Prof. Ming R. Luo Profile

Prof. Ming R. Luo

at Univ of Leeds

SPIE Involvement:

Conference Chair | Author | Editor

Publications (33)

SPIE Journal Paper | January 27, 2015

Optimal illumination for local contrast enhancement based on the human visual system

Wang, Huihui , Cuijpers, Raymond , Luo, Ming , Heynderickx, Ingrid , Zheng, Zhenrong

JBO Vol. 20 Issue 01

KEYWORDS: Light emitting diodes, Tissues, Reflectivity, Multispectral imaging, Light sources, Visual system, Light sources and illumination, RGB color model, Lamps, Visibility

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PROCEEDINGS ARTICLE | November 18, 2013

Testing the AUDI2000 colour-difference formula for solid colours using some visual datasets with usefulness to automotive industry

Proc. SPIE. 8785, 8th Iberoamerican Optics Meeting and 11th Latin American Meeting on Optics, Lasers, and Applications

KEYWORDS: Statistical analysis, Visualization, Colorimetry, Solids, Visual optics, Laser optics, Tolerancing, System on a chip, Americium, Communication engineering

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PROCEEDINGS ARTICLE | February 4, 2013

Prototypical colors of skin, green plant, and blue sky

Proc. SPIE. 8652, Color Imaging XVIII: Displaying, Processing, Hardcopy, and Applications

KEYWORDS: Digital photography, Data modeling, Cameras, Databases, Image processing, Skin, Photography, Digital cameras, Computer programming, RGB color model

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SPIE Journal Paper | August 2, 2012

Image quality evaluation for smart-phone displays at lighting levels of indoor and outdoor conditions

Gong, Rui , Xu, Haisong , Wang, Binyu , Luo, M. Ronnier

OE Vol. 51 Issue 8

KEYWORDS: Light sources and illumination, Image quality, Visualization, Skin, Statistical analysis, Pixel resolution, Display technology, Optical engineering, Organic light emitting diodes, Switching

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PROCEEDINGS ARTICLE | January 25, 2012

A new method for skin color enhancement

Proc. SPIE. 8292, Color Imaging XVII: Displaying, Processing, Hardcopy, and Applications

KEYWORDS: Facial recognition systems, 3D image enhancement, Image processing, Skin, Photography, Image enhancement, Color centers, Color reproduction, Mahalanobis distance, 3D image processing

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PROCEEDINGS ARTICLE | November 15, 2011

Methods of scaling threshold color difference using printed samples

Proc. SPIE. 8335, 2012 International Workshop on Image Processing and Optical Engineering

KEYWORDS: Packaging, Visualization, Error analysis, Colorimetry, Printing, Color difference, Color vision, Spectrophotometry, Tolerancing, Communication engineering

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PROCEEDINGS ARTICLE | January 25, 2011

Preferred skin color enhancement for photographic color reproduction

Proc. SPIE. 7866, Color Imaging XVI: Displaying, Processing, Hardcopy, and Applications

KEYWORDS: Facial recognition systems, Detection and tracking algorithms, Data modeling, Image processing, Skin, Photography, Color centers, Color reproduction, Mahalanobis distance, RGB color model

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PROCEEDINGS ARTICLE | January 19, 2010

Modelling memory colour region for preference colour reproduction

Proc. SPIE. 7528, Color Imaging XV: Displaying, Processing, Hardcopy, and Applications

KEYWORDS: Modeling, Visual process modeling, Data modeling, Visualization, Databases, Skin, 3D modeling, Image quality, Image enhancement, RGB color model

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PROCEEDINGS ARTICLE | January 19, 2009

Colour naturalness metric for evaluating image quality of mobile displays

Proc. SPIE. 7241, Color Imaging XIV: Displaying, Processing, Hardcopy, and Applications

KEYWORDS: Data modeling, Visualization, Image segmentation, Skin, Colorimetry, Image quality, Cognition, Tolerancing, Color centers, Performance modeling

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PROCEEDINGS ARTICLE | January 29, 2007

Colour appearance change of a large size display under various illumination conditions

Proc. SPIE. 6493, Color Imaging XII: Processing, Hardcopy, and Applications

KEYWORDS: Visual process modeling, Data modeling, Visualization, Photography, Lamps, Colorimetry, LCDs, CRTs, Plasma display panels, Performance modeling

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This paper describes an investigation into the effect of a wide range of surround conditions on the colour appearance of test colours on a 42" plasma display panel. Experiments were conducted using surrounds including dark, indoor and outdoor conditions. Additionally the stimulus size was changed by controlling the viewing distance. The viewing conditions studied were two bright, two average, two dim and two dark surrounds. Each of the test colours was assessed by 10 observers using a magnitude estimation method. These surrounds were divided into two categories. In the first category, the surround had no effect on the displayed colours, but observers could still sense the different brightness levels of the surround. In the second category, surround introduced flare to the displayed colours together. For the first category, little visual lightness difference was shown between bright and dark, and dim and dark surround, unlike the expectation that the perceived lightness contrast may increase as the surround becomes brighter. The lightness dependency of colourfulness, however, was found to change. For the second category, the visual colour appearance of the surround conditions was plotted against measured data, CIELAB L*, C* values, to try to understand the surround effect. As the surround became brighter, the perceived dynamic range of visual lightness decreased, and the perceived colourfulness increased, more obviously in high chroma colours. In the investigation of the change of stimulus size under different surround conditions, visual colour appearance was not affected by the stimulus sizes of 2^o and 0.6^o in the dark surround. However, the difference was found in the very dark colours with a dim surround. Finally, all of visual colour appearance data were used to test the performance of the colour appearance model CIECAM02. Minor modification was accomplished to improve the colourfulness predictor, especially for the black background.

PROCEEDINGS ARTICLE | January 29, 2007

Image quality difference modelling of a mobile display

Proc. SPIE. 6494, Image Quality and System Performance IV

KEYWORDS: Image visualization, Modeling, Cell phones, Visual process modeling, Data modeling, Visualization, Image resolution, Image quality, Image enhancement, Statistical modeling

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PROCEEDINGS ARTICLE | January 11, 2006

Recent progress in computing weighting tables for calculating CIE tristimulus values

Proc. SPIE. 6033, ICO20: Illumination, Radiation, and Color Technologies

KEYWORDS: Gold, Visible radiation, Lithium, Lamps, Reflectivity, Colorimetry, Solids, Spectrophotometry, CIE 1931 color space, Standards development

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In industrial practice, it is often required that weighting tables were prepared in advance and tristimulus values can then be directly computed using summation of the products of the weights and measured reflectance values. The CIE has never provided precise procedure to calculate the weighting tables, and various discrepant methods have been used. Hence it is possible to obtain significantly different tristimulus values from the same set of spectral data. In order to overcome this problem, the American Society for Testing and Materials (ASTM Intl.) has published two sets of weighting tables known as Table 5 and Table 6 respectively. Each set includes 36 weighting tables covering 9 illuminants and two standard colorimetric observers at two wavelength intervals (10-nm and 20-nm). The weighting tables of Table 5 must be used with the reflectance corrected using the Stearns and Stearns (SS) method, and weighting tables of Table 6 must be used with the measured reflectance values without the SS correction. In practice, the illuminant used may be different from the CIE standard illuminants and users have to prepare their own weighting tables corresponding to the illuminant actually used. ASTM Intl. E2022-99 provided a standard calculation method to generate weighting tables of Table 5 for a non-standard illuminant. No standard procedure is given to calculate weighting tables of Table 6 since it consisted of Venable and Stearns correction weights, and the Venable optimum weight is computed by an iterative procedure. In this artical, we will report some recent progress in generating weighting tables; compare the performances among the weighting tables such as ASTM Intl. Tables of Table 5 and Table 6, Optimum weighting tables, Least Square weighting tables, and Direct selection tables; quantify the possible colorimetric errors for each of the tables; and finally recommend for standardization of a method for generating weighting tables.

PROCEEDINGS ARTICLE | January 11, 2006

Recent progress in computing weighting tables for calculating CIE tristimulus values

Proc. SPIE. 6033, ICO20: Illumination, Radiation, and Color Technologies

KEYWORDS: Gold, Visible radiation, Lithium, Lamps, Reflectivity, Colorimetry, Solids, Spectrophotometry, CIE 1931 color space, Standards development

Read Abstract

In industrial practice, it is often required that weighting tables were prepared in advance and tristimulus values can then be directly computed using summation of the products of the weights and measured reflectance values. The CIE has never provided precise procedure to calculate the weighting tables, and various discrepant methods have been used. Hence it is possible to obtain significantly different tristimulus values from the same set of spectral data. In order to overcome this problem, the American Society for Testing and Materials (ASTM Intl.) has published two sets of weighting tables known as Table 5 and Table 6 respectively. Each set includes 36 weighting tables covering 9 illuminants and two standard colorimetric observers at two wavelength intervals (10-nm and 20-nm). The weighting tables of Table 5 must be used with the reflectance corrected using the Stearns and Stearns (SS) method, and weighting tables of Table 6 must be used with the measured reflectance values without the SS correction. In practice, the illuminant used may be different from the CIE standard illuminants and users have to prepare their own weighting tables corresponding to the illuminant actually used. ASTM Intl. E2022-99 provided a standard calculation method to generate weighting tables of Table 5 for a non-standard illuminant. No standard procedure is given to calculate weighting tables of Table 6 since it consisted of Venable and Stearns correction weights, and the Venable optimum weight is computed by an iterative procedure. In this article, we will report some recent progress in generating weighting tables; compare the performances among the weighting tables such as ASTM Intl. Tables of Table 5 and Table 6, Optimum weighting tables, Least Square weighting tables, and Direct selection tables; quantify the possible colorimetric errors for each of the tables; and finally recommend for standardization of a method for generating weighting tables.

PROCEEDINGS ARTICLE | December 18, 2003

How scalable are gamut mapping algorithms?

Proc. SPIE. 5293, Color Imaging IX: Processing, Hardcopy, and Applications

KEYWORDS: Transparency, Photography, CRTs, Electronic imaging, Color imaging

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PROCEEDINGS ARTICLE | June 17, 2003

Mesopic color appearance

Proc. SPIE. 5007, Human Vision and Electronic Imaging VIII

KEYWORDS: Optical filters, Rods, Visual process modeling, Visualization, Transmittance, CRTs, Color vision, Electronic filtering, Performance modeling, Cones

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SPIE Conference Volume | September 13, 2002

Color Science and Imaging Technologies

PROCEEDINGS ARTICLE | September 13, 2002

CIE Division 8: a servant for the imaging industry

Proc. SPIE. 4922, Color Science and Imaging Technologies

KEYWORDS: Metrology, Digital image processing, Visualization, Image processing, Computer programming, Colorimetry, Optical communications, Associative arrays, Visual optics, Standards development

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PROCEEDINGS ARTICLE | June 6, 2002

Instrumental color control for metallic coatings