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本代码实现的功能：24位位图（BMP）的灰度化。具体实现过程：打开图像文件,找到数据区(每3个字节代表一个象素的R,G,B值),循环扫描,根据RGB的权重,计算出灰度值. 如果要得到256色灰度图,则要新建一个调色板,修改信息头和文件头.(Realize the function of the code: 24-bit bitmap (BMP) of the gray. Concrete realization of the process: Open the image file, locate the data area (3 bytes per pixel on behalf of a R, G, B values), cyclic scan, according to the weight of RGB, gray value is calculated. If you want to get 256 colors grayscale, would create a new palette, modify the header and file header information.)

sift匹配算法 尺度不变变换 特征描述子 特征点匹配 (sift match)

暗通道去雾代码，使用c++实现 根据cvpr论文改写(Channel code to the dark fog)

基于自动种子选择的区域生长法，克服了传统区域法的很多缺点(Region growing method based on automatic seed selection, to overcome the many shortcomings of the traditional regional law)

亚像素级cvFindCornerSubPix角点检测，可较为精确地对角点进行检测(The subpixel cvFindCornerSubPix corner detection)

一款完全免费的专业级屏幕捕捉软件，能够让您得心应手地捕捉到需要的屏幕截图。捕捉图像方式灵活，主要可以捕捉整个屏幕、活动窗口、选定区域、固定区域、选定控件、选定菜单、选定网页等，图像输出方式多样，主要包括文件、剪贴板、画图和打印机。软件具有捕捉光标、设置捕捉前延时、显示屏幕放大镜、自定义捕捉热键、图像文件自动按时间或模板命名、捕捉成功声音提示、重复最后捕捉、预览捕捉图片、图像打印、图像裁切、图像去色、图像反色、图像翻转、图像旋转、图像大小设置、常用图片编辑、墙纸设置等功能。捕捉到的图像能够以保存图像文件、复制到剪贴板、输出到画图、打印到打印机等多种方式输出。 (A completely free professional screen capture software that allows you to be able to adequately capture the need for a screenshot. A flexible way to capture the image, the main can capture the entire screen, active window, selected regions, fixed region, the selected control, the selected menu, select Web pages, images, various output methods, mainly including documents, clipboard, drawing and printers. Software has to capture the cursor, set the delay before the capture, display screen magnifier, custom capture hotkeys, image file names automatically by time or templates to capture the success of the voice prompts, repeat last capture, preview captured pictures, image print, image cut , the image to the color, the anti-color images, image flip, image rotation, image size setting, commonly used photo editing, wallpaper settings and other functions. Images can be captured to save the image file, copy to the clipboard, export to the drawing, print to a printer output of a variety of ways)

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.)

说明：  平台基于visual studio2015，使用opencv函数实现双边滤波，进行图像处理(bilateral Filter based on opencv)

混合高斯(Mixture of Gaussian, MOG) 背景建模算法和Codebook 背景建模算法被广泛应用于监控视频的运动目标检测问题, 但混合高斯的球体模型通常假设RGB 三个分量是独立的, Codebook 的圆柱体模型假设背景像素值在圆柱体内均匀分布且背景亮度值变化方向指向坐标原点, 这些假设使得模型对背景的描述能力下降. 本文提出了一种椭球体背景模型, 该模型克服了混合高斯球体模型和Codebook 圆柱体模型假设的局限性, 同时利用主成分分析(Principal components analysis, PCA) 方法来刻画椭球体背景模型, 提出了一种基于主成分分析的Codebook 背景建模算法. 实验表明, 本文算法不仅能够更准确地描述背景像素值在RGB 空间中的分布特征, 而且具有良好的鲁棒性.(The background modeling algorithm of mixture of Gaussian (MOG) and codebook is widely used in moving object detection of surveillance video. However, the ball model of MOG usually assumes that the three components of RGB are independent, while the cylinder model of codebook assumes that the value of background pixel is distributed uniformly within the cylinder and the changing direction of brightness points to the origin of the coordinate system. These assumptions reduce the descriptive capability for background modeling. Therefore, the paper proposes an ellipsoid- based background model, which overcomes the MOG and codebook0s limitations. By using principal component analysis to depict the ellipsoid background model, a novel PCA-based codebook background modeling algorithm is proposed. Experiments show that this algorithm can not only give more accurate description of the distribution of background pixels but also have a better robustness.)

本文以自主驾驶车辆为实际应用背景．提出了一种改进的ＲＲＴ（快速随机搜索树）路径规划算法．该路 径规划算法将非完整性约束条件与双向多步扩展ＲＲＴ搜索算法相结合，在提高搜索效率的同时保证了规划路径的可 行性．同时将路径点作为Ｂ样条基函数的控制点，用三次Ｂ样条函数来拟合控制点生成平滑可跟踪的路径．通过在平 面障碍物环境下实验，验证了该算法的有效性．(ＡｉｍｉｎｇａｔｔｈｅａｐｐｌｉｃａｔｉｏｎｏｆＡＬＶ（ａｕｔｏｍａｔｉｃｌａｎｄｖｅｈｉｃｌｅ），ａｎｉｍｐｒｏｖｅｄＲＲＴ（ＲａｐｉｄｌｙＲａｎｄｏｍ?ｅｘｐｌｏｒｉｎｇ Ｔｒｅｅｓ）ｐａｔｈｐｌａｎｎｉｎｇａｌｇｏｒｉｔｈｍ ｉｓｐｒｏｐｏｓｅｄ．Ｔｈｅａｌｇｏｒｉｔｈｍ ｃｏｍｂｉｎｅｓｔｈｅｎｏｈｏｌｏｎｏｍｉｃｃｏｎｓｔｒａｉｎｔｓｏｆｖｅｈｉｃｌｅｗｉｔｈｄｏｕｂｌｅｅｘｔｅｎｄ ＲＲＴｓ，ｗｈｉｃｈｎｏｔｏｎｌｙｉｍｐｒｏｖｅｓｔｈｅｅｆｆｉｃｉｅｎｃｙｏｆｓｅａｒｃｈｉｎｇｂｕｔａｌｓｏｇｕａｒａｎｔｅｅｓｔｈｅｆｅａｓｉｂｉｌｉｔｙｏｆｔｈｅｐａｔｈａｔｔｈｅｓａｍｅｔｉｍｅ．Ｔｈｅｐａｔｈ ｐｏｉｎｔｓａｒｅｔａｋｅｎａｓｒｅｆｅｒｅｎｃｅｐｏｉｎｔｓｆｏｒｔｈｅＢ?ｓｐｌｉｎｅｂａｓｉｃｆｕｎｃｔｉｏｎ，ａｐｐｒｏａｃｈａｎｄｃｒｅａｔｅａｎｅｗｓｍｏｏｔｈｒｏｕｔｅｗｈｉｃｈｓｕｉｔｓｆｏｒｔｈｅｖｅｈｉ? ｃｌｅｔｏｆｏｌｌｏｗ．Ｔｈｅｓｉｍｕｌａｔｉｏｎｏｕｔｃｏｍｅｓａｎｄｆｉｅｌｄｔｅｓｔｓｖｅｒｉｆｙｔｈｅｅｆｆｅｃｔｉｖｅｎｅｓｓｏｆｔｈｅｍｅｔｈｏｄｐｒｏｐｏｓｅｄ．)