mean_shift_iteration

获取灰度图像LBP特征的matlab程序，两种LBP算子(LBP to obtain the characteristics of gray image matlab procedures, both LBP operator)

说明：  vibe算法实现，该算法是2011年提出，具有运行占用空间小的特性(Vibe algorithm, which was proposed in 2011, has the characteristics of small running space)

在已知高度的情况下利用单目视觉摄像头来进行定位。(Positioning with a monocular vision camera at a given height)

个人编写的Matlab的红外图像增强程序，比较简单易懂！(Individual written Matlab infrared image enhancement procedure is relatively simple to understand!)

CxImage是一款免费的、开源的、功能强大的图形处理库。 跨平台，支持windows、linux等；支持BMP、GIF、ICO、TGA、JPEG、PCX、PNG、TIFF、MNG、RAS等多种图像格式；支持格式转换、图像处理、几何变换等；最重要的是使用非常简单。此为VC6修正版(CxImage is a C++ image processing library. It can load, save, display, transform images in a very simple and fast way, with transparency, multiple layers and selections, support for BMP GIF JPG PNG MNG TIF ICO TGA PCX J2K JBG RAS PNM RAW PSD.)

JSteg对图像的DCT域进行信息隐写，并重构图像，再对重构的图像进行隐藏信息读取(JSteg steganates the DCT domain of the image, reconstructs the image, and then reads the hidden information from the reconstructed image.)

说明：  使用opencv语言绘制所有轮廓后根据轮廓长度丢弃小轮廓(Opencv draw all contours of the language used under the contour length of the drop after the small contour)

根据CT成像原理得到Sinogram图像，再经过Randon变换以及反向投影算法对物体图线进行复原。(It is a Matlab ToolBox which can solve CT problems or image reconstruction easily and convincely.)

图像的特征用到了Dense Sift，通过Bag of Words词袋模型进行描述，当然一般来说是用训练集的来构建词典，因为我们还没有测试集呢。虽然测试集是你拿来测试的，但是实际应用中谁知道测试的图片是啥，所以构建BoW词典我这里也只用训练集。 其实BoW的思想很简单，虽然很多人也问过我，但是只要理解了如何构建词典以及如何将图像映射到词典维上去就行了，面试中也经常问到我这个问题，不知道你们都怎么用生动形象的语言来描述这个问题？ 用BoW描述完图像之后，指的是将训练集以及测试集的图像都用BoW模型描述了，就可以用SVM训练分类模型进行分类了。 在这里除了用SVM的RBF核，还自己定义了一种核： histogram intersection kernel，直方图正交核。因为很多论文说这个核好，并且实验结果很显然。能从理论上证明一下么？通过自定义核也可以了解怎么使用自定义核来用SVM进行分类。(Image features used in a Dense Sift, by the Bag of Words bag model to describe the word, of course, the training set is generally used to build the dictionary, because we do not test set. Although the test set is used as the test you, but who knows the practical application of the test image is valid, so I am here to build BoW dictionary only the training set. In fact, BoW idea is very simple, although many people have asked me, but as long as you understand how to build a dictionary and how to image map to the dictionary D up on the line, and interviews are often asked me this question, do not know you all how to use vivid language to describe this problem? After complete description of the image with BoW, refers to the training set and test set of images are described with the BoW model, the training of SVM classification model can be classified. Apart from having to use the RBF kernel SVM, but also their own definition of a nuclear: histogram intersection kernel, histogram )

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.)