基于MATLAB的图像信息隐藏算法研究与实现_陈小娥

本文用用Harris算子提取特征点，基于特征点的匹配，加权融合图像拼接(In this paper, feature extraction using Harris operator point, based on feature matching, weighted fusion image mosaic)

现对图像进行边缘提取，然后利用霍夫变换提取图像中的圆环(Is image edge extraction, and then extracted using Hough transform image circle)

DMD相机原理，单像素成像原理，压缩感知(The principle of DMD camera)

偏最小二乘线性判别分析， 以及变量选择方法(partial least squares-linear discriminant analysis)

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.)

gmkl算法源码，广义多核学习，可用图像多类分类。(gmkl algorithm source, the a generalized multicore learning, free image multi-class classification.)

提出一种基于小波变换的图像配准方法。该方法首先用小波变换提取边缘特征点, 同时构成小波金字塔影像。在最高层利 用角度相关系数确定基准图像和配准图像之间的旋转角度, 经多分辨分级搜索匹配得到最终匹配特征点对, 利用这些特征点对待 配准图像进行配准纠正。实验结果表明, 改进方法有较好的效果, 可用于广泛应用领域。(Proposed a wavelet-based image registration method. In this method, using wavelet transform to extract the edge feature points, the same time constitute wavelet pyramid image. Point at the highest level the correlation coefficient using the baseline image registration between image and the angle of rotation, the multi-resolution hierarchical searching and matching features of the final match point, the use of these features point towards registration image registration correction. Experimental results show that the improved method has better results, can be used for wide range of applications.)

近两年关于图像检索的重要参考文献，包括IEEE和国内CNKI上发表的顶级期刊(Image Retrieval over the past two years on the importance of references, including the IEEE and domestic CNKI top journals published)

可以在程式畫面上任意畫圖寫字並且可清除再任意使用(Can any drawing program screens and write and to clear any longer use)

Video processing and coding algorithms