codebook

利用加权矩阵的盲超分辨率方法重建出一幅高分辨率的图像(The weighted matrix method of blind super-resolution reconstruction of a high-resolution image)

cvpr2012_oral On Multiple Foreground Cosegmentation(In this paper, we address a challenging image segmentation problem called multiple foreground cosegmentation (MFC), which concerns a realistic scenario in general Webuser photo sets where a finite number of K foregrounds of interest repeatedly occur over the entire photo set, but only an unknown subset of them is presented in each image. This contrasts the classical cosegmentation problem dealt with by most existing algorithms, which assume a much simpler but less realistic setting where the same set of foregrounds recurs in every image. We propose a novel optimization method for MFC, which makes no assumption on foreground configurations and does not suffer from the aforementioned limitation, while still leverages all the benefits of having co-occurring or (partially) recurring contents across images. Our method builds on an iterative scheme that alternates between a foreground modeling module and a region assignment module, both highly efficient and scalable. In part)

霍夫变换 以及相应的源码 用于图像处理中的 直线检测 参数估计等hough transform(Hough Transform)

xp 左侧 折叠式菜单

激光三维点云的处理源码，可以压缩点云，配准点云(Three-dimensional laser point cloud processing source code, can be compressed point cloud, point cloud registration)

matlab 噪声处理 白平衡处理 很强大的一段处理程序(matlab noise processing section of the white balance processing is a powerful process)

考虑了角加速度的飞机isar成像，进行转台模拟。(Consider the angular acceleration of the aircraft, isar imaging turntable simulation.)

说明：  图像分割，还可以，kmeans聚类的方法。k-means算法是一种聚类算法，所谓聚类，即根据相似性原则，将具有较高相似度的数据对象划分至同一类簇，将具有较高相异度的数据对象划分至不同类簇。(K-means algorithm is a clustering algorithm. The so-called clustering is to divide data objects with high similarity into the same class cluster and data objects with high dissimilarity into different class clusters according to the similarity principle.)

说明：  LSA的第一步是要去创建词到标题（文档）的矩阵。在这个矩阵里，每一个索引词占据了一行，每一个标题占据一列。每一个单元（cell）包含了这个词出现在那个标题中的次数。例如，词”book”出现在T3中一次，出现在T4中一次，而”investing”在所有标题中都出现了一次。一般来说，在LSA中的矩阵会非常大而且会非常稀疏（大部分的单元都是0）。这是因为每个标题或者文档一般只包含所有词汇的一小部分。更复杂的LSA算法会利用这种稀疏性去改善空间和时间复杂度。(The Little Book of Common SenseInvesting: The Only Way to Guarantee Your Fair Share of StockMarket Returns ,)

该函数用于简单背景下运动目标检测和轮廓提取，虽为简易实现，但效果不错，可根据个人需要添加代码(This function for a simple moving object detection and contour extraction, although it is easy to achieve, but good results can be based on individual need to add code)