The Synthesis and Analysis of Color Images
IEEE Transactions on Pattern Analysis and Machine Intelligence
IEEE Transactions on Pattern Analysis and Machine Intelligence
An introduction to support Vector Machines: and other kernel-based learning methods
An introduction to support Vector Machines: and other kernel-based learning methods
Robust Linear and Support Vector Regression
IEEE Transactions on Pattern Analysis and Machine Intelligence
A tutorial on support vector regression
Statistics and Computing
Pattern Recognition and Machine Learning (Information Science and Statistics)
Pattern Recognition and Machine Learning (Information Science and Statistics)
Neural Networks: A Comprehensive Foundation (3rd Edition)
Neural Networks: A Comprehensive Foundation (3rd Edition)
Tongue image analysis for appendicitis diagnosis
Information Sciences: an International Journal
RGB calibration for color image analysis in machine vision
IEEE Transactions on Image Processing
Color device calibration: a mathematical formulation
IEEE Transactions on Image Processing
Multiview Video Coding Using View Interpolation and Color Correction
IEEE Transactions on Circuits and Systems for Video Technology
An introduction to kernel-based learning algorithms
IEEE Transactions on Neural Networks
Computerized facial diagnosis using both color and texture features
Information Sciences: an International Journal
Facial image medical analysis system using quantitative chromatic feature
Expert Systems with Applications: An International Journal
A high quality color imaging system for computerized tongue image analysis
Expert Systems with Applications: An International Journal
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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 (ΔE*ab) between estimated values and the target reference values. The experimental results on the colorchecker showthat the color difference is less than 5 (ΔE*ab