demo_mtjsrc_OxfordFlower_17

目前最重要的图像变化检测评价标准：roc曲线，以及roc曲线下面积。(ROC curve of image change detection)

【实例简介】

对输入的图像帧感兴趣区域进行逆透视投影变换，得到俯视图。(The input of the image frame of interest in the area of the inverse perspective projection transform, get a top view. InversePerspectiveMapping)

说明：  MATLAB 给图像添加各种噪声：如椒盐噪声，高斯噪声、乘性噪声等等。(MATLAB add all kinds of noise to the image: If salt and pepper noise, Gaussian noise, multiplicative noise and so on.)

检测前跟踪 粒子滤波 概率假设密度 高斯混合粒子 平滑(Pre-test tracking particle filter probability hypothesis density Gaussian mixture particle smoothing)

说明：  高斯混合模型实现图像分割分类，包含中文注释。(Gaussian mixture model implements image segmentation and classification, including Chinese annotation.)

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. )

基于小波变化的边缘检测程序 matlab的m代码 效果不错(Wavelet transform edge)

MPPT algorithm INC method

SIFT特征提取演算法(包含匹配以及除错机制RANSAC)-可用于两张影像之特征点匹配 (SIFT feature extraction algorithm (including the match, as well as debug mechanisms RANSAC)- can be used for two images of the feature points matching)