process

张正友经典论文，灵活的相机标定法，内附图片+源码，可以直接使用(zhangzhengyou,image+program, A Flexible New Technique for Camera Calibration)

车流量检测,车流量检测,车流量检测,车流量检测,(traffic flow,traffic flow,traffic flow,traffic flow)

这是2004年一篇重要的角点检测文献的配套Matlab源码。该算法的效果比SIFT、Harris、Susan等算法的检测效果更好。本压缩包中有从Matlab官方网站下载的源码，以及2004年那篇文献，对该算法做了详细解释。(This is 2004, an important corner detection literature supporting Matlab source code. Of the algorithm is better than SIFT, Harris, Susan, such as better detection algorithm. The compressed package from the official website to download the Matlab source code, and in 2004 came across the literature, the algorithm to do a detailed explanation.)

Matlab下，读取raw文件，实现多波段图像融合，高光谱与高分辨率图像融合，包括直接融合，傅里叶变换后融合，小波变换后融合及HIS变换后融合。(On Matlab,how to read and show raw files, achieve more band mergering,spectrum image and high resolution image fusiing,including direct fusion,Fourier transform fusion, wavelet transform fusion and HIS transformation fusion )

说明：  细胞跟踪算法.文件中有细胞的运动模拟视频,说明文件.内容比较复杂,适合于研究生以上的研究人员揣摩.(Tracking Algorithm: Object Delineation Method Multiparticle.)

如题，matlab手写数字测试图像处理 样本（30+50个）加全代码加测试结果(Like title, matlab handwritten digital test image processing samples (30 + 50) plus full code plus test results)

说明：  

采用了Harrtraining和离子滤波算法实现了拥挤人群人头计数，非常有用。VC++和Opencv开发，采用Oepncv中的Harrtraining训练一个分类器即可运行(Human detection and tracking in high density crowds is an unsolved problem. Standard preprocessing techniques such as background modeling fail when most of the scene is in motion. We integrate human detection and tracking into a single framework, and in- troduce a conrmation-by-classication method to estimate condence in a tracked trajectory, track humans through occlusions, and eliminate false positive detections.)

说明：  用最大相似性方法实现独立分量分析算法的matlab代码，可用于盲信号处理和图像滤波器构造。(with the greatest similarity method independent component analysis algorithm Matlab code can be used to blind signal processing and image filter structure.)

利用卷积神经网络进行特征提取，并将卷积特征可视化(Feature extraction using convolutional neural networks and visualization of convolution features)