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说明：  带颜色恢复的MSR代码，可直接在MATLAB上调用。(MSR code with color recovery can be called directly on MATLAB.)

一个颜色直方图的matlab源码，备注非常详细，可以实现(A color histogram matlab source code, notes are very detailed, can be achieved)

说明：  双目测距bm算法，已经实现,基于vs2015+opencv3.2,测距精度很高，一米内精度一厘米误差(Binocular ranging bm algorithm, which has been implemented, is based on vs2015 + opencv3.2, which has high accuracy and one centimeter error in one meter)

利用双线性插值，获取亚像素位置的灰度值。适用于得到sift、surf得到的特征点坐标处的灰度值获取(The gray value of sub-pixel position is obtained by bilinear interpolation)

高精度亚像素全局运动估计的上采样梯度互相关算法(Precision subpixel global motion estimation on sampling the gradient of cross-correlation algorithm)

(l)小波分析理论在变形监测数据处理研究中的理论意义和实用价值 (2)小波阂值去噪和多分辨分析在变形监测信号分析中的应用 (3)在算法和图像处理方面的优越性 ((L) wavelet analysis theory in the study of deformation monitoring data processing theoretical significance and practical value (2) wavelet thresholding denoising and multi-resolution analysis in deformation monitoring signal analysis application (3) in the algorithm and image processing superiority )

易语言2D游戏引擎模块源码，易语言写的图形图象编程源码，很好的参考。(Easy language 2D game engine source module, easy language to write the graphics programming source code, a good reference.)

说明：  运用二元光学元器件实现高斯激光光束的波形整形(Using binary optical components Gaussian laser beam waveform shaping)

说明：  实现对图像的归一化处理，为图像进行水印嵌入等操作打下基础(The realization of the normalized image processing, for image watermark embedding operation, such as laying a foundation)

说明：  该课题为基于MATLAB平台的PCA的人脸识别图像考勤系统。传统的人脸识别都是直接人头和人头的对比，现实意义不大，这块都做烂了，不易通过毕设答辩，而且论文不好写。该课题识别原理为：从一副生活照中寻找到人脸，并且分割人脸图象，利用PCA算法进行降维，和库里图片进行对比，输出目标人脸以及记录识别的时间，可以统计人脸打卡的当月次数，从而统计出勤率。该课题还可以继续二次开发，做成库内外人脸的识别，如果是库外人脸则实现报警。GUI界面有清除数据功能，统计工资等功能。(This project is based on the MATLAB platform PCA face recognition image attendance system. The traditional face recognition is the comparison of direct head and head, which is of little practical significance. This part is not easy to pass the final defense, and the paper is not easy to write. The recognition principle of this subject is: find the face from a living picture, segment the face image, reduce the dimension by PCA algorithm, compare with the library picture, output the target face and record the recognition time, and then count the number of times in the month when the face is punched, so as to count the attendance rate. The subject can also be further developed to make face recognition inside and outside the database. If it is outside the database, the alarm can be realized. The GUI interface has the functions of data clearing and salary statistics.)