MRI brain tumor segmentation based on texture features and kernel sparse coding
An automatic brain tumor segmentation method based on texture feature and kernel sparse
coding from FLAIR (fluid attenuated inversion recovery) contrast-enhanced MRIs (magnetic …
coding from FLAIR (fluid attenuated inversion recovery) contrast-enhanced MRIs (magnetic …
Blind spectral unmixing based on sparse component analysis for hyperspectral remote sensing imagery
Recently, many blind source separation (BSS)-based techniques have been applied to
hyperspectral unmixing. In this paper, a new blind spectral unmixing method based on …
hyperspectral unmixing. In this paper, a new blind spectral unmixing method based on …
Underdetermined blind source separation for linear instantaneous mixing system in the non-cooperative wireless communication
W Cui, S Guo, L Ren, Y Yu - Physical Communication, 2021 - Elsevier
Under the condition of non-cooperative wireless communication, many signals always
overlap in time–frequencyfield, therefore, the signal separation and reconstruction of the …
overlap in time–frequencyfield, therefore, the signal separation and reconstruction of the …
Multiple kernel sparse representations for supervised and unsupervised learning
In complex visual recognition tasks, it is typical to adopt multiple descriptors, which describe
different aspects of the images, for obtaining an improved recognition performance …
different aspects of the images, for obtaining an improved recognition performance …
Elastic functional coding of Riemannian trajectories
Visual observations of dynamic phenomena, such as human actions, are often represented
as sequences of smoothly-varying features. In cases where the feature spaces can be …
as sequences of smoothly-varying features. In cases where the feature spaces can be …
An effective two-stage clustering method for mixing matrix estimation in instantaneous underdetermined blind source separation and its application in fault diagnosis
J Wang, X Chen, H Zhao, Y Li, D Yu - Ieee Access, 2021 - ieeexplore.ieee.org
The underdetermined blind source separation (UBSS) has been considered to be a novel
signal processing technique, which can separate the fault source signals from their mixtures …
signal processing technique, which can separate the fault source signals from their mixtures …
Mixing matrix estimation using discriminative clustering for blind source separation
Mixing matrix estimation in instantaneous blind source separation (BSS) can be performed
by exploiting the sparsity and disjoint orthogonality of source signals. As a result …
by exploiting the sparsity and disjoint orthogonality of source signals. As a result …
Ensemble sparse models for image analysis and restoration
Methods and systems for recovering corrupted/degraded images using approximations
obtained from an ensemble of multiple sparse models are disclosed. Sparse models may …
obtained from an ensemble of multiple sparse models are disclosed. Sparse models may …
Towards information-theoretic k-means clustering for image indexing
Information-theoretic K-means (Info-Kmeans) aims to cluster high-dimensional data, such as
images featured by the bag-of-features (BOF) model, using K-means algorithm with KL …
images featured by the bag-of-features (BOF) model, using K-means algorithm with KL …
An algorithm for underdetermined mixing matrix estimation
T Dong, Y Lei, J Yang - Neurocomputing, 2013 - Elsevier
This paper considers the problem of mixing matrix estimation in underdetermined blind
source separation (UBSS). We propose a simple and effective detection algorithm which …
source separation (UBSS). We propose a simple and effective detection algorithm which …