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2 January 1998 Calibrating spectrophotometers using neural networks
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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.
© (1998) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Hsiao-Pei Lee, Guoping Qiu, and Ming Ronnier Luo "Calibrating spectrophotometers using neural networks", Proc. SPIE 3300, Color Imaging: Device-Independent Color, Color Hardcopy, and Graphic Arts III, (2 January 1998); https://doi.org/10.1117/12.298289
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