics and Data mining.
His Artificial intelligence research incorporates elements of Computer vision and Pattern recognition.
His work on Point cloud as part of his general Computer vision study is frequently connected to Parametric analysis, thereby bridging the divide between different branches of science.
His studies in Noise integrate themes in fields like Proton magnetometer, Accuracy and precision, Algorithm, Noise reduction and Principal component analysis.
His study in Deep learning is interdisciplinary in nature, drawing from both Recurrent neural network and Image.
His Image fusion study integrates concerns from other disciplines, such as Field and Convolutional neural network.
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