What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Gene
- Computer vision
His primary areas of investigation include Artificial intelligence, Pattern recognition, Computer vision, Feature extraction and Biometrics.
His Artificial intelligence research includes themes of Machine learning and Identification.
His studies in Pattern recognition integrate themes in fields like Contextual image classification and Image.
His work on Computer vision is being expanded to include thematically relevant topics such as Matching.
His Feature extraction study which covers Subspace topology that intersects with Linear subspace.
His Biometrics research includes elements of Matching, Orientation, Line and Authentication.
His most cited work include:
- Two-dimensional PCA: a new approach to appearance-based face representation and recognition (2969 citations)
- FSIM: A Feature Similarity Index for Image Quality Assessment (2515 citations)
- A Completed Modeling of Local Binary Pattern Operator for Texture Classification (1469 citations)
What are the main themes of his work throughout his whole career to date?
Artificial intelligence, Pattern recognition, Computer vision, Feature extraction and Biometrics are his primary areas of study.
His Artificial intelligence study frequently intersects with other fields, such as Machine learning.
His study on Pattern recognition is mostly dedicated to connecting different topics, such as Speech recognition.
His Tongue research extends to Computer vision, which is thematically connected.
His research on Feature extraction often connects related areas such as Contextual image classification.
His Biometrics research is multidisciplinary, incorporating perspectives in Matching, Data mining, Identification and Authentication.
He most often published in these fields:
- Artificial intelligence (60.88%)
- Pattern recognition (42.84%)
- Computer vision (23.70%)
What were the highlights of his more recent work (between 2017-2021)?
- Artificial intelligence (60.88%)
- Pattern recognition (42.84%)
- Feature extraction (21.15%)
In recent papers he was focusing on the following fields of study:
David Zhang mainly focuses on Artificial intelligence, Pattern recognition, Feature extraction, Algorithm and Discriminative model.
The study of Artificial intelligence is intertwined with the study of Computer vision in a number of ways.
His Pattern recognition study combines topics from a wide range of disciplines, such as Latent variable, Subspace topology, Minutiae and Robustness.
The concepts of his Algorithm study are interwoven with issues in Signal, Image compression and Trigonometric functions.
While the research belongs to areas of Sparse approximation, David Zhang spends his time largely on the problem of Facial recognition system, intersecting his research to questions surrounding Categorization.
His research in Biometrics intersects with topics in Identifier and Authentication.
Between 2017 and 2021, his most popular works were:
- Learning Convolutional Networks for Content-Weighted Image Compression (169 citations)
- A Trilateral Weighted Sparse Coding Scheme for Real-World Image Denoising (93 citations)
- Learning Domain-Invariant Subspace Using Domain Features and Independence Maximization. (83 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Gene
- Cancer
David Zhang focuses on Artificial intelligence, Pattern recognition, Algorithm, Feature extraction and Discriminative model.
In his study, Limit is inextricably linked to Computer vision, which falls within the broad field of Artificial intelligence.
His Pattern recognition research is multidisciplinary, incorporating elements of Feature, Representation, Contextual image classification, Norm and Robustness.
David Zhang combines subjects such as Amplitude, Image, Image compression, Waveform and Pulse with his study of Algorithm.
His work carried out in the field of Feature extraction brings together such families of science as Graph, Feature, Medical imaging, Binary number and Minutiae.
His Discriminative model research incorporates elements of Subspace topology, Regularization, Sparse matrix, Sparse approximation and Principal component analysis.
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