radiometric-correction-

这是本人发表的目标跟踪方面论文使用的代码，是基于随机假设概率滤波的多目标跟踪算法。(This is the code used by the target tracking paper I published, filter multi-target tracking algorithm based on the probability of a random assumption.)

说明：  一个声纹识别的全代码,包括语音信号的预处理,建模,和识别(Voiceprint identification of a full code, including the speech signal pre-processing, modeling, and identification)

多尺度retinex算法的matlab程序，用着不错(The matlab program of multi-scale Retinex algorithm is well used)

说明：  评估指标为点目标距离向和方位向峰值坐标、峰值旁瓣比、一维积分旁瓣比、剖面图(The evaluation indexes are peak coordinates, peak sidelobe ratio, one-dimensional integral sidelobe ratio and profile of point targets in range and azimuth directions.)

图像拼接的Harris 算法，精确有效，实现图像无缝拼接，多幅图片显示，谢谢，希望得到采纳(image mosaicHarris algorithm for image mosaic, accurate and effective, seamless image mosaic)

c# 对任意网页进行截图 抓取

主成分分析程序包，包括主成分分析和独立主成分分析两个程序源代码。(Principal component analysis package, including principal component analysis principal component analysis and independent source code for both procedures.)

java语言开发 二维码的生成与解析

脑电信号 分析 fast ica去噪，去除心电，眼电，肌电，效果不错(fast ica for eeg)

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. )