scholarly journals ARL Spectral Fitting as an Application to Augment Spectral Data via Franck-Condon Lineshape Analysis and Color Analysis

10.3791/62425 ◽  
2021 ◽  
Author(s):  
William R. Roberts ◽  
Thomas N. Rohrabaugh ◽  
Ryan M. O'Donnell
Keyword(s):  
Spectral Data ◽  
Color Analysis ◽  
Lineshape Analysis ◽  
Spectral Fitting ◽  
Franck Condon

scholarly journals The XMM-Newton spectral-fit database

2013 ◽  
Vol 9 (S304) ◽  
pp. 155-156
Author(s):  
A. Corral ◽  
I. Georgantopoulos ◽  
S. Rosen ◽  
M. G. Watson ◽  
K. Page ◽  
...  
Keyword(s):  
Spectral Data ◽  
Spectral Properties ◽  
X Ray ◽  
Spectral Fitting ◽  
Source Selection ◽  
Fundamental Goal ◽  
Data Products ◽  
Funded Project ◽  
Representative Samples

AbstractThe XMM-Newton spectral-fit database is an ongoing ESA funded project aimed to construct a catalogue of spectral-fitting results for all the sources within the XMM-Newton serendipitous source catalogue for which spectral data products have been pipeline-extracted (≳ 120,000 X-ray source detections). The fundamental goal of this project is to provide the astronomical community with a tool to construct large and representative samples of X-ray sources by allowing source selection according to spectral properties.

Non-Contact Color Analysis and Digital Archive Method for Blue-and-White Porcelain by Using Spectral Imaging Technology

2013 ◽  
Vol 7 (supplement) ◽  
pp. 189-200
Author(s):  
M. James Shyu ◽  
Margaret Chen Lee
Keyword(s):  
Spectral Data ◽  
Spectral Reflectance ◽  
Spectral Imaging ◽  
Physical Contact ◽  
Digital Archive ◽  
Color Analysis ◽  
Lighting Condition ◽  
Digital Archiving ◽  
Imaging Technology ◽  
White Porcelain

The unique colour representation of Oriental art work distinguishes itself from what has been seen in Western art work. Not only the traditional Oriental watercolour paintings but also the ancient Chinese porcelains show a great deal of interesting colours that have been archived by digital camera in digital archive practice. However, the majority of the current digital cameras operate with RGB filters, which record every pixel in a tri-chromatic manner. There are several limitations for such a tri-chromatic camera. One is that it describes the colour of the object only by three primary colours. Another is that the image captured is strongly influenced by the illumination at the scene, such that the archived image appears as it does specifically under that lighting condition, not as the original object itself. With such limitations, a problem called ‘metamerism’ may occur when an object is viewed under different illuminations. This creates a serious problem when colour is a critical factor in the digital archiving process. Spectral imaging technology is capable of capturing the colour in spectral form, and the spectral reflectance of each pixel is recorded accordingly. With consequent analysis and process, art works can be archived with high precision spectral data with the advantages of resolving the issue of metamerism and of reproducing the art work under any kind of illumination. Another advantage of spectral imaging technology is that it captures and analyzes the spectral data of an art work without any physical contact. In this study ancient Chinese blue-and-white porcelains collected by the Hwa-Kang Museum of the Chinese Culture University are digitised by spectral imaging technology. A custom-built spectral camera is used to capture the spectral image by using a new spectrograph device. Statistics analysis is used to extract the primary spectral reflectance from the captured spectral data. Comparisons can then be made to explore the differences among the colour primaries of the porcelains. This new spectral system and the colour analysis of blue-and-white porcelain as well as the resulting spectral characteristics of this porcelain can be a new benchmark for the museum community in digital archiving.

FRANCK-CONDON-LIKE PATTERNS OBSERVED IN THE INFRARED SPECTRA OF PHENOL-ALKYLSILANE DIHYDROGEN-BONDED CLUSTERS IN THE EXCITED STATES

2019 ◽  
Author(s):  
Haruki Ishikawa ◽  
Yoshiteru Matsumoto ◽  
Yasutoshi Kasahara ◽  
Takutoshi Shimizu ◽  
Masaaki Uchida
Keyword(s):  
Excited States ◽  
Infrared Spectra ◽  
Franck Condon

Spectral band Selection Using a Genetic Algorithm Based Wiener Filter Estimation Method for Reconstruction of Munsell Spectral Data

2017 ◽  
Vol 2017 (18) ◽  
pp. 190-193 ◽  
Author(s):  
Keivan Ansari ◽  
Jean-Baptiste Thomas ◽  
Pierre Gouton
Keyword(s):  
Genetic Algorithm ◽  
Spectral Data ◽  
Spectral Band ◽  
Estimation Method ◽  
Wiener Filter ◽  
Band Selection

Estimating Pigment Concentrations from Spectral Images Using an Encoder‐Decoder Neural Network

2020 ◽  
Vol 64 (3) ◽  
pp. 30502-1-30502-15
Author(s):  
Kensuke Fukumoto ◽  
Norimichi Tsumura ◽  
Roy Berns
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Absorption Coefficient ◽  
Spectral Data ◽  
High Accuracy ◽  
Pigment Concentration ◽  
Scattering Coefficient ◽  
A Value ◽  
Input And Output ◽  
Pigment Concentrations

Abstract A method is proposed to estimate the concentration of pigments mixed in a painting, using the encoder‐decoder model of neural networks. The model is trained to output a value that is the same as its input, and its middle output extracts a certain feature as compressed information about the input. In this instance, the input and output are spectral data of a painting. The model is trained with pigment concentration as the middle output. A dataset containing the scattering coefficient and absorption coefficient of each of 19 pigments was used. The Kubelka‐Munk theory was applied to the coefficients to obtain many patterns of synthetic spectral data, which were used for training. The proposed method was tested using spectral images of 33 paintings, which showed that the method estimates, with high accuracy, the concentrations that have a similar spectrum of the target pigments.

Detection of potential aircraft icing regions with GOES multi-spectral data

1997 ◽  
Author(s):  
Gary Ellrod ◽  
James Nelson, III ◽  
Gary Ellrod ◽  
James Nelson, III
Keyword(s):  
Spectral Data ◽  
Aircraft Icing
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