WNNM_code

VisualC++数字图像处理技术详解第2版光盘-第六章(VisualC++ digital image processing technology Detailed Version 2 CD- Chapter VI)

陆吾生教授的压缩感知材料，PPT格式，开始学习基础知识的同学有帮助。(Professor Lu Wusheng compressed sensing materials, PPT format.)

滤波器图像增强的matlab算法实现程序。本人自己给出的程序代码(gabor wavelet matlab algorithm. Program code given by the original author)

游程编码是是一种有效的信源编码，能够实现对二值化的图像或者二元序列进行压缩，且具有较好的压缩率。(this program is a good kind of RLC. It could come out rlc, and could compression binary sequences and image )

基于遥感光学图像的宽网道路检索，能够快速的实现半自动道路提取，给出种子点对输入方向进行快速检索(Optical remote sensing images based on a wide network of roads retrieval, the realization of fast semi-automatic road extraction, given the input direction of the seed point for rapid retrieval)

说明：  使用了色调映射算法，调整图片的灰度，更好表达原图里的信息与特征。(The hue mapping algorithm is used to adjust the gray level of the image and better express the information and features in the original image.)

水平集代码和论文，图像分割,快速有效的图像分割方法(code and paper about level set,image segmentation)

This a very useful program from a book. It is mainly used for face-detecting.It also includes interface-program.

稀疏矩阵字典的优化KSVD，本人已进行测试，效果甚佳,可以用在图像，数据的压缩和分类(Sparse dictionary optimization KSVD, I have been tested, very good results)

说明：  本文以k-means算法为背景，引入信息熵相关知识，从而实现全自动分割图像。然而在利用混合高斯模型对图像进行数据分析时，会发生一定的过拟合现象，导致我们得不到预期的聚类数目。本文设计合理的合并准则，令模型简化，有效地消除过拟合现象，使得最后得到的聚类数目与预期符合。，设计合理的准则改进了图像的全自动分割方法，使得分割结果更加优化(In this paper, k-means algorithm is used as the background, and information entropy related knowledge is introduced to realize full-automatic image segmentation. However, when the Gaussian mixture model is used to analyze the image data, there will be some over-fitting phenomenon, resulting in that we cannot get the expected number of clusters. In this paper, a reasonable merging criterion is designed to simplify the model and effectively eliminate the over-fitting phenomenon, so that the final clustering number is in line with the expectation. A reasonable criterion is designed to improve the automatic image segmentation method and make the segmentation result more optimized.)