VesselTest

图像处理的Matlab系统，包含有巴特沃兹滤波、图像分割、高斯滤波、图像增强、图像锐化，图像旋转等功能。(Matlab image processing system, including a Butterworth filter, image segmentation, Gaussian filter, image enhancement, image enhancement, image rotation and other functions.)

说明：  结合Retinex和CLAHE图像去雾，Retinex有ssr，msr，msrcr。(Combine Retinex and clean image to remove fog)

实现肺实质初始分割及采用滚球法（形态学开运算）进行修补肺实质轮廓凹陷。(Achieve initial segmentation and the use of the pulmonary parenchyma ball method (morphological opening operation) to repair the lung parenchyma contour depression.)

基于图割方法的图像分割程序代码，希望对您有帮助哦！(graph cut method to segment images, it is nessary for you to have a look if you are doing image segmentation.)

立体匹配中的单目标定,输入图片，得出结果，官方实例(Monocular calibration)

工具条图标产生器，可以产生各种工具条图标(Toolbar icon generator, can produce a variety of toolbar icons)

基于DCT方法的数字水印论文5篇，对学习基于DCT方法的数字水印初学者会有帮助。可以了解基于DCT的水印基本方法。(DCT-based method of digital watermarking papers 5, to learn methods of digital watermarking-based DCT would be helpful for beginners. DCT-based watermark can learn the basic method.)

说明：  Invensense公司的modm实例代码example。 初始化以及IIC的引用示例。(The example of modm from Invensense. IIC initiation and configuration initiation.)

在对多光谱遥感影像进行大气校正处理以后，可以利用K_T变换矩阵对其进行水体提取，该算法效果较好(After the atmospheric correction of the multispectral remote sensing image, the water body can be extracted by using the k transform matrix, and the effect of the algorithm is good.)

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. )