Zobrazeno 1 - 10
of 32
pro vyhledávání: '"Shuang Wang"'
Publikováno v:
IEEE Transactions on Cognitive and Developmental Systems. 13:368-382
The scalp electroencephalogram (EEG) has been extensively studied for epileptic signal classification in the past, but little attention has been paid to the data imbalance among different epileptic states. It is well known that, in general, the durat
Publikováno v:
IEEE Transactions on Circuits and Systems II: Express Briefs. 68:1542-1546
Seizure onset detection and epileptic preictal prediction based on electroencephalogram (EEG) signals have been a challenge problem in the research community. In this brief, a novel epileptic states classification algorithm based on the multichannel
Publikováno v:
IEEE Access, Vol 9, Pp 9845-9854 (2021)
To solve the problems of the difficult feature extraction, poor feature credibility and low recognition accuracy of coal and gangue, this paper utilizes the difference in the dielectric properties of coal and gangue and in combination with a support
Publikováno v:
IEEE Access, Vol 8, Pp 96946-96954 (2020)
Breast cancer is one of the most dangerous diseases and the second largest cause of female cancer death. Breast cancer starts when malignant, cancerous lumps start to grow from the breast cells. Self-tests and Periodic clinical checks help to early d
Publikováno v:
IET Image Processing. 13:332-343
Hyperspectral images are mixtures of spectra of materials in a scene. Accurate analysis of hyperspectral image requires spectral unmixing. The result of spectral unmixing is the material spectral signatures and their corresponding fractions. The mate
Publikováno v:
ISPRS Journal of Photogrammetry and Remote Sensing. 145:148-164
We propose an effective deep neural network aiming at remote sensing image registration problem. Unlike conventional methods doing feature extraction and feature matching separately, we pair patches from sensed and reference images, and then learn th
Autor:
Licheng Jiao, Bowu Yang, Ning Huyan, Biao Hou, Yi Li, Jocelyn Chanussot, Dou Quan, Shuang Wang
Publikováno v:
IEEE Transactions on Image Processing
IEEE Transactions on Image Processing, Institute of Electrical and Electronics Engineers, 2021, 30, pp.7127-7142. ⟨10.1109/TIP.2021.3101414⟩
IEEE Transactions on Image Processing, 2021, 30, pp.7127-7142. ⟨10.1109/TIP.2021.3101414⟩
IEEE Transactions on Image Processing, Institute of Electrical and Electronics Engineers, 2021, 30, pp.7127-7142. ⟨10.1109/TIP.2021.3101414⟩
IEEE Transactions on Image Processing, 2021, 30, pp.7127-7142. ⟨10.1109/TIP.2021.3101414⟩
International audience; Deep convolutional neural networks attract increasing attention in image patch matching. However, most of them rely on a single similarity learning model, such as feature distance and the correlation of concatenated features.
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::3ae99a4109d6c2a4f24ccf1557605861
https://hal.archives-ouvertes.fr/hal-03429620
https://hal.archives-ouvertes.fr/hal-03429620
Publikováno v:
IGARSS
In this paper, the polarimetric synthetic aperture radar (PolSAR) scene classification is solved by using a novel low -rank tensor-based multi-view subspace representation (LRT-MSR) method. PolSAR data can be described in multimodal feature spaces, s
Publikováno v:
CISP-BMEI
Aiming at the problem of inaccurate location caused by missing information after feature extraction in traditional multitask cascade convolution network, feature pyramid structure is used to strengthen the context connection of images; aiming at the
Publikováno v:
ICCV
Image patch matching across different spectral domains is more challenging than in a single spectral domain. We consider the reason is twofold: 1. the weaker discriminative feature learned by conventional methods; 2. the significant appearance differ