Soft computing for assessing the quality of colour prints

Authors:
Antanas Verikas;Marija Bacauskiene;Carl-Magnus Nilsson
Affiliations:
Intelligent Systems Laboratory, Halmstad University, Halmstad, Sweden;Department of Applied Electronics, Kaunas University of Technology, Kaunas, Lithuania;Intelligent Systems Laboratory, Halmstad University, Halmstad, Sweden
Venue:
IEA/AIE'06 Proceedings of the 19th international conference on Advances in Applied Artificial Intelligence: industrial, Engineering and Other Applications of Applied Intelligent Systems
Year:
2006

Citing 4
Cited 1

Mean Shift: A Robust Approach Toward Feature Space Analysis

IEEE Transactions on Pattern Analysis and Machine Intelligence
Kernel Methods for Pattern Analysis

Kernel Methods for Pattern Analysis
Brief paper: Estimating ink density from colour camera RGB values by the local kernel ridge regression

Engineering Applications of Artificial Intelligence
Unsupervised colour image segmentation applied to printing quality assessment

Image and Vision Computing

Review: Advances in computational intelligence-based print quality assessment and control in offset colour printing

Expert Systems with Applications: An International Journal

Quantified Score

Hi-index	0.00

Visualization

Abstract

We present a soft computing techniques based option for assessing the quality of colour prints. The values of several print distortion attributes are evaluated by employing data clustering, support vector regression, and image analysis procedures and then aggregated into an overall print quality measure using fuzzy integration. The experimental investigations performed have shown that the print quality evaluations provided by the measure correlate well with the print quality rankings obtained from the experts. The developed tools are successfully used in a printing shop for routine print quality control.