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

Ghalia Hemrit, Futa Matsushita, Mihiro Uchida, Javier Vasqez-Corral, Han Gong, Norimichi Tsumura, Graham Finlayson

Research output: Chapter in Book/Report/Conference proceedingChapter

1 Citation (Scopus)

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 × 3 correction matrix using the least squares linear regression method when the images are not registered.
Original languageEnglish
Title of host publicationComputational Color Imaging
Subtitle of host publication7th International Workshop, CCIW 2019, Chiba, Japan, March 27-29, 2019, Proceedings
EditorsShoji Tominaga, Raimondo Schettini, Alain Trémeau, Takahiko Horiuchi
PublisherSpringer
Pages121-133
ISBN (Electronic)978-3-030-13940-7
ISBN (Print)978-3-030-13939-1
DOIs
Publication statusPublished - 20 Feb 2019

Publication series

Name Lecture Notes in Computer Science
Volume11418
  • Finding a Colour Filter to Make a Camera Colorimetric by Optimisation

    Finlayson, G. & Zhu, Y., 20 Feb 2019, Computational Color Imaging: 7th International Workshop, CCIW 2019, Chiba, Japan, March 27-29, 2019, Proceedings. Tominaga, S., Schettini, R., Tremeau, A. & Horiuchi, T. (eds.). Springer, p. 53-62 (Lectures Notes in Computer Science; vol. 11418).

    Research output: Chapter in Book/Report/Conference proceedingChapter

    5 Citations (Scopus)
  • Physically Plausible Dehazing for Non-physical Dehazing Algorithms

    Vasqez-Corral, J., Finlayson, G. & Bertalmio, M., 20 Feb 2019, Computational Color Imaging: 7th International Workshop, CCIW 2019, Chiba, Japan, March 27-29, 2019, Proceedings. Tominaga, S., Schettini, R., Trémeau, A. & Horiuchi, T. (eds.). Springer, p. 233-244 ( Lecture Notes in Computer Science; vol. 11418).

    Research output: Chapter in Book/Report/Conference proceedingChapter

    1 Citation (Scopus)

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