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Proceedings Paper

Calibrating spectrophotometers using neural networks
Author(s): Hsiao-Pei Lee; Guoping Qiu; Ming Ronnier Luo
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Paper Abstract

This paper describes a neural network based method to improve inter-instrument agreement. For each instrument, a three-layer feed-forward neural network was trained using standard reference materials with known reflectance values. The BCRA- NPL tiles were measured by each instrument. The neural network models were derived to correct the measured data in agreement with those measured by the CERAM (standard). Twelve BCRA-NPL tiles were used for training and 32 glossy paint samples selected from OSA Uniform Color Scales were used to test the method. Experimental results for two different spectrophotometers are presented which show good improvement in inter-instrument agreement for both the training and testing samples.

Paper Details

Date Published: 2 January 1998
PDF: 9 pages
Proc. SPIE 3300, Color Imaging: Device-Independent Color, Color Hardcopy, and Graphic Arts III, (2 January 1998); doi: 10.1117/12.298289
Show Author Affiliations
Hsiao-Pei Lee, Univ. of Derby (United Kingdom)
Guoping Qiu, Univ. of Derby (United Kingdom)
Ming Ronnier Luo, Univ. of Derby (United Kingdom)


Published in SPIE Proceedings Vol. 3300:
Color Imaging: Device-Independent Color, Color Hardcopy, and Graphic Arts III
Giordano B. Beretta; Reiner Eschbach, Editor(s)

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