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这是基于张亶，陈刚《偏微分方程的图像处理》书的实验及程序(image processing )

说明：  快速的人脸特征提取算法KPCA，比普通的pca特征提取算法在效率上好了不少(Fast facial feature extraction algorithm KPCA, than ordinary PCA feature extraction algorithm in the efficiency of a good many)

用matalab编译的winner滤波法对图像进行去噪处理，利用了FFT的方法(Compiled using matalab winner filtering method to deal with the noise of image, using the FFT method )

IDL中的批量重采样，可以省很多时间，根据自己的需求，采样成不同分辨率的图像。(IDL in batch resampling, you can save a lot of time, according to their needs, a different sampling resolution images.)

[SIAM]Nonlocal Active Contours该文件包含论文及代码，主要内容是在水平集图像分割中引入non-local的性质，构造能量泛函，进行图像分割(this file include both paper and code, the main idea is utilize the non-local peperty in level set method for image segmentation )

对测井曲线进行重构的程序，可以充分利用测井特征曲线所反应的岩性特征信息，完成多信息的融合(Log curve reconstruction procedures)

关于偏微分方程进行图像处理的电子书,对于学习动态轮廓实现图像的分割非常有用(A book about PDE and image processing, it is very useful for image processing learning)

根据D. Comaniciu, P. Meer: Mean Shift: A robust approach toward feature space analysis 以及 C. Christoudias, B. Georgescu, P. Meer: Synergism in low level vision.这两篇文献提供的方法编写的图像分割代码，作者是 Chris M. Christoudias, Bogdan Georgescu，代码经我看了后加了丰富的中文注释，希望可以给各位带来阅读上的方便。 基于meanshift的聚类分割方法包括滤波、区域融合等操作，通过调整sigma和sigmar来调整分割效果。(According to D. Comaniciu, P. Meer: Mean Shift: A robust approach toward feature space analysis, and C. Christoudias, B. Georgescu, P. Meer: Synergism in low level vision. These two documents prepared by the methods provided by image segmentation code , the author is Chris M. Christoudias, Bogdan Georgescu, after I read the code, add a rich Chinese notes, hoping to bring you the convenience of reading. Segmentation method based on clustering meanshift including filtering, regional integration and other operations, and by adjusting the sigma sigmar to adjust segmentation results.)

模式识别应用范围广泛，在机器学习，图像处理，运动目标检测与跟踪方面具有较大的应用价值。是本好书，值得推荐。(pattern recognition applied extensively in machine learning, image processing, moving target detection and tracking with greater value. Is a book worth recommending.)

梯度矢量扩散控制实现边缘保持的彩色图像去噪(Gradient vector diffusion denoising realize control color image edge preserving )