Why does the <scp>CIELAB</scp> a* axis point toward magenta instead of red?

Seymour, John

doi:10.1002/col.22547

Cited by 7 publications

(8 citation statements)

References 23 publications

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“…These are an estimate of one particular linear combination of the cone sensitivities, but the linear combination was chosen so as to minimize the amount of hand calculation that went into computing color coordinates, and not to closely emulate anything that might be in the actual visual pathway. 16,17 Normalization of XYZ values is one step in the computation of CIELAB. This step serves two purposes: (1) it forces a pure white (100% reflectance at each wavelength) to have an L* of 100 and a* and b* of 0, and (2) it provides a rough simulation of the chromatic adaptation that occurs in the human visual system.…”

Section: The Wrong Von Kries Transformmentioning

confidence: 99%

“…Equation ( 15) is the equation for ConeLab2, in matrix form, based on the regression A similar color space, named ConeLab, was previously introduced by this author. 16 ConeLab and ConeLab2 are both based on LMS. ConeLab2 differs from ConeLab in the scaling parameters.…”

Section: Description Of Conelab2mentioning

confidence: 99%

“…However, CIELAB computations are based on XYZ values, which are computed with the aid of the

\overset{x}{true}

\overset{y}{true}

, and

\overset{z}{true}

tristimulus functions. These are an estimate of one particular linear combination of the cone sensitivities, but the linear combination was chosen so as to minimize the amount of hand calculation that went into computing color coordinates, and not to closely emulate anything that might be in the actual visual pathway 16,17 …”

Section: Color Constancymentioning

confidence: 99%

“…A similar color space, named ConeLab, was previously introduced by this author 16 . ConeLab and ConeLab2 are both based on LMS .…”

Section: An Lms Variation Of Cielabmentioning

confidence: 99%

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Color inconstancy in CIELAB: A red herring?

Seymour

2022

Color Research & Application

Self Cite

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Color constancy is something we take for granted. An apple appears red despite monumental changes in the intensity and spectral balance of illumination. There are many components to the human visual system that make this illusion possible. Nonetheless, the system is imperfect. Objects will change apparent color under different illumination. The CIELAB color space allows for the computation of color values under any illuminant. CIELAB values will, in general, change as the illuminant changes. It would be natural to assume that these changes in CIELAB values would agree with the apparent color change that a human would experience. But the chromatic adaptation built into CIELAB is known to be less than optimal due to an imperfect emulation of how the adaptation process is performed in the human visual system. As a result, at least part of the illuminant color change implied by the CIELAB values is an artifact of this imperfect emulation. This paper investigates the magnitude of this artifact by comparing color changes implied by CIELAB with color changes in a color system that mimics CIELAB, except that it more closely emulates the human visual system. The difference between the two estimates of color change is assumed to be an artifact of CIELAB's poor emulation of the chromatic adaptation process. As color manufacturers are dealing with a wider spectrum of illumination sources, they may wish to consider the issue of color constancy; what colors and what formulations of colors exhibit better color constancy? Can CIELAB be used to quantify this? Or are color spaces that are based on LMS rather than XYZ more appropriate?

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Section: The Wrong Von Kries Transformmentioning

confidence: 99%

Section: Description Of Conelab2mentioning

confidence: 99%

“…However, CIELAB computations are based on XYZ values, which are computed with the aid of the

\overset{x}{true}

\overset{y}{true}

, and

\overset{z}{true}

Section: Color Constancymentioning

confidence: 99%

“…A similar color space, named ConeLab, was previously introduced by this author 16 . ConeLab and ConeLab2 are both based on LMS .…”

Section: An Lms Variation Of Cielabmentioning

confidence: 99%

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Color inconstancy in CIELAB: A red herring?

Seymour

2022

Color Research & Application

Self Cite

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show abstract

“…In CIE Lab, the luminance dimension we see in the RGB cube as a diagonal running from (0,0,0) to (1,1,1) is the vertical axis (L) of the space. The other axes are color channels A (green to red ratio) and B (yellow to blue ratio) [35]. Thanks to the above-presented properties, the A and B values can give a complete understanding of the chromatic content of a color independent of its luminance [36,37].…”

Section: Color Vision and Color Spacesmentioning

confidence: 99%

ABANICCO: A New Color Space for Multi-Label Pixel Classification and Color Segmentation

Nicolás-Sáenz¹,

Ledezma²,

Pascau³

et al. 2022

Preprint

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In any computer vision task involving color images, a necessary step is classifying pixels according to color and segmenting the respective areas. However, the development of methods able to successfully complete this task has proven challenging, mainly due to the gap between human color perception, linguistic color terms, and digital representation. In this paper, we propose a novel method combining geometric analysis of color theory, fuzzy color spaces, and multi-label systems for the automatic classification of pixels according to 12 standard color categories (Green, Yellow, Light Orange, Deep Orange, Red, Pink, Purple, Ultramarine, Blue, Teal, Brown, and Neutral). Moreover, we present a robust, unsupervised, unbiased strategy for color naming based on statistics and color theory. ABANICCO was tested against the state of the art in color classification and with the standarized ISCC-NBS color system, providing accurate classification and a standard, easily understandable alternative for hue naming recognizable by humans and machines. We expect this solution to become the base to successfully tackle a myriad of problems in all fields of computer vision, such as region characterization, histopathology analysis, fire detection, product quality prediction, object description, and hyperspectral imaging.

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Modeling the Ink Tuning Process Using Machine Learning

Costa,

Ferreira

2023

Lecture Notes in Computer Science

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Why does the CIELAB a* axis point toward magenta instead of red?

Cited by 7 publications

References 23 publications

Color inconstancy in CIELAB: A red herring?

Color inconstancy in CIELAB: A red herring?

ABANICCO: A New Color Space for Multi-Label Pixel Classification and Color Segmentation

Modeling the Ink Tuning Process Using Machine Learning

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