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Erschienen in:
Improving Generalization Ability of Deep Neural Networks for Visual Recognition Tasks Kapitel lesen Erstes Kapitel lesen

2019 | OriginalPaper | Buchkapitel

Using the Monge-Kantorovitch Transform in Chromagenic Color Constancy for Pathophysiology

verfasst von : Ghalia Hemrit, Futa Matsushita, Mihiro Uchida, Javier Vazquez-Corral, Han Gong, Norimichi Tsumura, Graham D. Finlayson

Erschienen in: Computational Color Imaging

Verlag: Springer International Publishing

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Abstract

The Chromagenic color constancy algorithm estimates the light color given two images of the same scene, one filtered and one unfiltered. The key insight underpinning the chromagenic method is that the filtered and unfiltered images are linearly related and that this linear relationship correlates strongly with the illuminant color. In the original method the best linear relationship was found based on the assumption that the filtered and unfiltered images were registered. Generally, this is not the case and implies an expensive image registration step.
This paper makes three contributions. First, we use the Monge-Kantorovich (MK) method to find the best linear transform without the need for image registration. Second, we apply this method on chromagenic pairs of facial images (used for Kampo pathophysiology diagnosis). Lastly, we show that the MK method supports better color correction compared with solving for a 3 \(\times \) 3 correction matrix using the least squares linear regression method when the images are not registered.

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Vorheriges Kapitel Spectral Estimation of Chromatically Adapted Corresponding Colors
Nächstes Kapitel Chromatic Adaptation in Colour Management
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Metadaten
Titel
Using the Monge-Kantorovitch Transform in Chromagenic Color Constancy for Pathophysiology
verfasst von
Ghalia Hemrit
Futa Matsushita
Mihiro Uchida
Javier Vazquez-Corral
Han Gong
Norimichi Tsumura
Graham D. Finlayson
Copyright-Jahr
2019
Buch
Computational Color Imaging

Print ISBN: 978-3-030-13939-1

Electronic ISBN: 978-3-030-13940-7

Copyright-Jahr: 2019

DOI
https://doi.org/10.1007/978-3-030-13940-7_10