公路交通标志（限速）分割和识别的仿真系统Untitled52

使用canny算子进行边缘检测的代码，效果很好(Using the canny edge detection operator code, good results)

基于核的相关滤波跟踪方法，最新的，TPMAI上的文章，速度超级快！(This MATLAB code implements a simple tracking pipeline based on the Kernelized Correlation Filter (KCF), and Dual Correlation Filter (DCF).)

hsv的空间量化，要求输入为hsv空间中的图像。分别对h、s、v三个分量进行量化。(hsv space quantization, for admission to hsv space images. Respectively h, s, v 3 components quantified.)

OTSU图像分割快速算法。原创，附参考文献。(a faster version of Otsu s method)

说明：  图像旋转任意角度，容易操作，方便简单，效果好！(image rotating for any angle you like.)

用于摄像机标定的Harris角点检测代码码。能达到亚像素精度。压缩包内附说明文档。 (Harris corner detection for camera calibration code code. To achieve sub-pixel accuracy. The compressed package included documentation.)

直方图均衡化，包括动态直方图均衡化，全局直方图均衡化，局部直方图均衡化，以及绘制图像的直方图 其中动态直方图均衡化用A dynamic histogram equalization for image contrast enhancement 只取x=0情况(Histogram equalization, including dynamic histogram equalization, global histogram equalization, local histogram equalization, and where to draw the image histogram dynamic histogram equalization with A dynamic histogram equalization for image contrast enhancement only take x = 0 Happening)

多光源光度立体法实现，用于表面纹理处理：根据光源入射向量球表面梯度进而实现三维重建(The multi source photometric stereo method is used to realize the surface texture processing: the 3D reconstruction is realized according to the surface gradient of the incident vector of the light source.)

目前比较流行的稀疏分解重构程序，可以用在人脸识别、字典构造等方面。(Currently popular sparse decomposition and reconstruction process, can be used in face recognition, dictionary structure and so on.)

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)