Color Constancy Using Local Color Shifts

Ebner, Marc

doi:10.1007/978-3-540-24672-5_22

Cited by 36 publications

(27 citation statements)

References 30 publications

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“…For example, (Fleyeh, 2005) uses the local color shifts method of (Ebner, 2004) to normalize the images before applying the dynamic thresholding method. (Le et al, 2010) use the same color constancy algorithm and (Zakir et al, 2011) use the Gray-World algorithm to normalize the images.…”

Section: Related Workmentioning

confidence: 99%

Robust Road Signs Segmentation in Color Images

Khanal

Ali

Sidibé

2012

Proceedings of the International Conference on Computer Vision Theory and Applications

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Abstract:This paper presents an efficient method for road signs segmentation in color images. Color segmentation of road signs is a difficult task due to variations in the image acquisition conditions. Therefore, a color constancy algorithm is usually applied prior to segmentation, which increases the computation time. The proposed method is based on a log-chromaticity color space which shows good invariance properties to changing illumination. Thus, the method is simple and fast since it does not require color constancy algorithms. Experiments with a large dataset and comparison with other approaches, show the robustness and accuracy of the method in detecting road signs in various conditions.

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Section: Related Workmentioning

confidence: 99%

Robust Road Signs Segmentation in Color Images

Khanal

Ali

Sidibé

2012

Proceedings of the International Conference on Computer Vision Theory and Applications

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“…Thus, an object-based illumination extraction is inappropriate in this case. Pixel diffusion-based approaches like the one by Ebner [7] can be expected to solve the church floor example, but are expected to fail on object boundaries as in the left image.…”

Section: From Uniform To Non-uniform Illuminationmentioning

confidence: 99%

“…To our knowledge there have been very few methods that explicitly focus on such local illuminant estimation. Ebner [7], for example, applied a diffusionbased methodology on the pixel intensities. However, he locally assumed a gray-world which can result in inaccuracies, especially in colorful scenes [16].…”

Section: Introductionmentioning

confidence: 99%

Color constancy and non-uniform illumination: Can existing algorithms work?

Bleier

Rieß

Beigpour

et al. 2011

2011 IEEE International Conference on Computer Vision Workshops (ICCV Workshops)

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The color and distribution of illuminants can significantly alter the appearance of a scene. The goal of color constancy (CC) is to remove the color bias introduced by the illuminants. Most existing CC algorithms assume a uniformly illuminated scene. However, more often than not, this assumption is an insufficient approximation of realworld illumination conditions (multiple light sources, shadows, interreflections, etc.). Thus, illumination should be locally determined, taking under consideration that multiple illuminants may be present. In this paper we investigate the suitability of adapting 5 state-of-the-art color constancy methods so that they can be used for local illuminant estimation. Given an arbitrary image, we segment it into superpixels of approximately similar color. Each of the methods is applied independently on every superpixel. For improved accuracy, these independent estimates are combined into a single illuminant-color value per superpixel. We evaluated different fusion methodologies. Our experiments indicate that the best performance is obtained by fusion strategies that combine the outputs of the estimators using regression.

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“…Methods can be semi-automatic [20] or automatic. Automatic methods usually work well under quite strong assumptions, like hard shadows and black-body radiators lights [19] or localized gray-world model [8]. A very recent work [18] proposes a white balance technique which renders visually pleasing images by recovering a set of dominant material colors using the technique proposed by [22].…”

Section: Related Workmentioning

confidence: 99%

Flash Lighting Space Sampling

Dellepiane

Callieri

Corsini

et al. 2009

Computer Vision/Computer Graphics CollaborationTechniques

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Abstract. Flash light of digital cameras is a very useful way to picture scenes with low quality illumination. Nevertheless, especially low-end cameras integrated flash lights are considered as not reliable for high quality images, due to known artifacts (sharp shadows, highlights, uneven lighting) generated in images. Moreover, a mathematical model of this kind of light seems difficult to create. In this paper we present a color correction space which, given some information about the geometry of the pictured scene, is able to provide a space-dependent correction of each pixel of the image. The correction space can be calculated once in a lifetime using a quite fast acquisition procedure; after 3D spatial calibration, obtained color correction function can be applied to every image where flash is the dominant illuminant. The correction space presents several advantages: it is independent from the kind of light used (provided that it is bound to the camera), it gives the possibly to correct only determinate artifacts (for example color deviation) introduced by flash light, and it has a wide range of possible applications, from image enhancement to material color estimation.

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Color Constancy Using Local Color Shifts

Cited by 36 publications

References 30 publications

Robust Road Signs Segmentation in Color Images

Robust Road Signs Segmentation in Color Images

Color constancy and non-uniform illumination: Can existing algorithms work?

Flash Lighting Space Sampling

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