Color constancy in the nearly natural image 2 Achromatic loci

Brainard, David H.

doi:10.1364/josaa.15.000307

Cited by 216 publications

(199 citation statements)

References 42 publications

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“…The level of colour constancy achieved by human observers is typically less for simulated scenes than for real scenes. Brainard (1998) found that with real scenes, observers can compensate for 84% of the change in illumination (assessed via achromatic settings), while typical performance with scenes presented on computer monitors suggests only 50% compensation. Yang & Maloney (2001) took several steps to ensure that their simulated scenes were as real as possible, and their observers achieved achromatic settings that compensated for 65% of the change in illumination.…”

Section: Estimating the Illuminant (A) What Is Estimated?mentioning

confidence: 94%

“…Fairchild & Lennie 1992;Brainard 1998). Importantly for these studies, Morgan et al (2000) have shown that accuracy can be as great with an implicit as with an explicit standard.…”

Section: Cone Sensitivities S(λ) M(λ) L(λ)mentioning

confidence: 97%

“…The flat-world has an important place in colour constancy research since scenes from such a world are straightforward to simulate, manipulate and control. Recently, the visual world of colour-constancy experiments has been enriched through the use of sophisticated physics-based rendering software (Yang & Maloney 2001;Maloney 2002) and through the use of real objects and computer-controlled lighting systems Brainard 1998). …”

Section: Estimating the Illuminant (A) What Is Estimated?mentioning

confidence: 99%

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Sensory, computational and cognitive components of human colour constancy

Smithson

2005

Phil. Trans. R. Soc. B

184

139

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When the illumination on a scene changes, so do the visual signals elicited by that scene. In spite of these changes, the objects within a scene tend to remain constant in their apparent colour. We start this review by discussing the psychophysical procedures that have been used to quantify colour constancy. The transformation imposed on the visual signals by a change in illumination dictates what the visual system must 'undo' to achieve constancy. The problem is mathematically underdetermined, and can be solved only by exploiting regularities of the visual world. The last decade has seen a substantial increase in our knowledge of such regularities as technical advances have made it possible to make empirical measurements of large numbers of environmental scenes and illuminants. This review provides a taxonomy of models of human colour constancy based first on the assumptions they make about how the inverse transformation might be simplified, and second, on how the parameters of the inverse transformation might be set by elements of a complex scene. Candidate algorithms for human colour constancy are represented graphically and pictorially, and the availability and utility of an accurate estimate of the illuminant is discussed. Throughout this review, we consider both the information that is, in principle, available and empirical assessments of what information the visual system actually uses. In the final section we discuss where in our visual systems these computations might be implemented.

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Section: Estimating the Illuminant (A) What Is Estimated?mentioning

confidence: 94%

“…Fairchild & Lennie 1992;Brainard 1998). Importantly for these studies, Morgan et al (2000) have shown that accuracy can be as great with an implicit as with an explicit standard.…”

Section: Cone Sensitivities S(λ) M(λ) L(λ)mentioning

confidence: 97%

Section: Estimating the Illuminant (A) What Is Estimated?mentioning

confidence: 99%

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Sensory, computational and cognitive components of human colour constancy

Smithson

2005

Phil. Trans. R. Soc. B

184

139

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“…The human visual system is, to a certain extent, colour constant [1,4,5]; that is, it discounts the colour of the illumination. This is why, for example, snow always appears white, no matter which illuminant it is observed under.…”

Section: Introductionmentioning

confidence: 99%

Bright Chromagenic Algorithm for Illuminant Estimation

Fredembach¹,

Finlayson²

2008

jist

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In this paper, we propose a new algorithm for illuminant estimation. We begin by reviewing the concept of chromagenic colour constancy, where two pictures are taken from each scene: a normal one and one where a coloured filter is placed in front of the camera, and look at parameters known to affect its performance such as filters and sensor choice.We show that the basic formulation of the chromagenic algorithm has inherent weaknesses: a need for perfectly registered images and occasional large errors in illuminant estimation. Our first contribution is to analyse the algorithm performance with respect to the reflectances present in a scene and demonstrate that fairly bright and desaturated reflectances (e.g., achromatic and pastel colours) provide significantly better chromagenic illuminant estimation.This analysis leads to the bright-chromagenic algorithm. We show that it not only remedies the large error problem but also allows us to relax the image registration constraint. Experiments performed on a variety of synthetic and real data show that the newly designed brightchromagenic algorithm significantly -in a strict statistical sense-outperforms current illuminant estimation methods, including those having a substantially higher complexity.

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“…The chromatic context of the surface might bias this estimate, but it is still correlated with the colour of the illuminant. The extent of this bias can be exploited as a quantitative probe of the effects of scene structure [34,37]. If subjects compare the surface with an imaginary standard, then the task is similar to asymmetric colour matching, but is less precise [16].…”

Section: Judging Whitementioning

confidence: 99%