CorrelationCoefficient

视觉注意工具箱，经典的Itti模型的工具箱，是GUI工具箱，有很直观的操作(visual attention toolbox for matlab ,the bos of GUI)

基于matlab的RANSAC算法，主要用来拟合二维直线，同时选出非直线的点用于后续曲线拟合。(The RANSAC algorithm based on MATLAB is used to fit two dimensional lines, and the point of non straight line is selected for the subsequent curve fitting.)

实现基于卡尔曼滤波器的多目标跟踪，可进行相关了解与研究(object tracking based on kalman)

两张图片相减 散斑干涉图像获取 Two image subtraction Two image subtraction(Two image subtraction Two image subtraction Two image subtraction)

说明：  文献 基于灰度-梯度共生矩阵的视网膜血管分割方法(Literature based on the gray- the gradient co-occurrence matrix of the retinal blood vessel segmentation method)

基于粒子滤波的目标跟踪源码，需要opencv和gsl两个库的支持，运行效率较高(Particle filter based target tracking source, two gsl library opencv and need the support of high efficiency)

基于Curvelet变换的样本块图像修复算法提高现有样本块修复算法性能。首先利用Curvelet 变换估计待修复图像的4方向特征．然后利用颜色信息与方向信息共同衡量样本块间的相似度，在此基础上构造颜色-方向结构稀疏度函数．(Based on the sample block transform image restoration algorithm Curvelet enhance existing sample block repair algorithm performance. Firstly Curvelet transform estimated to be repaired 4 directional characteristics of the image. Then use the color information and direction information together measure the similarity between the sample block, constructed on the basis of color- direction sparsity structure function.)

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)

说明：  SIFT特征点的提取和匹配，SIFT算法首先在尺度空间上进行关键点检测，确定关键点的位置和关键点所处的尺度，然后使用关键点邻域梯度的主方向作为该点的方向特征，最后由关键点邻域梯度信息生成关键点的特征向量(SIFT algorithm firstly detects the key points in the scale space, determines the location of the key points and the scale of the key points, then uses the main direction of the key point neighborhood gradient as the direction feature of the point, and finally generates the feature vector of the key points from the key point neighborhood gradient information)

基于菲涅耳全息图和离散余弦变换的_盲数字水印_技术(Blind digital watermarking _ technology based on the Fresnel hologram and the discrete cosine transform _)