Niblack

Image Indexing Using Color Correlogram

说明：  基于HARRIS特征点的数字水印的嵌入算法，以及数字水印的检测算法(Watermark embedding and Detection in image based on Harris features points)

很详细的Harris算法讲解~~~包括非最大值抑制的算法~自适应~~(Harris algorithm is very detailed explanation ~ ~ ~ including non-maximum suppression algorithm ~)

说明：  也是水平集方法的一个matlab源代码 基于边缘信息的(level is a collection of source code based on Matlab Edge Information)

vc++图像开发的附书光盘,包括众多的实例程序,每个程序都可以是功能完备的工具软件.(visual c++ image develop source code and instances)

两个图像质量评价函数，psnr峰值信噪比，正则均方误差nmse，能可用于去噪图像和压缩图像的质量评价，很有用啊。(Two image quality evaluation of the function. Including psnr PSNR, it is mean-square error nmse other. Images can be used for de-noising and compression of image quality evaluation.)

这个程序对图像进行arnold变换，把二值图像置乱，然后一定的周期之后进行复原。(This procedure is arnold image transformation, the binary image scrambling, and then after a certain recovery cycle.)

基于区域阈值的图像分割方法研究，迭代阈值法分割技术(Regional threshold-based image segmentation method, iterative threshold segmentation)

质心点求取，目标进行形跟踪，跟跟踪物体。希望高手指点(Centroid strike, shaped track)

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)