Harris

C-W节约算法是最经典的求解车辆路径问题的启发式算法,()

该源代码主要是利用bagging,adboosting等集成学习的方法进行图像融合处理，效果甚好！(The main source is the use of bagging, adboosting ensemble learning methods such as image fusion, the effect is very good!)

压缩感知，稀疏表示采用小波基表示，压缩测量采用随机高斯矩阵，重构算法是omp重构(Compressed sensing, sparse representation using wavelet representation, compression measurements using random Gaussian matrix remodeling reconstruction algorithm is omp)

对输入的一张彩色图像，自己写代码实现Harris Corner 检测算法： 1. 不能直接调用OpenCV 里面与Harris 角点检测相关的一些函数； 2. 只能用C/C++，不能用其他语言； 3. GUI 只能用自带的HighGUI，不能用QT 或其他的； 4. 平台可以用Windows， Linux, MacOS； 5. 显示中间的处理结果及最终的检测结果，包括最大特征值图，最小特征值图，R 图（可以考虑彩色 展示），原图上叠加检测结果等，并将这些中间结果都输出成图像文件； 6. 命令格式： “xxx.exe 图片文件 k 参数(=0.04) Aperture_size(=3)”。(The input of a color image, write your own code to achieve Harris Corner Detection Algorithm: 1. Can not be called directly inside OpenCV some functions associated with the Harris corner detection 2. To only use C/C++, can not be used in other languages 3. GUI only with their own HighGUI, can not use QT or other 4. The platform can be used Windows, Linux, MacOS 5. Display intermediate and final results of the test results, including the largest eigenvalues diagram, the minimum feature value graph , R chart (you can consider color display), picture superimposed on the test results, etc., and these intermediate results are output as an image file 6. Command Format: " xxx.exe picture file k parameters (= 0.04) Aperture_size (= 3 ). " )

这是用C++实现图像立体匹配的一个完整的论文代码，主要特性是提出了一种全新的跨规模代价聚合方法，适合对于图像的立体匹配有一定基础的人学习(Cross-Scale Cost Aggregation for Stereo Matching)

基于svd的数字水印，信噪比比较高(Svd based digital watermarking, can also signal to noise ratio is relatively high)

说明：  基于粒子滤波的强大的跟踪算法,可以在背景变化情况子实现可靠的实时跟踪，并且配有相应的论文(particle filter)

采用拉普拉斯金字塔的图像融合程序，完成图像融合功能，需采用高于matlab2009的matlab(the image fusion source code using lap pyramid, you have to use the matlab version later than 2009.)

对RGB图象的处理,一般要进行增强以达到去噪,锐化的功能,本程序在拉普拉斯算子基础上实现的(RGB images of the treatment, the general should be enhanced in order to achieve de-noising, sharpening function, the procedures in the Laplacian, based on the)

说明：  模式识别-贝叶斯最小错误率判别一幅图像是属于人，还是属于背景。图像经过预处理。(pattern recognition- Bayesian smallest error rate discriminant image is a person, or belong to the background. After pretreatment images.)