Interim Report to the Commission Internationale de l'Eclairage, Zurich, 1955, on the National Physical Laboratory's Investigation of Colour-matching (1955)

Stiles, Walter; Burch, J. M.

doi:10.1080/713821039

Cited by 155 publications

(72 citation statements)

References 6 publications

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“…We apply our method to two sets of classical individual color matching function data sets provided by the work of Stiles and Burch [34,35]: the 2 degree Color Matching Functions (1955) [36] and the 10 degree Color Matching Functions (1959). Measured CMFs are plotted in Figures 3 and 4.…”

Section: Resultsmentioning

confidence: 99%

Estimating individual cone fundamentals from their color-matching functions

2016

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Estimation of individual spectral cone fundamentals from color matching functions is a classical and longstanding problem in color science. In this paper we propose a novel method to carry out this estimation based on a linear optimization technique, employing an assumption of a priori knowledge of the retinal absorptance functions. The result is an estimation of the combined lenticular and macular filtration for an individual, along with the nine coefficients in the linear combination that relates their color matching functions to their estimated spectral cone fundamentals. We test the method on the individual Stiles and Burch color matching functions and derive cone fundamental estimations for different viewing fields and matching experiment repetition. We obtain cone fundamental estimations that are remarkably similar to those available in literature. This suggests that the method yields results that are close to the true fundamentals.

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Section: Resultsmentioning

confidence: 99%

Estimating individual cone fundamentals from their color-matching functions

2016

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“…We also generate pseudocolour imagery from these images making use of the colour matching functions in [40]. Note that the application of the colour matching functions is a linear operation whereby the resulting colour image is equivalent to a RAW image captured by digital colour cameras without further processing.…”

Section: Methodsmentioning

confidence: 99%

Segmentation and Estimation of Spatially Varying Illumination

Huynh

Robles-Kelly

2014

IEEE Trans. on Image Process.

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In this paper, we present an unsupervised method for segmenting the illuminant regions and estimating the illumination power spectrum from a single image of a scene lit by multiple light sources. Here, illuminant region segmentation is cast as a probabilistic clustering problem in the image spectral radiance space. We formulate the problem in an optimisation setting which aims to maximise the likelihood of the image radiance with respect to a mixture model while enforcing a spatial smoothness constraint on the illuminant spectrum. We initialise the sample pixel set under each illuminant via a projection of the image radiance spectra onto a low-dimensional subspace spanned by a randomly chosen subset of spectra. Subsequently, we optimise the objective function in a coordinateascent manner by updating the weights of the mixture components, the sample pixel set under each illuminant and the illuminant posterior probabilities. We then estimate the illuminant power spectrum per pixel making use of these posterior probabilities. We compare our method with a number of alternatives for the tasks of illumination region segmentation, illumination colour estimation and colour correction. Our experiments show the effectiveness of our method as applied to one hyperspectral and three trichromatic image datasets.

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“…The top row shows images rendered with all ground truth values whereas the bottom row shows images rendered with our estimated parameters. It is worth noting that, in the figures, we show pseudocolour imagery obtained making use of the CIE Colour Matching functions as proposed by Stiles and Burch [46].…”

Section: Methodsmentioning

confidence: 99%

An optimisation approach to the recovery of reflection parameters from a single hyperspectral image

Rahman

Robles-Kelly

2013

Computer Vision and Image Understanding

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In this paper, we present a method to recover the parameters governing the reflection of light from a surface making use of a single hyperspectral image. To do this, we view the image radiance as a combination of specular and diffuse reflection components and present a cost functional which can be used for purposes of iterative least squares optimisation. This optimisation process is quite general in nature and can be applied to a number of reflectance models widely used in the computer vision and graphics communities. We elaborate on the use of these models in our optimisation process and provide a variant of the Beckmann-Kirchhoff model which incorporates the Fresnel reflection term. We show results on synthetic images and illustrate how the recovered photometric parameters can be employed for skin recognition in real world imagery, where our estimated albedo yields a classification rate of 95.09 ± 4.26% as compared to an alternative, whose classification rate is of 90.94 ± 6.12%. We also show quantitative results on the estimation of the index of refraction, where our method delivers an average per-pixel angular error of 0.15 degrees. This is a considerable improvement with respect to an alternative, which yields an error of 9.9 degrees.

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Interim Report to the Commission Internationale de l'Eclairage, Zurich, 1955, on the National Physical Laboratory's Investigation of Colour-matching (1955)

Cited by 155 publications

References 6 publications

Estimating individual cone fundamentals from their color-matching functions

Estimating individual cone fundamentals from their color-matching functions

Segmentation and Estimation of Spatially Varying Illumination

An optimisation approach to the recovery of reflection parameters from a single hyperspectral image

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