2006 - Fellow of the International Association for Pattern Recognition (IAPR) For contributions to biometrics technologies and systems.
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.
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.
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.
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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Two-dimensional PCA: a new approach to appearance-based face representation and recognition
Jian Yang;D. Zhang;A.F. Frangi;Jing-yu Yang.
IEEE Transactions on Pattern Analysis and Machine Intelligence (2004)
FSIM: A Feature Similarity Index for Image Quality Assessment
Lin Zhang;Lei Zhang;Xuanqin Mou;D. Zhang.
IEEE Transactions on Image Processing (2011)
A Completed Modeling of Local Binary Pattern Operator for Texture Classification
Zhenhua Guo;Lei Zhang;David Zhang.
IEEE Transactions on Image Processing (2010)
Online palmprint identification
D. Zhang;Wai-Kin Kong;J. You;M. Wong.
IEEE Transactions on Pattern Analysis and Machine Intelligence (2003)
Progressive switching median filter for the removal of impulse noise from highly corrupted images
Zhou Wang;D. Zhang.
IEEE Transactions on Circuits and Systems Ii: Analog and Digital Signal Processing (1999)
The Visual Object Tracking VOT2017 Challenge Results
Matej Kristan;Ales Leonardis;Jiri Matas;Michael Felsberg.
international conference on computer vision (2017)
Fisher Discrimination Dictionary Learning for sparse representation
Meng Yang;Lei Zhang;Xiangchu Feng;David Zhang.
international conference on computer vision (2011)
KPCA plus LDA: a complete kernel Fisher discriminant framework for feature extraction and recognition
Jian Yang;A.F. Frangi;Jing-Yu Yang;David Zhang.
IEEE Transactions on Pattern Analysis and Machine Intelligence (2005)
Secure program execution via dynamic information flow tracking
G. Edward Suh;Jae W. Lee;David Zhang;Srinivas Devadas.
architectural support for programming languages and operating systems (2004)
Rotation invariant texture classification using LBP variance (LBPV) with global matching
Zhenhua Guo;Lei Zhang;David Zhang.
Pattern Recognition (2010)
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