work

方向检测的粒子滤波程序，内含重采样算法。(The direction of the particle filter detection procedures, including re-sampling algorithm.)

基于非局部图像去噪模型的遥感图像融合的变分方法。该变分方法采用梯度下降法。(Variational method of remote sensing images nonlocal image denoising model fusion. The variational method using gradient descent.)

基于MATLAB的鱼眼镜头几何矫正程序，将鱼眼镜头下产生的图像变换为正常的图像(Geometric correction procedures based on MATLAB fisheye, fisheye lens image to a normal image)

说明：  很有用的图像显示的源程序，是图像处理方面比较好用的程序！(very useful images of the source, the image processing is more user-friendly procedures.)

对图像灰度二值化，提取图像中某个色彩的部分并分割(The image is binarized and the part of a color in the image is extracted and segmented)

【高斯核函数改进 参考价值1】LBF活动轮廓模型的改进.pdf【高斯核函数的改进 参考价值5】单核和多核相关向量机的比较研究.pdf([Gaussian kernel improved reference value 1] .pdf LBF improved active contour models comparative study [.pdf Gaussian kernel improvements reference 5] single and multi-core related vector machine)

说明：  基于偏斜度的视频水印源程序, 一个高信息量的水印程序,对大信息量水印有很好的参考价值(Skewness-based video watermarking source, a high amount of information watermark procedures, the large amount of information watermark have a good reference value)

本算法在Matlab2008b 环境下实现。包括main,san 和ssd 三个函数。 这次实现的算法并不是比较两个已经知道的点是否匹配，而是已知一个图形中的特征点，在另外一个图像中找到与其最匹配的点，匹配度用SAD 或者SSD 来度量。 main.m 是程序的入口，包括生成和读入实验数据，调用sad 函数和ssd 函数求匹配点，最后绘 制出最后的结果。sad.m 是用sad 度量方法在另一图中求解匹配点的函数实现，有3 个参数 y=sad(x,image1,image2)。其中x 是代匹配的数据，image1 是图像1 数据，image2 是图像2 数据。 从理论上分析，ssd 比sad 算法要复杂一点，经过测试，在一幅640*480 的图像中寻找10 个匹配点数据SAD 用时25.062519 秒，SSD 用时25.291432 秒。(The algorithm Matlab2008b environment to achieve. Including the main, san, and ssd three functions. The implementation of the algorithm is not the point of comparing two matches already know, but the known feature points in a graphic, an image found in the other match with the most points, matching measured with the SAD or SSD. main.m is the entry, including the generation and reading test data, call the sad ssd function evaluation functions and matching points, and finally draw the final result. sad.m measure is sad figure in another match point in the function implementation to solve, there are three parameters y = sad (x, image1, image2). Where x is the generation of matching data, image1 is the data image 1, image2 is the image 2 data. From the theoretical analysis, ssd little more complicated than the sad algorithm, tested in a 640* 480 images of 10 match points in the search for data using time 25.062519 seconds SAD, SSD with time 25.291432 seconds. )

说明：  自组织神经网络SOM的算法，经过调试可以用(Self-organizing neural network SOM algorithm can be used after debugging)

matlab最小二乘法曲面拟合程序可以得到函数具体解析式(Matlab least squares surface fitting program can get the specific analytic expression of the function)