Results 161 to 170 of about 309,259 (214)

Detection of asbestos-based cement rooftops in conflict-affected settings using EnMAP hyperspectral data: a research article. [PDF]

Sci Rep
Shepherd JE, Sagi E, Zagron G, Ben-Dor E.   +3 more
europepmc   +1 more source

Bayesian reconstruction of rapidly scanned mid-infrared optoacoustic signals enables fast, label-free chemical microscopy. [PDF]

Sci Adv
Berger C, Kim M, Scheel-Platz L, Eigenberger A, Prantl L, Liu P, Gujrati V, Ntziachristos V, Jüstel D, Pleitez MA.   +9 more
europepmc   +1 more source

The AMEE-PPI Method to Extract Typical Outcrop Endmembers from GF-5 Hyperspectral Images. [PDF]

Sensors (Basel)
Hu L, Hu J, Gan S, Yuan X, Lu Y, Zhao H, Han G.   +6 more
europepmc   +1 more source

Multiscale and recursive unmixing of spatiotemporal rhythms for live-cell and intravital cardiac microscopy. [PDF]

Nat Cardiovasc Res
Ling Z, Liu W, Yoon K, Hou J, Forghani P, Hua X, Yoon H, Bagheri M, Dasi LP, Mandracchia B, Xu C, Nie S, Jia S.   +12 more
europepmc   +1 more source

Adaptive Raman spectral unmixing method based on Voigt peak compensation for quantitative analysis of cellular biochemical components. [PDF]

Biomed Opt Express
Chen X, Tang P, Wan J, Zhang W, Zhong L.
europepmc   +1 more source

Robot-assisted mapping of chemical reaction hyperspaces and networks. [PDF]

Nature
Jia Y, Frydrych R, Sobolev YI, Wong WS, Prajapati B, Matuszczyk D, Bilgi Y, Gadina L, Ahumada JC, Moldagulov G, Kim N, Larsen ES, Deschamps M, Jiang Y, Grzybowski BA.   +14 more
europepmc   +1 more source

Antibody-Based Multiplex Image Analysis: Standard Analytical Workflows and Artificial Intelligence Tools for Pathologists. [PDF]

Lab Invest
Omar M, Fanelli GN, Socciarelli F, Ullanat V, Puchala SR, Wen J, Chowdhury A, Valencia I, Scatena C, Marchionni L, Umeton R, Loda M.   +11 more
europepmc   +1 more source

Enhancing biomedical signals through genetic algorithm optimized Exponentiated transmuted weibull denoising techniques. [PDF]

Sci Rep
Sayedelahl MA, Farouk RM, Adam AM.
europepmc   +1 more source

Collaborative Sparse Regression for Hyperspectral Unmixing [PDF]

IEEE Transactions on Geoscience and Remote Sensing, 2014
Sparse unmixing has been recently introduced in hyperspectral imaging as a framework to characterize mixed pixels. It assumes that the observed image signatures can be expressed in the form of linear combinations of a number of pure spectral signatures known in advance (e.g., spectra collected on the ground by a field spectroradiometer).
José M Bioucas-Dias, Antonio Plaza, Marian-Daniel Iordache   +2 more
exaly   +3 more sources