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Cascade-CMAC neural network applications on the color scanner to printer calibration

By: King-Lung Huang; Hsin-Chia Fu; Shu-Cheng Hsieh;

1997 / IEEE / 0-7803-4122-8


This item was taken from the IEEE Periodical ' Cascade-CMAC neural network applications on the color scanner to printer calibration ' This paper presents an application of using a cascade-CMAC (cerebellar model articulation controller) neural network to solve some color calibration problems, which include color differences induced from gamuts mis-match and the nonlinear transformation characteristics between color scanning input devices and color printing output devices. For this purpose, we proposed a scalable learning architecture ""cascade-CMAC"" to implement an adaptive color calibration system. By analyzing the preliminary learning situation, the scalable architecture can dynamically create a new learning unit to better represent a finer color resolution, so that the learning capacity as well as the color details of the system can be greatly improved. From the experimental results, the proposed cascade-CMAC architecture can improve the rate of convergence and also can adjust the learning architecture effectively. The learning speed can be 2/spl sim/4 times faster than the conventional CMAC. The effectiveness of this neural network has been tested by observing the differences between the calibrated and the un-calibrated output on a number of known samples. By using the Macbeth color-checker which contains 24 color patches as benchmark, the average color differences between the original and the calibrated print-out is improved from 15 /spl Delta/E/sub ab/ to 8 /spl Delta/E/sub ab/ under the 3 /spl Delta/E/sub ab/ convergent criterion for training. The calibration performance is somewhat significant.