Matching image color from different cameras

Fairchild, Mark D.; Wyble, David R.; Johnson, Garrett

doi:10.1117/12.766409

Cited by 6 publications

(9 citation statements)

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“…7 and 8 makes it clear that the camera responsivities are not at all close to exact linear transformations of the human cone responsivities. This was also verified in the characterizations described below and in the analyses of Fairchild et al [36]. They are also not linear transformations of one another.…”

Section: Pseudo-color: Camera Spectral Responsessupporting

confidence: 62%

“…7. sensitivity functions to the CIE XYZ color-matching functions [36]. This method of characterization provides the best results for the monochromatic colors evaluated in this paper.…”

Section: Color Errors In Typical Professional Camerasmentioning

confidence: 90%

“…8 illustrates comparable data for a Canon 5D ("mark 1"). The spectral sensitivities of both cameras were measured using a carefully calibrated monochromatic and spectroradiometer system [36]. Simple visual inspection of Figs.…”

Section: Pseudo-color: Camera Spectral Responsesmentioning

confidence: 99%

“…This method of characterization provides the best results for the monochromatic colors evaluated in this paper. An alternative technique is to fit the matrix using a range of color samples (rather than monochromatic stimuli) such as those found in the X-Rite Color Checker Chart [36,37]. In previous work [36], it was shown that these characterizations were adequate to make images from the two cameras psychophysically indistinguishable in color.…”

Section: Color Errors In Typical Professional Camerasmentioning

confidence: 99%

“…An alternative technique is to fit the matrix using a range of color samples (rather than monochromatic stimuli) such as those found in the X-Rite Color Checker Chart [36,37]. In previous work [36], it was shown that these characterizations were adequate to make images from the two cameras psychophysically indistinguishable in color. However, the characterized images were not identical; the color differences that remained simply were not perceptible in complex images [38].…”

Section: Color Errors In Typical Professional Camerasmentioning

confidence: 99%

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Seeing, adapting to, and reproducing the appearance of nature

Fairchild

2014

Appl. Opt.

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The perception of color in nature is a complex multidimensional phenomenon. The vast range and high dimensionality of the light stimulus in a natural scene is reduced in range and dimension by the human visual system. The color experience is reduced to the appearance attributes of brightness, lightness, colorfulness, chroma, saturation, and hue from spectral energy distributions in the scene, while the vast range of light levels present in the world is reduced to a more manageable perceptual range through local adaptation. These processes set the stage for our efforts to capture, process, and reproduce the colors of nature as well as make artistic interpretations of them. This paper reviews the challenges involved in accurately capturing and reproducing optical phenomena observed in nature.

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Section: Pseudo-color: Camera Spectral Responsessupporting

confidence: 62%

“…7. sensitivity functions to the CIE XYZ color-matching functions [36]. This method of characterization provides the best results for the monochromatic colors evaluated in this paper.…”

Section: Color Errors In Typical Professional Camerasmentioning

confidence: 90%

Section: Pseudo-color: Camera Spectral Responsesmentioning

confidence: 99%

Section: Color Errors In Typical Professional Camerasmentioning

confidence: 99%

Section: Color Errors In Typical Professional Camerasmentioning

confidence: 99%

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Seeing, adapting to, and reproducing the appearance of nature

Fairchild

2014

Appl. Opt.

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Development of an image‐based measurement system for human facial skin colour

Xiao

Pointer

et al. 2021

Color Research & Application

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In this study, an image‐based measurement system was developed for human facial skin colour, involving the development of a digital imaging system, collection of facial skin colour from 60 human subjects, generation of different colour characterization models, and performance evaluation. The factors that affect facial skin colour characterization, including different training datasets (two colour charts and the collected facial skin colour dataset), mathematical mapping methods (linear transformation, polynomial regression, root‐polynomial regression and neural network) and camera image formats (JPG and RAW), were investigated and quantified not only by the conventional method of CIELAB colour difference, but also two newly introduced measures, facial colour contrast and skin colour gamut. The results indicate that the RAW image format for camera digital signals gave a more stable performance than the JPG format images, and the higher order polynomial regression with good predictive accuracy in terms of CIELAB colour difference did not perform well for the whole facial image. It is suggested to evaluate the model performance using the colour of both specific facial positions and the overall facial skin colour. Our comparative analysis in this study provides useful guidance for determining the colour characterization model for facial skin.

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