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An optimized tongue image color correction scheme
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机译:一种优化的舌图像色彩校正方案
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摘要
The color images produced by digital cameras are usually device-dependent, i.e., the generated color information (usually presented in RGB color space) is dependent on the imaging characteristics of specific cameras. This is a serious problem in computer-aided tongue image analysis because it relies on the accurate rendering of color information. In this paper, we propose an optimized correction scheme that corrects the tongue images captured in different device-dependent color spaces to the target device-independent color space. The correction algorithm in this scheme is generated by comparing several popular correction algorithms, i.e., polynomial-based regression, ridge regression, support vector regression, and neural network mapping algorithms. We test the performance of the proposed scheme by computing the CIE L* a* b* color difference ( Δ E ab * ) between estimated values and the target reference values. The experimental results on the colorchecker show that the color difference is less than 5 ( Δ Eab * 5 ), while the experimental results on real tongue images show that the distorted tongue images (captured in various device-dependent color spaces) become more consistent with each other. In fact, the average color difference among them is greatly reduced by more than 95.
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机译:数码相机产生的彩色图像通常取决于设备,即生成的色彩信息(通常以RGB颜色空间表示)取决于特定相机的成像特性。这是计算机辅助舌图像分析中的一个严重问题,因为它依赖于颜色信息的准确呈现。在本文中,我们提出了一种优化的校正方案,该方案将在不同设备相关颜色空间中捕获的舌头图像校正为目标设备无关颜色空间。通过比较几种流行的校正算法(即基于多项式的回归,岭回归,支持向量回归和神经网络映射算法)来生成该方案中的校正算法。我们通过计算估计值与目标参考值之间的CIE L * a * b *色差(ΔE ab *)来测试所提出方案的性能。在colorchecker上的实验结果表明,色差小于5(ΔEab * <5),而在真实舌头图像上的实验结果表明,扭曲的舌头图像(捕获在各种与设备相关的色彩空间中)变得更加一致彼此。实际上,它们之间的平均色差大大减少了95%以上。
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