Pattern Recognition Letters - Special issue: Colour image processing and analysis
Kernel Methods for Pattern Analysis
Kernel Methods for Pattern Analysis
Unsupervised colour image segmentation applied to printing quality assessment
Image and Vision Computing
Ink flow control by multiple models in an offset lithographic printing process
Computers and Industrial Engineering
Expert Systems with Applications: An International Journal
Soft computing for assessing the quality of colour prints
IEA/AIE'06 Proceedings of the 19th international conference on Advances in Applied Artificial Intelligence: industrial, Engineering and Other Applications of Applied Intelligent Systems
Assessing print quality by machine in offset colour printing
Knowledge-Based Systems
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We present an option for CCD colour camera based ink density measurements in newspaper printing. To solve the task, first, a reflectance spectrum is reconstructed from the CCD colour camera RGB values and then a well-known relation between ink density and the reflectance spectrum of a sample being measured is used to compute the density. To achieve an acceptable spectral reconstruction accuracy, the local kernel ridge regression is employed. The superiority of the local kernel ridge regression over the global regression and the popular ordinary polynomial regression is demonstrated by experimental comparisons. For a database consisting of 250 colour patches printed on newsprint by an ordinary offset printing press, the average spectrum reconstruction error of @DE@?=0.733 and the maximum error @DE"m"a"x=3.29 was obtained. Such an error proved to be low enough for achieving the average ink density measuring error lower than 0.01D.