leijiayanse

主要适用于图像分割，用的是马尔科夫随机场，非常好用(Mainly applicable to image segmentation, using the Markov random field, very easy to use)

实现了车辆目标的背景建模、前景检测、阴影消除、车辆跟踪(Achieve a vehicle target background modeling, foreground detection, shadow elimination, vehicle tracking)

28个vc++图像处理原程序之--对比度拉伸(28 vc++ Image processing of the original procedure- contrast stretching)

人脸检测,是采用国外的一个论文中的算法实现，如果想要论文，请发信给我(face detecting)

绘制电脑中原始人声信号的时域、频域图，并在其上加入高斯白噪声，之后设计巴特沃兹滤波器将其滤除。(The draw primitive people in the computer sound signal time domain, frequency domain, and join in on the white Gaussian noise, after design Butterworth filter to filter out.)

智能算法实现图像分割，读取一张黑白图片进行阈值分割。(Intelligent algorithms for image segmentation, read a black and white image thresholding.)

说明：  这是HALCON自带的例程，名称全部汉化。将其中文件对应index_examples_en_US.xml替换，另外operators文件夹也替换掉。(This is the routine that comes with Halcon. All names are Chinese. Index the file_ examples_ En_ US.xml Replace. In addition, the operators folder is also replaced)

PCA+SVM，对图像进行降维分类，并在yale库上测试取得比较好的效果(PCA+ SVM, dimensionality reduction of image classification, and yale library to achieve better test results)

说明：  该工具箱可实现matlab编程和zemax通信，同时借助于matlab 强大的编程能力以及zemax的光学设计能力，为光学领域工作的科研人员提供了新的途径。(This program can realize the communication between the matlab and zemax, very helpful for optical engineer and student whose major in optical science.)

说明：  边缘检测是图像处理和计算机视觉中的基本问题，边缘检测的目的是标识数字图像中亮度变化明显的点。图像属性中的显著变化通常反映了属性的重要事件和变化。这些包括（i）深度上的不连续、（ii）表面方向不连续、（iii）物质属性变化和（iv）场景照明变化。边缘检测是图像处理和计算机视觉中，尤其是特征提取中的一个研究领域。 一阶:?Roberts Cross算子，Prewitt算子，Sobel算子， Kirsch算子，罗盘算子； 二阶： Marr-Hildreth，在梯度方向的二阶导数过零点，Canny算子，Laplacian算子。(Edge detection is a basic problem in image processing and computer vision. Significant changes in image attributes usually reflect significant events and changes in attributes. These include (I) discontinuities in depth, (ii) discontinuities in surface orientation, (iii) changes in material properties, and (iv) changes in scene lighting. Edge detection is a research field in image processing and computer vision, especially in feature extraction.)