matlab

K近邻(KNN)平滑滤波器算法，包括椒盐噪声和高斯白噪声下平滑处理的结果(K-Near Neighbor (KNN) Smoothing Filter Algorithms, Including Smoothing Processing Results under Salt and Pepper Noise and Gauss White Noise)

说明：  《数字图像处理（第三版）》课后习题答案，作者冈萨雷斯(Answers to Exercises after Class of Digital Image Processing (Third Edition),author Gonzalez)

利用NSCT对脑CT图像增强的例子 ，代码可编辑，本例为适用于灰度图像(Using NSCT for brain CT image enhancement example, the code can be edited. This example applies to grayscale images.)

车牌识别，图像分割出车牌，然后进行具体识别，matlab源代码(vehicle license plate recognition)

一段音频处理程序，FFT+降噪，简单的试验适合初学者(An audio processor, FFT+ noise reduction, a simple test for beginners)

照片比对、视频采集人脸识别 运行环境x64，自行申请虹软的APP_ID和SDKKEY替换App.config里的相关内容

闻婷实验板的四通道视频采集实验，通过键盘控制四个通到采集图像，将采集的图像在同一台显示器上显示(Wen Ting experimental four-channel video capture board experiments, four-pass to collecting image, the collected image displayed on the same monitor keyboard control)

radarsat1 的成像程序，包括源码，是个很好用那个的算法(radarsat1 imaging procedures, including source code, is a good use of that algorithm)

hslogic算法仿真，基于matlab的灰度共生算法的集成电路裂痕修复仿真(Hslogic algorithm simulation, integrated circuit crack repair simulation based on MATLAB gray level co-occurrence algorithm)

为了提高数字图像中椭圆检测的效率和准确性,提出了一个基于最小二乘法的改进的随机椭圆检测算法. 该算法随机选取图像中的3 个边缘点,在以这3 个点为中心的窗口内,从边缘点中拟合出可能椭圆,并通过随机选 取的第4 个边缘点来确认可能椭圆. 利用直接最小二乘法椭圆拟合的特性,引入可能椭圆边缘点收集和椭圆重新 拟合的迭代过程来提取最终的椭圆参数. 通过对含有不同噪声的仿真图片和包括残缺椭圆的实际图片的实验表 明,新算法的改进是有效的. 与原算法相比,新算法降低了对参数的依赖性,提高了检测的速度、稳定性和准确性, 同时保留了原算法的抗噪声能力.(: An imp roved randomized ellip se detection algorit hm based on least square approach was proposed to enhance t he efficiency and accuracy of ellip se detection in digital images. This algorit hm randomly se2 lect s three edge point s in t he image , and t hen uses least square approach to fit all t he edge point s in three windows , which are defined by t he t hree edge point s. The forth edge point is randomly selected to judge whet her a possible ellip se exist s in t he image. Utilizing the characteristic of direct least square fit ting of ellip se , an iteration process of edge point collecting and ellip se refit ting of possible ellip se was int roduced to ext ract t he final ellip se’s parameters. Artificial images wit h different level s of noise and nature images containing incomplete ellip ses were employed to test t his algorithm. Experimental result s show that t he improvement s are notable. Compared with the original algorit hm , t he propo sed algorit hm reduces)