SURF

本实验要求编写一个计算压缩-解压缩图像的均方根误差、均方信噪比的程序。该程序是一个通用程序。编写程序产生图示的结果。使用上面的保真度准则程序计算可视信息的损失特性。(Prepared in this experiment, a calculation of compression- decompression of the root-mean-square error of image, mean square signal to noise ratio of the procedure. The procedure is a common procedure. Picture shows the preparation of the results of procedures. The use of the above criteria for the fidelity of calculated loss characteristics of visual information.)

利用传统的SVM-HOG算法，进行行人检测(HOG-SVM algorithm for pedestrian detection)

说明：  标准三步搜索法，用matlab实现，经过实验效果还可以(Standard three-step search method, using matlab to achieve, after the experimental effect can also be)

高分辨率遥感影像的支持张量机分类方法，此方法能够利用少量的训练样本实现更优的分类精度。(Support tensor machines resolution remote sensing image classification method, and this method can use a small amount of training samples to achieve better classification accuracy.)

书上的简单DCT图像处理程序，最基本的DCT，简单，价值低(Book on the simple DCT image processing program, the most basic DCT, simple, low value)

讨论了OpenCV(Open Source Computer Vision Library)相对于现有的计算机视觉软件包所具有的优势，描述了OpenCV的 环境配置、数据定义、图像元素访问方式。OpenCV成为一种源码开放、包含丰富的高级数学计算函数、图像处理函数和计算机视觉 函数、不断更新和平台无关性的计算机视觉软件包。(Discussed the OpenCV (Open Source Computer Vision Library) relative to the existing computer vision, the advantages of packages, description of OpenCV environment configuration, data definition, the image element access mode. OpenCV become an open-source, contains a wealth of advanced mathematical calculations, functions, image processing functions, and computer vision functions, continuously updated, and platform-independent package of computer vision.)

灰度质心法提取图像圆点中心坐标程序源代码(Extracted gray-scale image centroid coordinates of the dot center of the source code)

VIDEO COMPRESSIONS BEST TUTORIAL

图像三维重建 matlab环境 可用于各种图像(Image three-dimensional reconstruction of the environment can be used for a variety of images matlab)

说明：  改进LBP纹理提取以及纹理分类，主要包括三部分测试：纹理提取、分类和分割(Improved LBP texture extraction and texture classification, including three parts of testing: texture extraction, classification and segmentation)