The process of eliminating color errors from the gamut mismatch, resolution conversion, quantization and non-linearity between scanner and printer is usually recognized as an essential issue of color reproduction. To efficiently calibrate the non-linearity between scanning/printing devices, we present a linear systolic array architecture to realize the higher-order CMAC neural network model and propose an extended direct weight cell address mapping scheme for weight retrieving. This mapping scheme exhibits fast computation speed in generating weight cell addresses. Some experiments are performed to evaluate the approximation capability of the higher-order CMAC neural network models. It is shown that the CMAC model behaves well for those trained regions over the input space and exhibits smooth approximation for those untrained regions over the input space.
|Number of pages||6|
|Publication status||Published - 1995 Dec 1|
|Event||Proceedings of the 1995 IEEE International Conference on Neural Networks. Part 1 (of 6) - Perth, Aust|
Duration: 1995 Nov 27 → 1995 Dec 1
|Other||Proceedings of the 1995 IEEE International Conference on Neural Networks. Part 1 (of 6)|
|Period||95-11-27 → 95-12-01|
All Science Journal Classification (ASJC) codes