A parallel algorithm for color constancy

Ebner, Marc

doi:10.1016/j.jpdc.2003.06.004

Cited by 46 publications

(30 citation statements)

References 28 publications

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“…We previously reported on a parallel algorithm for color constancy (Ebner, 2004) based on the computation of local space average color. Here, we we describe how this parallel algorithm may be implemented using a resistive grid which allows integration into imaging chips and display devices.…”

Section: Motivationmentioning

confidence: 99%

“…Once the illuminant is estimated, the reflectances can be computed. Quite a large number of algorithms have been proposed that use the gray world assumption in one way or another (Ebner, 2003(Ebner, , 2004(Ebner, , 2006Finlayson et al, 1998;Gershon et al, 1987;Moore et al, 1991;Paulus et al, 1998;Pomierski and Groß, 1995;Rahman et al, 1999;Tominaga, 1991).…”

Section: The Gray World Assumptionmentioning

confidence: 99%

“…Since the illuminant has to be estimated locally for each image pixel we can search for a parallel algorithm (Ebner, 2004(Ebner, , 2006). There are two major incentives for parallelizing this color constancy algorithm: (1) A parallel algorithm for color constancy can be mapped to what is known about the human stages of color perception.…”

Section: Estimating the Illuminant Locallymentioning

confidence: 99%

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Color constancy based on local space average color

Ebner

2008

Machine Vision and Applications

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Light, which is reflected from an object, varies with the type of illuminant used. Nevertheless, the color of an object appears to be approximately constant to a human observer. The ability to compute color constant descriptors from reflected light, is called color constancy. In order to solve the problem of color constancy, some assumptions have to be made. One frequently made assumption is that on average, the world is gray. We address the problem of color constancy and focus on the use of space average color for color constancy. Instead of computing global space average color we suggest to use local space average color as the illuminant frequently varies across an image. We discuss several different methods on how to compute local space average color. The performance of the different algorithms as well as related algorithms is evaluated on an object recognition task. Algorithms based on local space average color are simple, yet highly effective for the problem of color constancy. Such algorithms are particularly suited for object recognition tasks.

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Section: Motivationmentioning

confidence: 99%

Section: The Gray World Assumptionmentioning

confidence: 99%

Section: Estimating the Illuminant Locallymentioning

confidence: 99%

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Color constancy based on local space average color

Ebner

2008

Machine Vision and Applications

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“…Using the constraint that the illuminant can be approximated by the curve of the black-body radiator, in theory it is possible, to determine the color of the illuminant from a single surface. Algorithms based on the gray world assumption [8,9,10] in contrast, require that the scene be sufficiently diverse.…”

Section: Natural Illuminantsmentioning

confidence: 99%

On Determining the Color of the Illuminant Using the Dichromatic Reflection Model

Ebner

Herrmann

2005

Lecture Notes in Computer Science

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Abstract. The human visual system is able to accurately determine the color of objects irrespective of the spectral power distribution used to illuminate the scene. This ability to compute color constant descriptors is called color constancy. Many different algorithms have been proposed to solve the problem of color constancy. Usually, some assumptions have to be made in order to solve this problem. Algorithms based on the dichromatic reflection model assume that the light reflected from an object results from a combined matte and specular reflection. This assumption is used to estimate the color of the illuminant. Once the color of the illuminant is known, one can compute a color corrected image as it would appear under a canonical, i.e. white illuminant. A number of different methods can be used to estimate the illuminant from the dichromatic reflection model. We evaluate several different methods on a standard set of images. Our results indicate that the median operator is particularly useful in estimating the color of the illuminant. We also found that it is not advantageous to assume that the illuminant can be approximated by the curve of the black-body radiator.

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“…만약 나무가 적색 조명체에 의해 조명되어지고 나뭇잎들이 실제로 녹색성분 보다 적색성분을 더 많이 반사하더라도 인 간 관찰자에게 그것은 녹색으로 보인다 [2]. 그러나 일반 카 메라와 같은 영상 획득 장비는 물체가 반사한 빛의 양과 물 체 표면의 반사계수의 곱을 측정하기 때문에, 동일 장면에 서 동일 물체를 촬영할지라도 조명 조건에 따라 물체 표면 색을 다르게 획득한다 [3].…”

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Acquisition of Intrinsic Image by Omnidirectional Projection of ROI and Translation of White Patch on the X-chromaticity Space

Kim¹,

Hwang²,

Lee³

et al. 2011

The KIPS Transactions:PartB

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Algorithms for intrinsic images reduce color differences in RGB images caused by the temperature of black-body radiators. Based on the reference light and detecting single invariant direction, these algorithms are weak in real images which can have multiple invariant directions when the scene illuminant is a colored illuminant. To solve these problems, this paper proposes a method of acquiring an intrinsic image by omnidirectional projection of an ROI and a translation of white patch in the  -chromaticity space. Because it is not easy to analyze an image in the three-dimensional RGB space, the  -chromaticity is also employed without the brightness factor in this paper. After the effect of the colored illuminant is decreased by a translation of white patch, an invariant direction is detected by omnidirectional projection of an ROI in this chromaticity space. In case the RGB image has multiple invariant directions, only one ROI is selected with the bin, which has the highest frequency in 3D histogram. And then the two operations, projection and inverse transformation, make intrinsic image acquired. In the experiments, test images were four datasets presented by Ebner and evaluation methods was the follows: standard deviation of the invariant direction, the constancy measure, the color space measure and the color constancy measure. The experimental results showed that the proposed method had lower standard deviation than the entropy, that its performance was two times higher than the compared algorithm.

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A parallel algorithm for color constancy

Cited by 46 publications

References 28 publications

Color constancy based on local space average color

Color constancy based on local space average color

On Determining the Color of the Illuminant Using the Dichromatic Reflection Model

Acquisition of Intrinsic Image by Omnidirectional Projection of ROI and Translation of White Patch on the X-chromaticity Space

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