Hardware realization of higher-order CMAC model for color calibration

Jar Shone Ker, Yau-Hwang Kuo, Bin-Da Liu

Research output: Contribution to conferencePaperpeer-review

10 Citations (Scopus)

Abstract

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.

Original languageEnglish
Pages1656-1661
Number of pages6
Publication statusPublished - 1995 Dec 1
EventProceedings of the 1995 IEEE International Conference on Neural Networks. Part 1 (of 6) - Perth, Aust
Duration: 1995 Nov 271995 Dec 1

Other

OtherProceedings of the 1995 IEEE International Conference on Neural Networks. Part 1 (of 6)
CityPerth, Aust
Period95-11-2795-12-01

All Science Journal Classification (ASJC) codes

  • Software

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