Results 151 to 160 of about 6,322 (191)

Comparison of mass spectrometry and fourier transform infrared spectroscopy of plasma samples in identification of patients with fracture-related infections. [PDF]

PLoS One
Malek S, Natoli RM, Rajwa B.
europepmc   +1 more source

Label-free nanoscopy of cell metabolism by ultrasensitive reweighted visible stimulated Raman scattering. [PDF]

Nat Methods
Lin H, Seitz S, Tan Y, Lugagne JB, Wang L, Ding G, He H, Rauwolf TJ, Dunlop MJ, Connor JH, Porco JA, Tian L, Cheng JX.   +12 more
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Hybrid brain tumor classification of histopathology hyperspectral images by linear unmixing and an ensemble of deep neural networks. [PDF]

Healthc Technol Lett
Cruz-Guerrero IA, Campos-Delgado DU, Mejía-Rodríguez AR, Leon R, Ortega S, Fabelo H, Camacho R, Plaza ML, Callico G.   +8 more
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Experimental method to assess depth sensing limits of inelastic scattering measurements using spatial-offset Raman spectroscopy imaging. [PDF]

J Biomed Opt
Tavera H, Sheehy G, Orsini P, Bismuth J, de Denus-Baillargeon MM, Massabki M, Masson JF, Leblond F.   +7 more
europepmc   +1 more source

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Nonlocal Tensor-Based Sparse Hyperspectral Unmixing

IEEE Transactions on Geoscience and Remote Sensing, 2021
Sparse unmixing is an important technique for analyzing and processing hyperspectral images (HSIs). Simultaneously exploiting spatial correlation and sparsity improves substantially abundance estimation accuracy. In this article, we propose to exploit nonlocal spatial information in the HSI for the sparse unmixing problem.
Jie Huang, Ting-Zhu Huang, Xi-Le Zhao, Liang-Jian Deng   +3 more
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Sparse distributed hyperspectral unmixing

2016 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), 2016
Blind hyperspectral unmixing is the task of jointly estimating the spectral signatures of material in a hyperspectral images and their abundances at each pixel. The size of hyperspectral images are usually very large, which may raise difficulties for classical optimization algorithms, due to limited memory of the hardware used.
Jakob Sigurdsson, Magnus O. Ulfarsson, Johannes R. Sveinsson, Jose M. Bioucas-Dias   +3 more
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Unmixing sparse hyperspectral mixtures

2009 IEEE International Geoscience and Remote Sensing Symposium, 2009
Finding an accurate sparse approximation of a spectral vector described by a linear model, when there is available a library of possible constituent signals (called endmembers or atoms), is a hard combinatorial problem which, as in other areas, has been increasingly addressed. This paper studies the efficiency of the sparse regression techniques in the
Marian-Daniel Iordache, Jose Bioucas-Dias, Antonio Plaza   +2 more
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Sparse unmixing with adaptive background

2017 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), 2017
We propose a new hyperspectral sparse unmixing method under the assumption of the availability of a spectral library. Hyperspectral signals inevitably possess non-linearity or distortion caused by the presence of endmembers outside of the collection, inaccurate measurement of atmosphere, and endmember mismatches.
Yuki Itoh, Mario Parente
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