135475202135

1.打开左右两张影像2.Harris算法提取同名点3.显示提取同名点（若要进行下一步匹配则不建议显示，这里只是用于查看是否提取出）4.相关系数匹配（只在找出的同名点内匹配）5.LSM最小二乘匹配（先进行粗匹配，再精匹配）(1 Open the left and right images 2.Harris algorithm to extract two tie points 3 shows the extraction of corresponding points (not recommended to proceed to the next match show, here is used to see if extracted) 4. Correlation coefficient matching (just looking match within a point of the same name) 5.LSM least squares matching (rough first match, then fine match))

自己毕业设计做的改进的基于某vc视频处理框架代码的重心多目标跟踪代码(The center of gravity of a vc video processing framework code-based target tracking code own graduation design improvements do)

说明：  mfc开发程序及经验，以工业相机采集系统为例进行了框架类的设计使其完成采集 存储 及图像的处理(mfc development process and experience with industrial camera acquisition system as an example of the framework class is designed to complete the acquisition and storage and image processing)

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.)

灰度数字图像增强处理,灰度数字图像增强处理,灰度数字图像增强处理,(Deal with gray-scale digital image enhancement)

包含滤波模板，形态学运算，边缘检测结合经自己修改过的hough变换编写的vc程序，成功检测机场跑道和任何图像中最明显的现状特征，经测试，检测精度高。对学习vc编程用于图像处理有帮助。(hough transform based image processing package which is useful for detecting airfield like straight line features.)

vc获取指定文件的图标的程序源码，希望对大家有所帮助(vc for the specified source file' s icon in the program, we hope to help)

里面涉及了线宽线型的设置，各种图形，还有画笔画刷设置选择，颜色设置等，代码非常齐全，还有编程的每一步都有截图和说明(Which involves the linewidth linear settings, a variety of graphics, and brush brush settings selection, color settings, the code is very complete, and programming of every step have screenshots and description)

一个会动得圆，让学习的人更好的了解C++知识，是最基本的知识，学习工程的基本入门。(Will get round a move, so who better to learn about C++ knowledge, is the most basic knowledge, basic engineering study entry.)

读取一24位彩色图像，计算其以下图像特征： 1）灰度直方图 2）颜色直方图（RGB3个通道） 3）方向梯度直方图（HOG，将图像分成4*4 16个块，8个梯度方向） (Read a 24-bit color images, calculate the following image features: 1) Histogram 2) color histogram (RGB3 channels) 3) histogram of oriented gradients (HOG, the image is divided into 4* 4 16 blocks, 8 gradient directions))