Wang Xingzheng, Zhang David
Biometrics Research Center, Department of Computing, The Hong Kong Polytechnic University, Kowloon, Hong Kong.
IEEE Trans Inf Technol Biomed. 2010 Nov;14(6):1355-64. doi: 10.1109/TITB.2010.2076378. Epub 2010 Sep 13.
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 (∆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 (∆E(ab)(*) < 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%.
数码相机生成的彩色图像通常依赖于设备，即生成的颜色信息（通常以RGB颜色空间呈现）取决于特定相机的成像特性。在计算机辅助舌象分析中，这是一个严重的问题，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，将在不同依赖于设备的颜色空间中捕获的舌象校正到目标独立于设备的颜色空间。该方案中的校正算法是通过比较几种流行的校正算法生成的，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。我们通过计算估计值与目标参考值之间的CIE L()a()b()色差（∆E(ab)()）来测试所提方案的性能。在色卡上的实验结果表明，色差小于5（∆E(ab)(*) < 5），而在真实舌象图像上的实验结果表明，（在各种依赖于设备的颜色空间中捕获的）失真舌象图像彼此之间变得更加一致。事实上，它们之间的平均色差大幅降低了95%以上。
