Robust colour calibration of an imaging system using a colour space transform and advanced regression modelling

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• 作者：Patrick Jackman ; Da-Wen Sun ; ^{dawen.sun@ucd.ie} ; ; Gamal ElMasry
• 关键词：Computer vision ; Image processing ; Colour calibration ; Colour space ; L*a*b* ; Colour transform
• 刊名：Meat Science
• 出版年：2012
• 期刊代码：78_03091740
• 类别：abs
• 出版时间：August, 2012
• 卷：91
• 期：4
• 页码：402-407
• 文件大小：511 K

摘要

A new algorithm for the conversion of device dependent RGB colour data into device independent L*a*b* colour data without introducing noticeable error has been developed. By combining a linear colour space transform and advanced multiple regression methodologies it was possible to predict L*a*b* colour data with less than 2.2 colour units of error (CIE 1976). By transforming the red, green and blue colour components into new variables that better reflect the structure of the L*a*b* colour space, a low colour calibration error was immediately achieved (螖E_CAL = 14.1). Application of a range of regression models on the data further reduced the colour calibration error substantially (multilinear regression 螖E_CAL = 5.4; response surface 螖E_CAL = 2.9; PLSR 螖E_CAL = 2.6; LASSO regression 螖E_CAL = 2.1). Only the PLSR models deteriorated substantially under cross validation. The algorithm is adaptable and can be easily recalibrated to any working computer vision system. The algorithm was tested on a typical working laboratory computer vision system and delivered only a very marginal loss of colour information 螖E_CAL = 2.35. Colour features derived on this system were able to safely discriminate between three classes of ham with 100%correct classification whereas colour features measured on a conventional colourimeter were not.