Computational Colour Science using MATLAB®

Westland, Stephen; Ripamonti, Caterina; Cheung, Vien

doi:10.1002/9780470710890

Cited by 126 publications

(108 citation statements)

References 19 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Results from previous experiences show that good results can be obtained using second order polynomial transformation equations (Westland et al 2012;Balasubramanian 2003).…”

Section: Digital Camera Characterisation Processmentioning

confidence: 95%

“…The exposed methodology combines the direct method based on colourimetric measurements, and the indirect method using raw images with RGB information (Westland et al 2012;Martínez-Verdú et al 2003). The software developed in this study (pyColourimetry) covers each of the steps required for proper camera characterisation, and gives the user full control on the overall computation process.…”

Section: Discussionmentioning

confidence: 99%

“…Three polynomial models were considered: linear, second and third order (Westland et al 2012). The number of coefficients to be determined is 3, 10 and 19, respectively, depending on the degree of the polynomial (Table 2).…”

Section: Software Development For Colourimetric and Spectral Data Promentioning

confidence: 99%

“…An approach to transform RGB dependent values into a independent colour space (such CIE XYZ or sRGB which is somehow based on CIE XYZ), is the digital camera characterisation by polynomial regression with least squares fitting (Balasubramanian 2003;Westland et al 2012;Malacara 2011). Once the RGB to CIE XYZ transformation equations are obtained, a conventional digital camera could be used for rigorous colour determination as if it were a colourimeter (Martínez-Verdú et al 2003).…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Software Development for Colourimetric and Spectral Data Processing: Pycolourimetry

Molada-Tebar

Lerma

Marqués-Mateu

2017

Proceedings - 1st Congress in Geomatics Engineering - CIGeo

View full text Add to dashboard Cite

Abstract:Archaeological documentation is a complex process where the technical measurement and specification of colour is a key aspect. In the last years heritage documentation processes have largely benefited from the application of digital recording methods, imagery analysis software and technologies that offers great advantages over the traditional methods. The rigorous processing of colourimetric data requires software packages with specific colourimetric technical characteristics. In this paper we report on our in-house pyColourimetry software that was developed and tested taking into account the recommendations of the Commission Internationale de l'Éclairage (CIE). The objective is to apply a rigorous procedure for the characterisation of cameras based a priori on polynomial models. Most of the digital cameras capture colour information in the well-known RGB format, but the signals generated by the digital camera are device dependent. By means of the characterisation we establish the relationship between device dependent RGB values and the tristimulus coordinates defined by the CIE standard colourimetric observer. Once the camera is characterised, users have the potential to obtain output images in the sRGB space that is independent of the sensor of the camera. pyColourimetry software allows users to control the entire digital image processing and the colourimetric data workflow proposed. We applied the methodology on a set of pictures targeting Levantine rock art motifs in Cova dels Cavalls (Castellón, Spain) which is considered part of a UNESCO World Heritage Site. The outcomes obtained are satisfactory and very promising for proper colour documentation in cultural heritage estudies. Key words: Camera characterisation, Rock Art, Colourimetry, CIE Colour spaces, Python Resumen:La documentación del patrimonio arqueológico es un proceso complejo donde la correcta medición del color es un aspecto clave. En los últimos años los procesos de documentación se han beneficiado gracias al uso de imágenes digitales, software de tratamiento de imágenes, así como de otras tecnologías frente a los métodos tradicionales. El procesamiento riguroso de datos colorimétricos requiere paquetes de software con características técnicas específicas. En este artículo presentamos un informe sobre el software pyColourimetry, desarrollado y testado teniendo en cuenta las recomendaciones de la Comisión Internacional de la Iluminación (CIE). Mediante este software el susuario puede caracterizar cámaras digitales a partir de modelos polinomiales. La mayoría de cámaras digitales captan información del color en el conocido sistema RGB. Sin embargo, la señal generada depende del dispositivo. A partir de la caracterización establecemos la relación entre los valores RGB dependientes y las coordenadas independientes CIE triestímulo. Una vez caracterizada la cámara se obtienen imágenes en el espacio sRGB, independiente del sensor empleado. El software pyColourimetry permite a los usuarios controlar todo el procesamiento digital de imágenes y ...

show abstract

“…Results from previous experiences show that good results can be obtained using second order polynomial transformation equations (Westland et al 2012;Balasubramanian 2003).…”

Section: Digital Camera Characterisation Processmentioning

confidence: 95%

Section: Discussionmentioning

confidence: 99%

Section: Software Development For Colourimetric and Spectral Data Promentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Software Development for Colourimetric and Spectral Data Processing: Pycolourimetry

Molada-Tebar

Lerma

Marqués-Mateu

2017

Proceedings - 1st Congress in Geomatics Engineering - CIGeo

View full text Add to dashboard Cite

show abstract

“…(9). Figure 11 shows a sRGB color image obtained by transforming the recovered L*, a*, b* values to sRGB using MATLAB [18]. At a glance, the colors in Fig.…”

Section: Target Color Objectsmentioning

confidence: 99%

Recovering the Colors of Objects from Multiple Near-IR Images

Kim¹,

Oh²,

Kim³

et al. 2015

Journal of the Optical Society of Korea

View full text Add to dashboard Cite

This paper proposes an algorithm for recovering the colors of objects from multiple near-infrared (near-IR) images. The International Commission on Illumination (CIE) color coordinates of objects are recovered from a series of gray images captured under multiple spectral near-IR illuminations using polynomial regression. The feasibility of the proposed algorithm is tested experimentally by using 24 color patches of the Color Rendition Chart. The experimental apparatus is composed of a monochrome digital camera without an IR cut-off filter and a custom-designed LED illuminator emitting multiple spectral near-IR illuminations, with peak wavelengths near the red edge of the visible band, namely at 700, 740, 780, and 860 nm. The average color difference between the original and the recovered colors for all 24 patches was found to be 11.1. However, if some particular patches with high value are disregarded, the average color difference is reduced to 4.2, and this value is within the acceptability tolerance for complex image on the display.

show abstract

The Past

2018

2.5D Printing

View full text Add to dashboard Cite

Computational Colour Science using MATLAB®

Cited by 126 publications

References 19 publications

Software Development for Colourimetric and Spectral Data Processing: Pycolourimetry

Software Development for Colourimetric and Spectral Data Processing: Pycolourimetry

Recovering the Colors of Objects from Multiple Near-IR Images

The Past

Contact Info

Product

Resources

About