Metamer mismatching underlies color difference sensitivity.

Color difference sensitivity as represented by the size of discrimination ellipsoids is known to depend on where the colors reside within color space. In the past, various color spaces and color difference formulas have been developed as parametric fits to the experimental data with the goal of establishing a color coordinate system in which equally discriminable colors are equal distances apart. These empirical models, however, provide no explanation as to why color discrimination varies in the way it does.

Color Sensor Accuracy Index Utilizing Metamer Mismatch Radii.

A novel method is described for evaluating the colorimetric accuracy of digital color cameras based on a new measure of the metamer mismatch body (MMB) that is induced by the change from the camera as an 'observer' to the human standard observer. In comparison to the majority of existing methods for evaluating colorimetric accuracy, the advantage of using the MMB is that it is based on the theory of metamer mismatching and, therefore, shows how much color error can arise in principle. A new measure of colorimetric accuracy based on the shape of the camera-induced MMB is proposed and tested.

Providing a Single Ground-Truth for Illuminant Estimation for the ColorChecker Dataset.

The ColorChecker dataset is one of the most widely used image sets for evaluating and ranking illuminant estimation algorithms. However, this single set of images has at least 3 different sets of ground-truth (i.e.

Computational color prediction versus least-dissimilar matching.

The performance of color prediction methods CIECAM02, KSM, Waypoint, Best Linear, Metamer Mismatch Volume Center, and Relit color signal are compared in terms of how well they explain Logvinenko and Tokunaga's asymmetric color matching results [Seeing Perceiving24, 407 (2011)]. In their experiment, four observers were asked to determine (three repeats) for a given Munsell paper under a test illuminant which of 22 other Munsell papers was the least-dissimilar under a match illuminant. Their use of "least-dissimilar" as opposed to "matching" is an important aspect of their experiment.

Metamer mismatching in practice versus theory.

Metamer mismatching (the phenomenon that two objects matching in color under one illuminant may not match under a different illuminant) potentially has important consequences for color perception. Logvinenko et al. [PLoS ONE10, e0135029 (2015)] show that in theory the extent of metamer mismatching can be very significant.

Gaussian-Based Hue Descriptors.

A robust and accurate hue descriptor that is useful in modeling human color perception and for computer vision applications is explored. The hue descriptor is based on the peak wavelength of a Gaussian-like function (called a wraparound Gaussian) and is shown to correlate as well as CIECAM02 hue to the hue designators of papers from the Munsell and Natural Color System color atlases and to the hue names found in Moroney's Color Thesaurus. The new hue descriptor is also shown to be significantly more stable under a variety of illuminants than CIECAM02.

Rethinking Colour Constancy.

Colour constancy needs to be reconsidered in light of the limits imposed by metamer mismatching. Metamer mismatching refers to the fact that two objects reflecting metameric light under one illumination may reflect non-metameric light under a second; so two objects appearing as having the same colour under one illuminant can appear as having different colours under a second. Yet since Helmholtz, object colour has generally been believed to remain relatively constant.

How metamer mismatching decreases as the number of colour mechanisms increases with implications for colour and lightness constancy.

Metamer mismatching has been previously found to impose serious limitations on colour constancy. The extent of metamer mismatching is shown here to be considerably smaller for trichromats than for dichromats, and maximal for monochromats. The implications for achromatic colour perception are discussed.

Object-color-signal prediction using wraparound Gaussian metamers.

Alexander Logvinenko introduced an object-color atlas based on idealized reflectances called rectangular metamers in 2009. For a given color signal, the atlas specifies a unique reflectance that is metameric to it under the given illuminant. The atlas is complete and illuminant invariant, but not possible to implement in practice.

Outlier modeling for spectral data reduction.

The spectra in spectral reflectance datasets tend to be quite correlated and therefore they can be represented more compactly using standard techniques such as principal components analysis (PCA) as part of a lossy compression strategy. However, the presence of outlier spectra can often increase the overall error of the reconstructed spectra. This paper introduces a new outlier modeling (OM) method that detects, clusters, and separately models outliers with their own set of basis vectors.

Metamer mismatching.

A new algorithm for calculating the metamer mismatch volumes that arise in colour vision and colour imaging is introduced. Unlike previous methods, the proposed method places no restrictions on the set of possible object reflectance spectra. As a result of such restrictions, previous methods have only been able to provide approximate solutions to the mismatch volume.

Evaluating combinational illumination estimation methods on real-world images.

Illumination estimation is an important component of color constancy and automatic white balancing. A number of methods of combining illumination estimates obtained from multiple subordinate illumination estimation methods now appear in the literature. These combinational methods aim to provide better illumination estimates by fusing the information embedded in the subordinate solutions.

Illumination estimation via thin-plate spline interpolation.

Thin-plate spline interpolation is used to interpolate the chromaticity of the color of the incident scene illumination across a training set of images. Given the image of a scene under unknown illumination, the chromaticity of the scene illumination can be found from the interpolated function. The resulting illumination-estimation method can be used to provide color constancy under changing illumination conditions and automatic white balancing for digital cameras.

A comparison of computational color constancy algorithms--part II: experiments with image data.

We test a number of the leading computational color constancy algorithms using a comprehensive set of images. These were of 33 different scenes under 11 different sources representative of common illumination conditions. The algorithms studied include two gray world methods, a version of the Retinex method, several variants of Forsyth's gamut-mapping method, Cardei et al.

A comparison of computational color constancy algorithms--part I: methodology and experiments with synthesized data.

We introduce a context for testing computational color constancy, specify our approach to the implementation of a number of the leading algorithms, and report the results of three experiments using synthesized data. Experiments using synthesized data are important because the ground truth is known, possible confounds due to camera characterization and pre-processing are absent, and various factors affecting color constancy can be efficiently investigated because they can be manipulated individually and precisely. The algorithms chosen for close study include two gray world methods, a limiting case of a version of the Retinex method, a number of variants of Forsyth's gamut-mapping method, Cardei et al.

A multilinear constraint on dichromatic planes for illumination estimation.

A new multilinear constraint on the color of the scene illuminant based on the dichromatic reflection model is proposed. The formulation avoids the problem, common to previous dichromatic methods, of having to first identify pixels corresponding to the same surface material. Once pixels from two or more materials have been identified, their corresponding dichromatic planes can be intersected to yield the illuminant color.

Estimating the scene illumination chromaticity by using a neural network.

A neural network can learn color constancy, defined here as the ability to estimate the chromaticity of a scene's overall illumination. We describe a multilayer neural network that is able to recover the illumination chromaticity given only an image of the scene. The network is previously trained by being presented with a set of images of scenes and the chromaticities of the corresponding scene illuminants.