UFCLS

图象去污渍，mask为污渍图象，另一个为原图或受损图象(Image to remove stains, mask for stained images, and the other for original or damaged images.)

用于c#获取多个摄像头影像！

拉普拉斯边缘检测与图像增强，Matlab6.5实现的.m文件，附有原图、检测到的边缘图和增强后的图。(Laplacian edge detection and image enhancement, Matlab6.5 of implementation. M file, with image, detected and enhanced edge map after the map.)

对嵌入水印的图象进行不同的攻击 添加噪音，滤波，剪切，压缩，旋转45度(Watermark on the images embedded in a different attack to add noise, filter, shear, compression, rotating 45 degrees)

MATLAB工具sift实现图像拼接，上下拼接(MATLAB tool sift image stitching, stitching up and down)

进行简单的数字图像处理，用MATLAB进行图像旋转，灰度，亮度调节(Perform simple digital image processing)

内容包括高斯混合概率假设密度滤波器仿真程序，GM-PHD滤波器提供了一种PHD滤波器工程化的方法，在无数据关联的情况下进行目标跟踪。(This algorithm is extended to accommodate mildly nonlinear target dynamics using approximation strategies from the extended and unscented Kalman filters.)

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)

极坐标和平面坐标的转换，在图像处理中可以很好解决在一个坐标下不能解决的问题。(Polar coordinates and plane coordinates conversion, in image processing can be solved in a coordinate system can not solve the problem.)

说明：  GVF-SNAKE分割 解决了收敛速度慢、收敛不理想的问题(Image segmentation using GVF Snake)