THEPROBABILITYH-THEVIDENCEFUSION

用于人脸识别边缘检测，实现5个尺度、8个方向的检测。(Edge detection for face recognition)

graph cut 源码实现,yuri Boykov大牛的经典之作(graph cut algorithm matlab implementation)

这个算法是基于峭度的快速不动点算法，混合方式是线性瞬时混合，适定情况，该算法收敛速度较梯度算法快，很实用。(failed to translate)

discrete hartley trnasform paper

FMCW调频连续波雷达的测距测角，能量熵的计算，ICA（主分量分析）算法和程序。( FMCW frequency modulated continuous wave radar range and angular measurements, Energy entropy calculation, ICA (Principal Component Analysis) algorithm and procedures.)

包括harris经典算法、harris-Laplace算法，以及susan算法，鲁棒性好(Harris, including the classic algorithm, harris-Laplace algorithm, as well as susan algorithm, better robustness)

说明：  此算法使用于单波段或者多波段图像中的非监督异常检测，性能很好~(The algorithm used in single-band or multi-band image unsupervised anomaly detection, performance very good ~)

图像变换检测 主成分分析法 含PDF介绍(iamge change detection using PCA)

sift算法在cv领域的重要性不言而喻，该作者的文章引用率在cv界是number1.本篇博客只是本人把sift算法知识点整理了下，以免忘记。本文比较早的一篇博文opencv源码解析之(3)：特征点检查前言1 中有使用opencv自带的sift做了个简单的实验，而这次主要是利用Rob Hess的sift源码来做实验，其实现在的opencv版本中带的sift算法也是Rob Hess的，只是稍微包装了下。 下面来做下试验,试验sift代码采用Rob Hess的代码，opencv目前版本中的sift源码也是采用Rob Hess的。代码可以在他的主页上下载：http://blogs.oregonstate.edu/hess/code/sift/ 这里我下载的是windows版本的，并采用Qt做了个简单的界面。(sift algorithm is self-evident the importance of the cv field, the author of the article referenced in the cv sector is number1 this blog I just sift algorithm knowledge compiled under, so as not to forget. This article compares the earlier blog post opencv source code analysis (3): check the preface of the feature points 1 the opencv bring their own sift to do a simple experiment, but this time Rob Hess' s sift source to experiment. In fact, with the opencv version sift algorithm is Rob Hess, only slightly packing the next. Below to do the next test, test sift code using the code of Rob Hess, sift source opencv version of Rob Hess. The code can be downloaded on his home page: http://blogs.oregonstate.edu/hess/code/sift/ here I downloaded the windows version, and using Qt to do a simple interface.)

改进的数学形态学小目标检测，用于红外图像中的点目标检测，比传统的Top-Hat变换检测精度高(Small target detection improved mathematical morphology for point target detection in infrared images, higher detection accuracy than traditional Top-Hat transform)