Gonio photometric imaging for recording of reflectance spectra of 3D objects

2002 ◽  
Author(s):  
Yoichi Miyake ◽  
Norimichi Tsumura ◽  
Hideaki Haneishi ◽  
Junichiro Hayashi
Keyword(s):  
Reflectance Spectra ◽  
3D Objects ◽  
Photometric Imaging

scholarly journals Development of goniophotometric imaging system for recording reflectance spectra of 3D objects

2001 ◽  
Author(s):  
Kazutaka Tonsho ◽  
Y Akao ◽  
Norimichi Tsumura ◽  
Yoichi Miyake
Keyword(s):  
Imaging System ◽  
Reflectance Spectra ◽  
3D Objects

Recognition of partially occluded 3D objects

1989 ◽  
Vol 136 (2) ◽  
pp. 124
Author(s):  
Ming-Hong Chan ◽  
Hung-Tat Tsui
Keyword(s):  
3D Objects ◽  
Partially Occluded

Study of vegetation red edge indices of reflectance spectra for crops of winter wheat cultivated in the steppe zone of Ukraine

2012 ◽  
Vol 18 (4(77)) ◽  
pp. 45-51
Author(s):  
G.M. Zholobak ◽  
◽  
Z.M. Shportiuk ◽  
O.N. Sibirtseva ◽  
S.S. Dugin ◽  
...  
Keyword(s):  
Winter Wheat ◽  
Steppe Zone ◽  
Reflectance Spectra ◽  
Red Edge

Color Space Transformation using Neural Networks

2019 ◽  
Vol 2019 (1) ◽  
pp. 153-158
Author(s):  
Lindsay MacDonald
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Color Space ◽  
Reflectance Spectra ◽  
Network Architectures ◽  
Color Spaces ◽  
Natural Materials ◽  
Space Transformation ◽  
Color Space Transformation

We investigated how well a multilayer neural network could implement the mapping between two trichromatic color spaces, specifically from camera R,G,B to tristimulus X,Y,Z. For training the network, a set of 800,000 synthetic reflectance spectra was generated. For testing the network, a set of 8,714 real reflectance spectra was collated from instrumental measurements on textiles, paints and natural materials. Various network architectures were tested, with both linear and sigmoidal activations. Results show that over 85% of all test samples had color errors of less than 1.0 ΔE2000 units, much more accurate than could be achieved by regression.

scholarly journals Mineral Physicochemistry Underlying Feature-Based Extraction of Mineral Abundance and Composition from Shortwave, Mid and Thermal Infrared Reflectance Spectra

Minerals ◽  
2021 ◽  
Vol 11 (4) ◽  
pp. 347
Author(s):  
Carsten Laukamp ◽  
Andrew Rodger ◽  
Monica LeGras ◽  
Heta Lampinen ◽  
Ian C. Lau ◽  
...  
Keyword(s):  
Mineral Exploration ◽  
Relevant Information ◽  
Ore Deposits ◽  
Reflectance Spectra ◽  
Full Potential ◽  
Infrared Reflectance ◽  
Vibrational Modes ◽  
Diagnostic Features ◽  
Wide Range ◽  
Mineral Assemblages

Reflectance spectroscopy allows cost-effective and rapid mineral characterisation, addressing mineral exploration and mining challenges. Shortwave (SWIR), mid (MIR) and thermal (TIR) infrared reflectance spectra are collected in a wide range of environments and scales, with instrumentation ranging from spaceborne, airborne, field and drill core sensors to IR microscopy. However, interpretation of reflectance spectra is, due to the abundance of potential vibrational modes in mineral assemblages, non-trivial and requires a thorough understanding of the potential factors contributing to the reflectance spectra. In order to close the gap between understanding mineral-diagnostic absorption features and efficient interpretation of reflectance spectra, an up-to-date overview of major vibrational modes of rock-forming minerals in the SWIR, MIR and TIR is provided. A series of scripts are proposed that allow the extraction of the relative intensity or wavelength position of single absorption and other mineral-diagnostic features. Binary discrimination diagrams can assist in rapidly evaluating mineral assemblages, and relative abundance and chemical composition of key vector minerals, in hydrothermal ore deposits. The aim of this contribution is to make geologically relevant information more easily extractable from reflectance spectra, enabling the mineral resources and geoscience communities to realise the full potential of hyperspectral sensing technologies.

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