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A Taxonomy of Color Constancy and Invariance Algorithm

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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

Color is an effective cue for identifying regions of interest or objects for a wide range of applications in computer vision and digital image processing research. However, color information in recorded image data, typically represented in RGB format, is not always an intrinsic property of an object itself, but rather it also depends on the illumination condition and sensor characteristic. When these factors are not properly taken into consideration, the performance of color analysis system can deteriorate significantly. This chapter investigates two common methodologies to attain reliable color description of recorded image data, color constancy and color invariance. Comprehensive overview of existing techniques are presented. Further, fundamental physical models of light reflection, and a color image formation process in typical imaging devices are discussed, which provide important underlying concepts for various color constancy and invariance algorithms. Finally, two experiments are demonstrated to evaluate the performance of representative color constancy and invariance algorithms.

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Notes

  1. 1.

    The choice of canonical illuminant is arbitrary, but for image reproduction application, it is commonly defined as an illuminant for which the camera sensor is balanced [4].

  2. 2.

    Unlike optically homogeneous materials (e.g. metals, glasses, and crystals) which have a constant refraction index throughout the material, inhomogeneous materials (e.g. paints, ceramics, and plastics) are composed of a vehicle with many embedded colorant particles that differs optically from the vehicle. The DRM limits its discussion to optically inhomogeneous materials.

  3. 3.

    The bi-directional reflectance distribution function (BRDF) describes that surface reflectance of a material \(S(\varvec{\theta },\lambda )\) is a function of wavelength \(\lambda \) and imaging geometry \(\varvec{\theta }\), where \(\varvec{\theta }\) contains information related to the directions of the incident and reflected radiance in the local coordinate system. According to the Lambertian surface model, the BRDF is a constant function of the imaging geometry so that \(S(\varvec{\theta },\lambda ) \rightarrow S(\lambda )\).

  4. 4.

    Here, NIR hypothesis is used that the spectral reflectance distribution of the specular reflection term in the DRM is similar to the spectral energy distribution of the scene illuminant.

  5. 5.

    where its horizontal axis represents the inverse-intensity and its vertical axis represents the image chromaticity \(\sigma _c\).

  6. 6.

    The log-relative-chromaticity value of a pixel is defined as the logarithm of the ratio between the chromaticity of the given pixel and the chromaticity of the scene illuminant.

  7. 7.

    The instability of the method when \(I_B = 0\) can be addressed by exploiting histogram based method rather than using input RGB values directly [18].

  8. 8.

    The memory color refers to a group of colors that has the most perceptive impact on the human visual system, such as skin color, blue sky, and green foliage [71].

  9. 9.

    A plane divides 3-dimensional space into two half-spaces. A half-space can be specified by a linear inequality.

  10. 10.

    Here, geometric terms \(w_d\) and \(w_s\) of DRM in (3) are restated by specifying their dependence on the surface normal, the direction of the light source, and the direction of the viewer.

  11. 11.

    Here, we assume the integrated white light condition (i.e. the area under the sensor spectral sensitivity function is approximately the same for all three channels)[35] such that \(\int _\omega \rho _R(\lambda )d\lambda = \int _\omega \rho _G(\lambda )d\lambda = \int _\omega \rho _B(\lambda )d\lambda = \rho \).

  12. 12.

    Although there exist many different ways to compute HSI representation, here, we use a definition given in [55].

  13. 13.

    Shadow edges indicate the shadow of the illuminated object. Highlight edges are generated by the specular property of the object surface. Material edges indicate the discontinuity due to a change of surface material

  14. 14.

    It is well known that Apple systems are calibrated to gamma value of 1.8, whereas most other systems are calibrated to gamma value of 2.2. It implies that images of same scene may appear differently dependent on the target systems.

  15. 15.

    Since the hue is dependent on \(w_d(\mathbf n ,\mathbf s )\) and \(w_s(\mathbf n ,\mathbf s ,\mathbf v )\), it is no longer invariant to surface orientation and illumination direction with the nonlinear response. It is still invariant to illumination intensity as \(E\) term can be cancelled out.

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Authors and Affiliations

  1. Multimedia Lab, The Edward S. Rogers Department of Electrical and Computer Engineering, University of Toronto, 10 King’s College Road, Toronto, Canada

    Dohyoung Lee & Konstantinos N. Plataniotis

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  1. Dohyoung Lee
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  2. Konstantinos N. Plataniotis
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Correspondence to Dohyoung Lee .

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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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Lee, D., Plataniotis, K.N. (2014). A Taxonomy of Color Constancy and Invariance Algorithm. 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_3

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  • DOI: https://doi.org/10.1007/978-94-007-7584-8_3

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