dftregistration

本程序，直接用MATLAB实现otsuf算法，并使用此算法对经典图像进行分割。(This procedure, directly with MATLAB to achieve otsuf algorithm, and use this algorithm to segment the classic image.)

粒子滤波与PHD多目标跟踪比较，三目标分别进行粒子滤波和marginalPHD滤波跟踪比较结果。(PHD particle filter multi-target tracking compared with the three goals were to filter tracking particle filter and marginalPHD comparison.)

四叉树分割 opencv 编写 读入灰度图 进行递归分割(Quadtree segmentation opencv read into grayscale prepared for recursive partitioning)

有限体积法，解决蒸发器的热传递问题，提供外形仿真，模板仿真，以及算法的开发，易于使用(Finite volume method to solve the evaporator heat transfer problems)

说明：  基于opencv的图像灰度化，将24位彩色图像重新量化为8位灰度图像(gray image)

图象分割中应用较多的ostu大津阈值法,matlab源代码(Application of image segmentation ostu more Otsu threshold method, matlab source code)

marburg.c 用Burg算法求AR模型的参数。 mar1psd.c 由AR模型参数得到功率谱。 maryuwa.c 用Levinson算法求解Yule-Walker方程以得到 阶AR模型的参数 。 (marburg.c using Burg algorithm for AR model parameters. mar1psd.c by the AR model parameters obtained power spectrum. Levinson algorithm maryuwa.c with Yule-Walker equation in order to get rank AR model parameters.)

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. )

一种新的彩色图像去噪与增强模型(A new color image denoising and enhancement model)

运用粒子群优化算法对PID控制器进行优化设计，通过matlab实现(The use of particle swarm optimization algorithm to optimize the design of PID controller, through matlab to achieve)