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On the von Kries Model: Estimation, Dependence on Light and Device, and Applications

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Advances in Low-Level Color Image Processing

Part of the book series: Lecture Notes in Computational Vision and Biomechanics ((LNCVB,volume 11))

Abstract

The von Kries model is widely employed to describe the color variation between two pictures portraying the same scene but captured under two different lights. Simple but effective, this model has been proved to be a good approximation of such a color variation and it underpins several color constancy algorithms. Here we present three recent research results: an efficient histogram-based method to estimate the parameters of the von Kries model, and two theoretical advances, that clarify the dependency of these parameters on the physical cues of the varied lights and on the photometric properties of the camera used for the acquisition. We illustrate many applications of these results: color correction, illuminant invariant image retrieval, estimation of color temperature and intensity of a light, and photometric characterization of a device. We also include a wide set of experiments carried out on public datasets, in order to allow the reproducibility and the verification of the results, and to enable further comparisons with other approaches.

An erratum to this chapter is available at 10.1007/978-94-007-7584-8_14

An erratum to this chapter can be found at http://dx.doi.org/10.1007/978-94-007-7584-8_14

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Authors and Affiliations

  1. Fondazione Bruno Kessler, via Sommarive 18, 38123, Trento, Italy

    Michela Lecca

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  1. Michela Lecca
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Correspondence to Michela Lecca .

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Editors and Affiliations

  1. Computer Science dept., Louisiana State University, Shreveport, Louisiana, USA

    M. Emre Celebi

  2. Automatic Control, Silesian University of Technology, Gliwice, Poland

    Bogdan Smolka

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Lecca, M. (2014). On the von Kries Model: Estimation, Dependence on Light and Device, and Applications. In: Celebi, M., Smolka, B. (eds) Advances in Low-Level Color Image Processing. Lecture Notes in Computational Vision and Biomechanics, vol 11. Springer, Dordrecht. https://doi.org/10.1007/978-94-007-7584-8_4

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  • DOI: https://doi.org/10.1007/978-94-007-7584-8_4

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