Numerical methods for colorimetric calculations: Sampling density requirements

Smith, Brent; Spiekermann, Charles E.; Sember, Robert

doi:10.1002/col.5080170605

Cited by 21 publications

(9 citation statements)

References 4 publications

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“…Such a representation is well justified by the Sampling Theorem 15 and a common and welljustified 16 sampling for most imaging systems is 31 samples at 10-nm intervals in the range of 400 -700 nm. With such a representation the continuous integral in Eq.…”

Section: Image Formation and Illumination Changementioning

confidence: 99%

Investigating von Kries‐like adaptation using local linear models

Finlayson

Hordley

Alsam³

2006

Color Research & Application

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Section: Image Formation and Illumination Changementioning

confidence: 99%

Investigating von Kries‐like adaptation using local linear models

Finlayson

Hordley

Alsam³

2006

Color Research & Application

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“…Spectral functions are normally represented by sampling at 10-nanometer intervals across the visible spectrum. 10 Hence, e, s, and r are, normally, 31 3 1 vectors.…”

Section: Theorymentioning

confidence: 99%

Integer programming for optimal reduction of calibration targets

Alsam

Finlayson

2008

Color Research & Application

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Calibration targets are widely used to characterize imaging devices. The question addressed in this article is that of how many surfaces in a calibration target are needed to account for the performance of the whole target. Different to previous research where the problem of reducing calibration charts is addressed independently of the calibration problem; in this article we tackle the reduction question based on the calibration performance. We argue that the outcome of both spectral and colorimetric calibration is dependent on the properties of the cross-product matrix encompassing the color-signals. Further, we show that by careful mathematical manipulation it is possible to write the cross-product matrix as a linear sum of the submatrices corresponding to each individual color signal. This formulation allows us to cast the reduction problem as a quadratic minimization where we ask: given the spectral properties of the available color signals, what is the minimum number of surfaces needed to emulate the global cross-product matrix. To reduce the number of surfaces we impose an integer constraint on the minimization, where the weight of each surface can only assume a value of 1 or 0. Our results show that around 13 surfaces are sufficient to account of the 24 surfaces of the Macbeth color checker.

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“…As shown in [11], it is usually enough to represent the functions R k ( ), S(x, ) and E(x, ) by samples taken at 10 nm intervals over the spectral range of 400 to 700 nm. Using linear algebra notations, reflectance S(x, ), illumination E(x, ), and sensor sensitivity R k ( ) can thus respectively be expressed as the 31 × 1 vectors s, e, r k and Eq.…”

Section: Skin Spectral Reflectance Estimationmentioning

confidence: 99%

Face image enhancement using 3D and spectral information

Dubout

Tsukada

Ishiyama

et al. 2009

2009 16th IEEE International Conference on Image Processing (ICIP)

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This paper presents a novel method of enhancing image quality of face pictures using 3D and spectral information. Most conventional techniques directly work on the image data, shifting the skin color to a predefined skin tone, and thus do not take into account the effects of shape and lighting. The proposed method first recovers the 3D shape of a face in an input image using a 3D morphable model. Then, using color constancy and inverse rendering techniques, specularities and the true skin color, i.e., its spectral reflectance, are recovered. The quality of the input image is improved by matching the skin reflectance to a predefined reference and reducing the amount of specularities. The method realizes the enhancement in a more physically accurate manner compared to previous ones. Subjective experiments on image quality demonstrate the validity of the proposed method.

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Numerical methods for colorimetric calculations: Sampling density requirements

Cited by 21 publications

References 4 publications

Investigating von Kries‐like adaptation using local linear models

Investigating von Kries‐like adaptation using local linear models

Integer programming for optimal reduction of calibration targets

Face image enhancement using 3D and spectral information

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