Search results for "Separation algorithm"
showing 5 items of 5 documents
Branch-and-Cut
2010
This chapter focuses on the approach for solving the LOP to optimality which can currently be seen as the most successful one. It is a branch-and-bound algorithm, where the upper bounds are computed using linear programming relax- ations.
Analysis of human skin hyper-spectral images by non-negative matrix factorization
2011
International audience; This article presents the use of Non-negative Matrix Factorization, a blind source separation algorithm, for the decomposition of human skin absorption spectra in its main pigments: melanin and hemoglobin. The evaluated spectra come from a Hyper-Spectral Image, which is the result of the processing of a Multi-Spectral Image by a neural network-based algorithm. The implemented source separation algorithm is based on a multiplicative coeffi cient upload. The goal is to represent a given spectrum as the weighted sum of two spectral components. The resulting weighted coefficients are used to quantify melanin and hemoglobin content in the given spectra. Results present a …
Quantification of melanin and hemoglobin in humain skin from multispectral image acquisition: use of a neuronal network combined to a non-negative ma…
2012
International audience; This article presents a multispectral imaging system which, coupled with a neural network-based algorithm, reconstructs reflectance cubes. The reflectance spectra are obtained using artificial neural-netwok reconstruction which generates reflectance cubes from acquired multispectral images. Then, a blind source separation algorithm based on Non-negative Matrix Factorization is used for the decomposition of human skin absorption spectra in its main pigments: melanin and hemoglobin. The analysis is performed on reflectance spectra. The implemented source separation algorithm is based on a multiplicative coefficient upload. The goal is to represent a given spectrum as t…
Land surface emissivity retrieval from different VNIR and TIR sensors
2008
This paper discusses the application and adaptation of two existing operational algorithms for land surface emissivity (epsiv) retrieval from different operational satellite/airborne sensors with bands in the visible and near-infrared (VNIR) and thermal IR (TIR) regions: (1) the temperature and emissivity separation algorithm, which retrieves epsiv only from TIR data and (2) the normalized-difference vegetation index thresholds method, in which epsiv is retrieved from VNIR data.
The Mistigri Thermal Infrared Project: Scientific Objectives And Mission Specifications
2013
ISI Document Delivery No.: 147NI Times Cited: 4 Cited Reference Count: 117 Cited References: Abrams M, 2000, INT J REMOTE SENS, V21, P847, DOI 10.1080/014311600210326 Agam N, 2007, J GEOPHYS RES-ATMOS, V112, DOI 10.1029/2007JD008407 Allen RG, 2007, J IRRIG DRAIN E-ASCE, V133, P380, DOI 10.1061/(ASCE)0733-9437(2007)133:4(380) Alletto L, 2006, CHEMOSPHERE, V64, P1053, DOI 10.1016/j.chemosphere.2005.12.004 Arnfield AJ, 2003, INT J CLIMATOL, V23, P1, DOI 10.1002/joc.859 Baschek B., 2010, 2010 OC SCI M PORTL Bastiaanssen WGM, 1998, J HYDROL, V212, P198, DOI 10.1016/S0022-1694(98)00253-4 Bastiaanssen WGM, 2005, J IRRIG DRAIN E-ASCE, V131, P85, DOI 10.1061/(ASCE)0733-9437(2005)131:1(85) Beck LR, 2…