What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Computer vision
- Operating system
The scientist’s investigation covers issues in Artificial intelligence, Computer vision, Pattern recognition, Facial recognition system and Feature extraction.
His research in Artificial intelligence intersects with topics in Machine learning, Vocabulary and Speech recognition.
His study looks at the relationship between Computer vision and topics such as Algorithm, which overlap with Discrete cosine transform and Rate–distortion optimization.
His Pattern recognition research integrates issues from Feature and Visual Word.
His work in Facial recognition system addresses issues such as Gabor wavelet, which are connected to fields such as Gabor filter.
His Feature extraction study integrates concerns from other disciplines, such as Contextual image classification, Visualization, Feature and Edge detection.
His most cited work include:
- Local Gabor binary pattern histogram sequence (LGBPHS): a novel non-statistical model for face representation and recognition (911 citations)
- WLD: A Robust Local Image Descriptor (784 citations)
- The CAS-PEAL Large-Scale Chinese Face Database and Baseline Evaluations (772 citations)
What are the main themes of his work throughout his whole career to date?
His main research concerns Artificial intelligence, Computer vision, Pattern recognition, Algorithm and Coding.
He regularly ties together related areas like Machine learning in his Artificial intelligence studies.
His work in Motion estimation, Data compression, Image processing, Multiview Video Coding and Face detection are all subfields of Computer vision research.
His Multiview Video Coding research incorporates elements of Coding tree unit, Scalable Video Coding and Video compression picture types.
His research in Pattern recognition intersects with topics in Contextual image classification and Histogram.
His studies in Algorithm integrate themes in fields like Encoder, Real-time computing and Discrete cosine transform.
He most often published in these fields:
- Artificial intelligence (63.16%)
- Computer vision (42.14%)
- Pattern recognition (26.32%)
What were the highlights of his more recent work (between 2013-2021)?
- Artificial intelligence (63.16%)
- Computer vision (42.14%)
- Pattern recognition (26.32%)
In recent papers he was focusing on the following fields of study:
His primary areas of investigation include Artificial intelligence, Computer vision, Pattern recognition, Algorithm and Coding.
All of his Artificial intelligence and Data compression, Visualization, Iterative reconstruction, Image and Feature detection investigations are sub-components of the entire Artificial intelligence study.
His study in Pattern recognition is interdisciplinary in nature, drawing from both Machine learning and Visual Word.
His research integrates issues of Encoder and Real-time computing in his study of Algorithm.
His Coding research is multidisciplinary, incorporating elements of Speech recognition, Multimedia and Reference frame.
In his study, which falls under the umbrella issue of Coding tree unit, Scalable Video Coding is strongly linked to Multiview Video Coding.
Between 2013 and 2021, his most popular works were:
- Robust Estimation of 3D Human Poses from a Single Image (182 citations)
- Multi-Task Learning with Low Rank Attribute Embedding for Person Re-Identification (169 citations)
- Image Restoration Using Joint Statistical Modeling in a Space-Transform Domain (123 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Computer vision
- Operating system
Wen Gao focuses on Artificial intelligence, Pattern recognition, Computer vision, Algorithm and Visualization.
His Artificial intelligence study frequently links to other fields, such as Machine learning.
His Pattern recognition research includes themes of Regularization, Image and Visual Word.
His work deals with themes such as Real-time computing and Coding, Algorithmic efficiency, which intersect with Algorithm.
The study incorporates disciplines such as Display device, Visual search, Neural coding, Upsampling and Joint in addition to Visualization.
His Feature extraction research integrates issues from Transform coding, Noise, Convolutional neural network and Robustness.
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