Optical Lens Design with Short-Focus and Ultra-Wide FOV

Jiyan, 张继艳 Zhang; Yuanqing, 黄元庆 Huang; Xianguo, 孟宪国 Meng; Haifeng, 林海峰 Lin

doi:10.3788/lop50.102202

Cited by 3 publications

(1 citation statement)

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“…There are a lot of device color characteristics methods at present. These methods are mainly classified as traditional methods (3D-looked up table and polynomial regression) [2,3] and artificial intelligence methods [4][5][6][7]. For the highly nonlinear of color space conversion, so many scholars put their research emphasis on the artificial intelligence methods.…”

Section: Introductionmentioning

confidence: 99%

Research on Robustness of Color Device Characteristic Methods Based on Artificial Intelligence

Chuan

Shi³

2012

AMM

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The nature of device color characteristic methods is the mutual conversion of device-dependent color space and device-independent color space. This paper does the comparative study on the robustness of some color space conversion methods which are based on fuzzy control, dynamic subspace divided BP neural network identification method, and fuzzy and neural identification method, by defining the robustness of color space conversion model and evaluation method. The result shows that the device color characteristic methods which are based on fuzzy and neural identification method can make the feature of BP neural network combine with fuzzy control to greatly improve the robustness of model.

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