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简单的图形处理程序 传上来提供给更多人学习 欢迎大家交流代码(Simple graphics program available to more people pass up study welcome to share code)

实现阈值图像分割，用全局阈值，OTSU和迭代法求阈值(Achieve the threshold image segmentation, the threshold required by the global threshold, OTSU and iterative method)

用粒子滤波实现视频中人头跟踪算法，有训练好的人头分类器。()

该程序用来实现对图片的多级小波分解！提取多级分解后各子带的小波系数！(The program is used to achieve multi-level wavelet decomposition of an image! And extraction of multi-level decomposition of the wavelet coefficients of each sub-band! )

基于低频融合策略的小波图像融合算法 ，高频系数融合均采用基于像素点绝对值取大的规则(Wavelet image fusion algorithm based on low-frequency fusion strategy, integration of high-frequency coefficients are based on the absolute value of the pixel point to take a big rule)

图像分割和分形图像编码，其中图像分割的算法较多，而其只是分形编码的一部分(Image segmentation and fractal image coding, in which larger image segmentation algorithm, and its just part of fractal coding)

一篇介绍粒子滤波的资料，介绍的很详细，适合研究粒子滤波的初学者，希望对大家有帮助(Introduce a particle filter information on the very detailed study of particle filter is suitable for beginners)

说明：  LSA的第一步是要去创建词到标题（文档）的矩阵。在这个矩阵里，每一个索引词占据了一行，每一个标题占据一列。每一个单元（cell）包含了这个词出现在那个标题中的次数。例如，词”book”出现在T3中一次，出现在T4中一次，而”investing”在所有标题中都出现了一次。一般来说，在LSA中的矩阵会非常大而且会非常稀疏（大部分的单元都是0）。这是因为每个标题或者文档一般只包含所有词汇的一小部分。更复杂的LSA算法会利用这种稀疏性去改善空间和时间复杂度。(The Little Book of Common SenseInvesting: The Only Way to Guarantee Your Fair Share of StockMarket Returns ,)

说明：  在图像恢复技术中，总变分最小化是一种以保存图像细节为目标的规整化复原方法。本算法基于总变分最小化的思想，采取交替最小化的精确迭代策略来对图像和点扩展函数同时进行恢复。实验表明，该算法在噪声水平较高的情况下仍能对图像进行稳健的恢复。(Total variation image restoration techniques is a goal to save the image detail regularization recovery method. Algorithm based on total variation minimization thinking, take the alternating minimization accurate iteration strategy to simultaneously restore the image and point spread function. The experiments show that the algorithm in the case of high noise levels can still robust recovery image.)

说明：  SVM支持向量机在图像分割中的应用，包含Matlab源代码和SVM所用函数。(The application of SVM support vector machine in image segmentation, including Matlab source code and functions used by SVM.)