The Experts below are selected from a list of 3912 Experts worldwide ranked by ideXlab platform
Arjan Gijsenij - One of the best experts on this subject based on the ideXlab platform.
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Color Constancy by derivative-based gamut mapping, in "Workshop on Photometric Analysis for Computer Vision in conjuncture with ICCV", October 2007, http:// lear.inrialpes.fr/pubs/2007/GGV07d
2015Co-Authors: Arjan Gijsenij, Theo Gevers, Joost Van De WeijerAbstract:Color Constancy aims to compute object Colors despite differences in the Color of the light source. Gamut-based approaches are very promising methods to achieve Color Constancy. In this paper, the gamut mapping approach is extended to incorporate higher-order statistics (derivatives) to estimate the illuminant. A major problem of gamut mapping is that in case of a failure of the diagonal model no solutions are found, and therefore no illuminant estimation is performed. Im-age value offsets are often used to model deviations from the diagonal model. Prior work which incorporated robust-ness to offsets for gamut mapping assumed a constant off-set over the whole image. In contrast to previous work, we model these offsets to be position dependent, and show that for this case derivative-based gamut mapping yields a valid solution to the illuminant estimation problem. Experiments on both synthetic data and images taken un-der controlled laboratory settings reveal that the derivative-based and regular gamut mapping methods provide similar performance. However, the derivative-based method out-performs other methods on the more challenging task of Color Constancy for real-world images. 1
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Color Constancy using natural image statistics and scene semantics
IEEE Transactions on Pattern Analysis and Machine Intelligence, 2011Co-Authors: Arjan Gijsenij, Theo GeversAbstract:Existing Color Constancy methods are all based on specific assumptions such as the spatial and spectral characteristics of images. As a consequence, no algorithm can be considered as universal. However, with the large variety of available methods, the question is how to select the method that performs best for a specific image. To achieve selection and combining of Color Constancy algorithms, in this paper natural image statistics are used to identify the most important characteristics of Color images. Then, based on these image characteristics, the proper Color Constancy algorithm (or best combination of algorithms) is selected for a specific image. To capture the image characteristics, the Weibull parameterization (e.g., grain size and contrast) is used. It is shown that the Weibull parameterization is related to the image attributes to which the used Color Constancy methods are sensitive. An MoG-classifier is used to learn the correlation and weighting between the Weibull-parameters and the image attributes (number of edges, amount of texture, and SNR). The output of the classifier is the selection of the best performing Color Constancy method for a certain image. Experimental results show a large improvement over state-of-the-art single algorithms. On a data set consisting of more than 11,000 images, an increase in Color Constancy performance up to 20 percent (median angular error) can be obtained compared to the best-performing single algorithm. Further, it is shown that for certain scene categories, one specific Color Constancy algorithm can be used instead of the classifier considering several algorithms.
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Generalized Gamut Mapping using Image Derivative Structures for Color Constancy
International Journal of Computer Vision, 2010Co-Authors: Arjan Gijsenij, Theo Gevers, Joost WeijerAbstract:The gamut mapping algorithm is one of the most promising methods to achieve computational Color Constancy. However, so far, gamut mapping algorithms are restricted to the use of pixel values to estimate the illuminant. Therefore, in this paper, gamut mapping is extended to incorporate the statistical nature of images. It is analytically shown that the proposed gamut mapping framework is able to include any linear filter output. The main focus is on the local n -jet describing the derivative structure of an image. It is shown that derivatives have the advantage over pixel values to be invariant to disturbing effects (i.e. deviations of the diagonal model) such as saturated Colors and diffuse light. Further, as the n -jet based gamut mapping has the ability to use more information than pixel values alone, the combination of these algorithms are more stable than the regular gamut mapping algorithm. Different methods of combining are proposed. Based on theoretical and experimental results conducted on large scale data sets of hyperspectral, laboratory and real-world scenes, it can be derived that (1) in case of deviations of the diagonal model, the derivative-based approach outperforms the pixel-based gamut mapping, (2) state-of-the-art algorithms are outperformed by the n -jet based gamut mapping, (3) the combination of the different n -jet based gamut mappings provide more stable solutions, and (4) the fusion strategy based on the intersection of feasible sets provides better Color Constancy results than the union of the feasible sets.
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edge based Color Constancy
IEEE Transactions on Image Processing, 2007Co-Authors: J Van De Weijer, Theo Gevers, Arjan GijsenijAbstract:Color Constancy is the ability to measure Colors of objects independent of the Color of the light source. A well-known Color Constancy method is based on the gray-world assumption which assumes that the average reflectance of surfaces in the world is achromatic. In this paper, we propose a new hypothesis for Color Constancy namely the gray-edge hypothesis, which assumes that the average edge difference in a scene is achromatic. Based on this hypothesis, we propose an algorithm for Color Constancy. Contrary to existing Color Constancy algorithms, which are computed from the zero-order structure of images, our method is based on the derivative structure of images. Furthermore, we propose a framework which unifies a variety of known (gray-world, max-RGB, Minkowski norm) and the newly proposed gray-edge and higher order gray-edge algorithms. The quality of the various instantiations of the framework is tested and compared to the state-of-the-art Color Constancy methods on two large data sets of images recording objects under a large number of different light sources. The experiments show that the proposed Color Constancy algorithms obtain comparable results as the state-of-the-art Color Constancy methods with the merit of being computationally more efficient.
Theo Gevers - One of the best experts on this subject based on the ideXlab platform.
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UvA-DARE (Digital Academic Repository) A Physical Basis for Color Constancy
2020Co-Authors: Jan-mark Geusebroek, Rein Van Den Boomgaard, Arnold W M Smeulders, Theo GeversAbstract:A Physical Basis for Color Constancy Geusebroek, J.M.; van den Boomgaard, R.; Smeulders, A.W.M.; Gevers, T. General rights It is not permitted to download or to forward/distribute the text or part of it without the consent of the author(s) and/or copyright holder(s), other than for strictly personal, individual use, unless the work is under an open content license (like Creative Commons). Disclaimer/Complaints regulations If you believe that digital publication of certain material infringes any of your rights or (privacy) interests, please let the Library know, stating your reasons. In case of a legitimate complaint, the Library will make the material inaccessible and/or remove it from the website. Please Ask the Library: https://uba.uva.nl/en/contact, or a letter to: Library of the University of Amsterdam, Secretariat, Singel 425, 1012 WP Amsterdam, The Netherlands. You will be contacted as soon as possible. Abstract A fundamental problem in psychophysical experiments is that significant conclusions are hard to draw due to the complex experimental environment necessary to examine Color Constancy. An alternative approach to reveal the mechanisms involved in Color Constancy is by modeling the physical process of spectral image formation. In this paper, we aim at a physical basis for Color Constancy rather than a psychophysical one. By considering spatial and spectral derivatives of the Lambertian image formation model, object reflectance properties are derived independent of the spectral energy distribution of the illuminant. Gaussian spectral and spatial probes are used to estimate the proposed differential invariant. Knowledge about the spectral power distribution of the illuminant is not required for the proposed invariant. The physical approach to Color Constancy offered in the paper confirms relational Color Constancy as a first step in Color constant vision systems. Hence, low-level mechanisms as Color constant edge detection reported here may play an important role in front-end vision. The research presented raises the question whether the illuminant is estimated at all in pre-attentive vision
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Color Constancy by derivative-based gamut mapping, in "Workshop on Photometric Analysis for Computer Vision in conjuncture with ICCV", October 2007, http:// lear.inrialpes.fr/pubs/2007/GGV07d
2015Co-Authors: Arjan Gijsenij, Theo Gevers, Joost Van De WeijerAbstract:Color Constancy aims to compute object Colors despite differences in the Color of the light source. Gamut-based approaches are very promising methods to achieve Color Constancy. In this paper, the gamut mapping approach is extended to incorporate higher-order statistics (derivatives) to estimate the illuminant. A major problem of gamut mapping is that in case of a failure of the diagonal model no solutions are found, and therefore no illuminant estimation is performed. Im-age value offsets are often used to model deviations from the diagonal model. Prior work which incorporated robust-ness to offsets for gamut mapping assumed a constant off-set over the whole image. In contrast to previous work, we model these offsets to be position dependent, and show that for this case derivative-based gamut mapping yields a valid solution to the illuminant estimation problem. Experiments on both synthetic data and images taken un-der controlled laboratory settings reveal that the derivative-based and regular gamut mapping methods provide similar performance. However, the derivative-based method out-performs other methods on the more challenging task of Color Constancy for real-world images. 1
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Color Constancy using natural image statistics and scene semantics
IEEE Transactions on Pattern Analysis and Machine Intelligence, 2011Co-Authors: Arjan Gijsenij, Theo GeversAbstract:Existing Color Constancy methods are all based on specific assumptions such as the spatial and spectral characteristics of images. As a consequence, no algorithm can be considered as universal. However, with the large variety of available methods, the question is how to select the method that performs best for a specific image. To achieve selection and combining of Color Constancy algorithms, in this paper natural image statistics are used to identify the most important characteristics of Color images. Then, based on these image characteristics, the proper Color Constancy algorithm (or best combination of algorithms) is selected for a specific image. To capture the image characteristics, the Weibull parameterization (e.g., grain size and contrast) is used. It is shown that the Weibull parameterization is related to the image attributes to which the used Color Constancy methods are sensitive. An MoG-classifier is used to learn the correlation and weighting between the Weibull-parameters and the image attributes (number of edges, amount of texture, and SNR). The output of the classifier is the selection of the best performing Color Constancy method for a certain image. Experimental results show a large improvement over state-of-the-art single algorithms. On a data set consisting of more than 11,000 images, an increase in Color Constancy performance up to 20 percent (median angular error) can be obtained compared to the best-performing single algorithm. Further, it is shown that for certain scene categories, one specific Color Constancy algorithm can be used instead of the classifier considering several algorithms.
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Generalized Gamut Mapping using Image Derivative Structures for Color Constancy
International Journal of Computer Vision, 2010Co-Authors: Arjan Gijsenij, Theo Gevers, Joost WeijerAbstract:The gamut mapping algorithm is one of the most promising methods to achieve computational Color Constancy. However, so far, gamut mapping algorithms are restricted to the use of pixel values to estimate the illuminant. Therefore, in this paper, gamut mapping is extended to incorporate the statistical nature of images. It is analytically shown that the proposed gamut mapping framework is able to include any linear filter output. The main focus is on the local n -jet describing the derivative structure of an image. It is shown that derivatives have the advantage over pixel values to be invariant to disturbing effects (i.e. deviations of the diagonal model) such as saturated Colors and diffuse light. Further, as the n -jet based gamut mapping has the ability to use more information than pixel values alone, the combination of these algorithms are more stable than the regular gamut mapping algorithm. Different methods of combining are proposed. Based on theoretical and experimental results conducted on large scale data sets of hyperspectral, laboratory and real-world scenes, it can be derived that (1) in case of deviations of the diagonal model, the derivative-based approach outperforms the pixel-based gamut mapping, (2) state-of-the-art algorithms are outperformed by the n -jet based gamut mapping, (3) the combination of the different n -jet based gamut mappings provide more stable solutions, and (4) the fusion strategy based on the intersection of feasible sets provides better Color Constancy results than the union of the feasible sets.
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edge based Color Constancy
IEEE Transactions on Image Processing, 2007Co-Authors: J Van De Weijer, Theo Gevers, Arjan GijsenijAbstract:Color Constancy is the ability to measure Colors of objects independent of the Color of the light source. A well-known Color Constancy method is based on the gray-world assumption which assumes that the average reflectance of surfaces in the world is achromatic. In this paper, we propose a new hypothesis for Color Constancy namely the gray-edge hypothesis, which assumes that the average edge difference in a scene is achromatic. Based on this hypothesis, we propose an algorithm for Color Constancy. Contrary to existing Color Constancy algorithms, which are computed from the zero-order structure of images, our method is based on the derivative structure of images. Furthermore, we propose a framework which unifies a variety of known (gray-world, max-RGB, Minkowski norm) and the newly proposed gray-edge and higher order gray-edge algorithms. The quality of the various instantiations of the framework is tested and compared to the state-of-the-art Color Constancy methods on two large data sets of images recording objects under a large number of different light sources. The experiments show that the proposed Color Constancy algorithms obtain comparable results as the state-of-the-art Color Constancy methods with the merit of being computationally more efficient.
Seoung Wug Oh - One of the best experts on this subject based on the ideXlab platform.
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approaching the computational Color Constancy as a classification problem through deep learning
Pattern Recognition, 2017Co-Authors: Seoung Wug OhAbstract:Abstract Computational Color Constancy refers to the problem of computing the illuminant Color so that the images of a scene under varying illumination can be normalized to an image under the canonical illumination. In this paper, we adopt a deep learning framework for the illumination estimation problem. The proposed method works under the assumption of uniform illumination over the scene and aims for the accurate illuminant Color computation. Specifically, we trained the convolutional neural network to solve the problem by casting the Color Constancy problem as an illumination classification problem. We designed the deep learning architecture so that the output of the network can be directly used for computing the Color of the illumination. Experimental results show that our deep network is able to extract useful features for the illumination estimation and our method outperforms all previous Color Constancy methods on multiple test datasets.
Joost Weijer - One of the best experts on this subject based on the ideXlab platform.
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Generalized Gamut Mapping using Image Derivative Structures for Color Constancy
International Journal of Computer Vision, 2010Co-Authors: Arjan Gijsenij, Theo Gevers, Joost WeijerAbstract:The gamut mapping algorithm is one of the most promising methods to achieve computational Color Constancy. However, so far, gamut mapping algorithms are restricted to the use of pixel values to estimate the illuminant. Therefore, in this paper, gamut mapping is extended to incorporate the statistical nature of images. It is analytically shown that the proposed gamut mapping framework is able to include any linear filter output. The main focus is on the local n -jet describing the derivative structure of an image. It is shown that derivatives have the advantage over pixel values to be invariant to disturbing effects (i.e. deviations of the diagonal model) such as saturated Colors and diffuse light. Further, as the n -jet based gamut mapping has the ability to use more information than pixel values alone, the combination of these algorithms are more stable than the regular gamut mapping algorithm. Different methods of combining are proposed. Based on theoretical and experimental results conducted on large scale data sets of hyperspectral, laboratory and real-world scenes, it can be derived that (1) in case of deviations of the diagonal model, the derivative-based approach outperforms the pixel-based gamut mapping, (2) state-of-the-art algorithms are outperformed by the n -jet based gamut mapping, (3) the combination of the different n -jet based gamut mappings provide more stable solutions, and (4) the fusion strategy based on the intersection of feasible sets provides better Color Constancy results than the union of the feasible sets.
Andreas Bartels - One of the best experts on this subject based on the ideXlab platform.
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Invariance of surface Color representations across illuminant changes in the human cortex.
NeuroImage, 2017Co-Authors: Mm Bannert, Andreas BartelsAbstract:A central problem in Color vision is that the light reaching the eye from a given surface can vary dramatically depending on the illumination. Despite this, our Color percept, the brain's estimate of surface reflectance, remains remarkably stable. This phenomenon is called Color Constancy. Here we investigated which human brain regions represent surface Color in a way that is invariant with respect to illuminant changes. We used physically realistic rendering methods to display natural yet abstract 3D scenes that were displayed under three distinct illuminants. The scenes embedded, in different conditions, surfaces that differed in their surface Color (i.e. in their reflectance property). We used multivariate fMRI pattern analysis to probe neural coding of surface reflectance and illuminant, respectively. While all visual regions encoded surface Color when viewed under the same illuminant, we found that only in V1 and V4α surface Color representations were invariant to illumination changes. Along the visual hierarchy there was a gradient from V1 to V4α to increasingly encode surface Color rather than illumination. Finally, effects of a stimulus manipulation on individual behavioral Color Constancy indices correlated with neural encoding of the illuminant in hV4. This provides neural evidence for the Equivalent Illuminant Model. Our results provide a principled characterization of Color Constancy mechanisms across the visual hierarchy, and demonstrate complementary contributions in early and late processing stages.