Zobrazeno 1 - 10
of 29
pro vyhledávání: '"Cuiming Zou"'
Publikováno v:
IEEE Access, Vol 7, Pp 154131-154140 (2019)
Linear regression has shown an effective tool for face recognition in recent years. Most existing linear regression based methods are devised for grayscale image based face recognition and fail to exploit the color information of color face images. T
Externí odkaz:
https://doaj.org/article/60db762e65524f3b92ee3b2b49d4220b
Publikováno v:
IEEE Transactions on Cybernetics. 52:2675-2686
This article presents a generalized collaborative representation-based classification (GCRC) framework, which includes many existing representation-based classification (RC) methods, such as collaborative RC (CRC) and sparse RC (SRC) as special cases
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::b979e847e8f51f0ed3535aa2509a978c
https://doi.org/10.1109/tcyb.2020.3021712
https://doi.org/10.1109/tcyb.2020.3021712
Publikováno v:
Pattern Recognition. 142:109653
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::a4473e3f98700b5fb6f4398d6f07d1e7
https://doi.org/10.1016/j.patcog.2023.109653
https://doi.org/10.1016/j.patcog.2023.109653
Publikováno v:
Signal Processing. 210:109097
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::245e3e10e883ffc577033f648cd1b8a8
https://doi.org/10.1016/j.sigpro.2023.109097
https://doi.org/10.1016/j.sigpro.2023.109097
Publikováno v:
Neurocomputing. 372:73-83
Greedy algorithm (GA) is an efficient sparse representation framework with numerous applications in machine learning and computer vision. However, conventional GA methods may fail when applied to grossly corrupted data because they iteratively estima
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::9ee7b1b119d32715e00d4bc8dc534559
https://doi.org/10.1016/j.neucom.2019.09.056
https://doi.org/10.1016/j.neucom.2019.09.056
Publikováno v:
Signal Processing. 164:284-294
Greedy algorithms have attracted considerable interest for sparse signal recovery (SSR) due to their appealing efficiency and performance recently. However, conventional greedy algorithms utilize the l2 norm based loss function and suffer from severe
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::5ca6b7037bb68d470a3e71d59ae324a5
https://doi.org/10.1016/j.sigpro.2019.06.006
https://doi.org/10.1016/j.sigpro.2019.06.006
Publikováno v:
International Journal of Wavelets, Multiresolution and Information Processing. 19
In this paper, the uncertainty principle of discrete signals associated with Quaternion Fourier transform is investigated. It suggests how sparsity helps in the recovery of missing frequency.
18 pages, 3 figures
18 pages, 3 figures
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::ddab7b4f25fbdd6a99ef4454cb279fb8
https://doi.org/10.1142/s0219691321500193
https://doi.org/10.1142/s0219691321500193
Publikováno v:
IEEE transactions on image processing : a publication of the IEEE Signal Processing Society. 30
Sparse representation has achieved great success across various fields including signal processing, machine learning and computer vision. However, most existing sparse representation methods are confined to the real valued data. This largely limit th
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::1be38b731d059ae8a4d3be39f563501f
https://pubmed.ncbi.nlm.nih.gov/33705312
https://pubmed.ncbi.nlm.nih.gov/33705312
Autor:
Kit Ian Kou, Cuiming Zou
Publikováno v:
Mechanical Systems and Signal Processing. 104:279-289
Signal recovery is one of the most important problem in signal processing. This paper proposes a novel signal recovery method based on prolate spherical wave functions (PSWFs). PSWFs are a kind of special functions, which have been proved having good
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::148d151e42b1d690c38cedf81b512ab8
https://doi.org/10.1016/j.ymssp.2017.10.025
https://doi.org/10.1016/j.ymssp.2017.10.025
Publikováno v:
Mathematical Methods in the Applied Sciences. 40:3892-3900
The classical uncertainty principle of harmonic analysis states that a nontrivial function and its Fourier transform cannot both be sharply localized. It plays an important role in signal processing and physics. This paper generalizes the uncertainty
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::eab1d88d20d96b8ec569588c36aa6601
https://doi.org/10.1002/mma.4271
https://doi.org/10.1002/mma.4271