Illuminant color estimation for real-world mixed-illuminant scenes

Rieß, Christian; Eibenberger, Eva; Angelopoulou, Elli

doi:10.1109/iccvw.2011.6130332

Cited by 24 publications

(18 citation statements)

References 32 publications

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“…Hsu et al [2008] can handle only two light colors, need to know their exact values a priori, and cannot treat scenes with "a strong foregroundbackground separation." Riess et al [2011] assume that photos can be decomposed into regions where a single illuminant dominates.…”

Section: Introductionmentioning

confidence: 99%

User-guided white balance for mixed lighting conditions

et al. 2012

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(a) input with mixed lighting (daylight + neon under the cabinets + low-energy bulbs on ceiling) exhibits unsightly color casts everywhere (b) naive single-light white balance makes the ceiling white, but other color casts remain (c) user indicates regions that are neutral (white strokes) or correct after the single-light white balance (grey strokes) (d) the image is improved, but color variations can still be observed, e.g., on the wooden cabinet (e) user adds marks to specify uniform color, e.g., the cabinet and the wall (f) our final output with no color casts Figure 1: In this photo, the ambient lighting, the cabinet light, and the ceiling lights all have different colors, which produces unpleasant color casts (a). In such situations, the single-light white balance tool provided in all photo editing software only improves a portion of the image, but the result is not satisfying (b). We address this issue by letting users make annotations on the photo. First, they mark objects of neutral color (i.e., white or gray), and regions that look fine after the standard white balance (c). This improves the result, but undesirable color variations are still visible, e.g., on the cabinetry and on the wall (d). Users can indicate that these elements should have a constant color (e), which yields a result free of color cast (f). AbstractProper white balance is essential in photographs to eliminate color casts due to illumination. The single-light case is hard to solve automatically but relatively easy for humans. Unfortunately, many scenes contain multiple light sources such as an indoor scene with a window, or when a flash is used in a tungsten-lit room. The light color can then vary on a per-pixel basis and the problem becomes challenging at best, even with advanced image editing tools.We propose a solution to the ill-posed mixed light white balance problem, based on user guidance. Users scribble on a few regions that should have the same color, indicate one or more regions of neutral color , and select regions where the current color looks correct. We first expand the provided scribble groups to more regions using pixel similarity and a robust voting scheme. We formulate the spatially varying white balance problem as a sparse data interpolation problem in which the user scribbles and their extensions form constraints. We demonstrate that our approach can produce satisfying results on a variety of scenes with intuitive scribbles and without any knowledge about the lights.

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Section: Introductionmentioning

confidence: 99%

User-guided white balance for mixed lighting conditions

et al. 2012

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“…The illumination chromaticity can be measured and compensated with a white reference reflectance target in the scene. Since in most cases of FR under uncontrolled conditions, such targets may not be available, we rely on a color constancy algorithm, the physics-based Illuminant Estimation by Voting algorithm by Riess et al 17 , to estimate the illumination chromaticity. We correct this estimate by a fixed Fresnel term 18 , as skin reflectance slightly biases the output of the method.…”

Section: Methods For Specularity Removalmentioning

confidence: 99%

The impact of specular highlights on 3D-2D face recognition

et al. 2013

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One of the most popular form of biometrics is face recognition. Face recognition techniques typically assume that a face exhibits Lambertian reflectance. However, a face often exhibits prominent specularities, especially in outdoor environments. These specular highlights can compromise an identity authentication. In this work, we analyze the impact of such highlights on a 3D-2D face recognition system. First, we investigate three different specularity removal methods as preprocessing steps for face recognition. Then, we explicitly model facial specularities within the face detection system with the Cook-Torrance reflectance model. In our experiments, specularity removal increases the recognition rate on an outdoor face database by about 5% at a false alarm rate of 10 −3 . The integration of the Cook-Torrance model further improves these results, increasing the verification rate by 19% at a FAR of 10 −3 .

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“…Research on color constancy computation the goal is to eliminate the influence of scene illumination. [15], [16]. Its expression is shown in 10 …”

Section: Von Kriesmentioning

confidence: 99%

Estimating Illumination Chromaticity Based on Structured Support Vector Machine

Tang¹,

Liu²,

Yuan³

et al. 2017

dtcse

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Abstract. Illumination estimation is crucial to the color constancy problem. Most existing color constancy algorithms assume uniform illumination. However, in real-world scenes, this is not often the case. Thus, we propose a novel framework for estimation the colors of multiple illuminants and their spatial distribution in the scene. We formulate this problem as an illuminant color estimate is obtained independently from distinct image main-regions. We think there is only one kind of light in each main-regions. From these main-regions a robust local illumination color is computed by consensus. So we can solve the problem of multiple illumination estimation using single illuminant color constancy method. The performance if our method is evaluated on multiple data sets. Experimental results show that we can reliably process mixed illuminant scenes.

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Illuminant color estimation for real-world mixed-illuminant scenes

Cited by 24 publications

References 32 publications

User-guided white balance for mixed lighting conditions

User-guided white balance for mixed lighting conditions

The impact of specular highlights on 3D-2D face recognition

Estimating Illumination Chromaticity Based on Structured Support Vector Machine

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