TY - CHAP
T1 - Using the monge-kantorovitch transform in chromagenic color constancy for pathophysiology
AU - Hemrit, Ghalia
AU - Matsushita, Futa
AU - Uchida, Mihiro
AU - Vazquez-Corral, Javier
AU - Gong, Han
AU - Tsumura, Norimichi
AU - Finlayson, Graham D.
N1 - Publisher Copyright:
© Springer Nature Switzerland AG 2019.
PY - 2019
Y1 - 2019
N2 - 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.
AB - 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.
KW - Chromagenic computer vision
KW - Color constancy
UR - http://www.scopus.com/inward/record.url?scp=85064211261&partnerID=8YFLogxK
U2 - 10.1007/978-3-030-13940-7_10
DO - 10.1007/978-3-030-13940-7_10
M3 - Chapter
AN - SCOPUS:85064211261
SN - 9783030139391
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 121
EP - 133
BT - Computational Color Imaging - 7th International Workshop, CCIW 2019, Proceedings
A2 - Trémeau, Alain
A2 - Horiuchi, Takahiko
A2 - Tominaga, Shoji
A2 - Schettini, Raimondo
ER -