2012
DOI: 10.1002/cem.2444
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Chemometric approach to chromatic spatial variance. Case study: patchiness of the Skyros wall lizard

Abstract: In this paper, we demonstrate how to take advantage of the large number of spatial samples provided by commercial multispectral RGB imagers. We investigate the possibility to use various multidimensional histograms and probability distributions for decomposition and predictive models. We show how these methods can be used in an example using images of different Skyros wall lizards and demonstrate improved performance in prediction of color morph compared with traditional parameterization techniques of spatial … Show more

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Cited by 7 publications
(5 citation statements)
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References 41 publications
(49 reference statements)
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“…To capture the colour variance within each sample, the chromaticities of individual pixels were summarized in 2‐D histogram planes (Drew & Au, ; Brydegaard et al ., ; Fig. e,f).…”
Section: Methodsmentioning
confidence: 83%
See 2 more Smart Citations
“…To capture the colour variance within each sample, the chromaticities of individual pixels were summarized in 2‐D histogram planes (Drew & Au, ; Brydegaard et al ., ; Fig. e,f).…”
Section: Methodsmentioning
confidence: 83%
“…the bimodal colour distribution reflecting the presence of both green and black scales in the histograms in Fig. c,e) is preferable if the spectral region of interest is covered by the RGB imager (Brydegaard et al ., ). All lizards were photographed with a DMC FX01 Panasonic colour camera (a RGB imaging system) in an optical isolated box with a white background reference, using the built‐in xenon flash.…”
Section: Methodsmentioning
confidence: 97%
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“…This would be the case if several clusters share the same chromophores, but in different discrete concentrations [1], [2]. The four groups of damselflies (males and females of C. splendens and C. virgo) share the same melanin pigment, but are differently melanised in discrete quantities [3], [24]. The most prominent signature, however, arises from the structural phenomenon and this does not decompose linearly because the center wavelength and feature width relate to the size and ordering of the nanostructures, respectively, rather than to specific spectral transition energy.…”
Section: Unsupervised Clustering and Classificationmentioning
confidence: 99%
“…Therefore a close to a top-hat beam profile can be obtained with any radius, simply by adjusting the focal stage of the expanding telescope. This is ideal for imaging applications, e.g., for spatial chromatic variance 47 of vegetation. A test target made with light marker pens on white paper demonstrates the principle with images recorded remotely and directly at the target as shown in Fig.…”
Section: Imaging Recording Of Laser-induced Fluorescence Using An Rgbmentioning
confidence: 99%