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该程序为直方图均衡化程序，附有GUI界面，操作简单，可现实处理前后图像对比及处理前后的直方图分布情况显示，使用方便。(The program for histogram equalization program, with GUI interface, simple operation, the histogram distribution around the reality that is processing the image before and after treatment, easy to use.)

利用相位相关法对图像进行配准，配准精度可达到0.01(The registration accuracy can reach 0.01 by using phase correlation method.)

压缩感知CS——采用小波变换进行稀疏表示，高斯随机矩阵为观测矩阵，重构算法为ROMP算法，对256*256的lena图处理，比较原图和ROMP算法在不同采样比例(0.74、0.5、0.3)下的重构效果，并各运行50次，比较算法性能PSNR和每次的运行时间 (Compressed sensing CS- using wavelet transform as sparse representation, Gaussian random matrix as the observation matrix and ROMP algorithm as the reconstruction algorithm. Compare lena figure and the reconstruction results using ROMP algorithm at different sampling ratio (0.74,0.50.3),then each runs 50 times, compare the performance of PSNR and each running time)

说明：  多目标跟踪，动态背景下实现多目标跟踪算法，算法优良，实时性和鲁棒性都比较好(Tracking, dynamic context to achieve multi-target tracking algorithm, algorithm quality, real-time and better robustness than)

Encapsulate Namespace tracking logic for use by SAX drivers.

基于Hessian矩阵对输入的心血管图像进行增强和分割(The input cardiovascular images are enhanced and segmented based on the Hessian matrix)

说明：  很好用的三维重建软件，VisualSFM软件允许上传一系列图像，它从这些图像中找到每一个图像的特定特征，利用这些特征信息重建出3D模型的稀疏点云，而后还可进行稠密点云重建。(The VisualSFM software, a good 3D reconstruction software, allows uploading a series of images. It finds the specific features of each image from these images, and USES these feature information to reconstruct the sparse point cloud of the 3D model, and then it can reconstruct the dense point cloud.)

压缩包展示了对含高斯噪声和椒盐噪声图片去噪处理的基本去噪演示，包括均值滤波、中值滤波、维纳滤波、小波滤波。希望可以对初学的朋友有所帮助。(Basic denoising with Gaussian noise and salt and pepper noise image denoising demo, including mean filter, median filtering, Wiener filtering, wavelet filter)

Graph cut with matlab. It is very powerful

根据D. Comaniciu, P. Meer: Mean Shift: A robust approach toward feature space analysis 以及 C. Christoudias, B. Georgescu, P. Meer: Synergism in low level vision.这两篇文献提供的方法编写的图像分割代码，作者是 Chris M. Christoudias, Bogdan Georgescu，代码经我看了后加了丰富的中文注释，希望可以给各位带来阅读上的方便。 基于meanshift的聚类分割方法包括滤波、区域融合等操作，通过调整sigma和sigmar来调整分割效果。(According to D. Comaniciu, P. Meer: Mean Shift: A robust approach toward feature space analysis, and C. Christoudias, B. Georgescu, P. Meer: Synergism in low level vision. These two documents prepared by the methods provided by image segmentation code , the author is Chris M. Christoudias, Bogdan Georgescu, after I read the code, add a rich Chinese notes, hoping to bring you the convenience of reading. Segmentation method based on clustering meanshift including filtering, regional integration and other operations, and by adjusting the sigma sigmar to adjust segmentation results.)