The SAGE Handbook of Online Research Methods(2nd) 英文无水印原版pdf 第2版 pdf所有页面使用FoxitReader、PDF-XChangeViewer、SumatraPDF和Firefox测试都可以打开 本资源转载自网络，如有侵权，请联系上传者或csdn删除 查看此书详细信息请在美国亚马逊官网搜索此书

内点法讲义 (ORIE 6300 Lecture Notes)

Robert M. Freund and Jorge Vera

Simultaneous Localization and Mapping for Mobile Robots Introduction and Methods 2012

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Statistical and Econometric Methods for Transportation Data Analysis

逆问题是几乎所有遥感探测的数学原理，诸如医学成像、地震探测、雷达成像，超声探测等。掌握了逆问题求解方法，也就掌握了不同探测模式的共同本质。

Mathematical Methods for Physicists by G.Arfken.pdf sixth edition

Introducing Monte Carlo Methods with R Introducing Monte Carlo Methods with R (Use R) By Christian P. Robert, George Casella Publisher: Springer Number Of Pages: 302 Publication Date: 2009-12-14 ISBN-10 / ASIN: 1441915753 ISBN-13 / EAN: 9781441915757 Product Description: Computational techniques based on simulation have now become an essential part of the statistician's toolbox. It is thus crucial to provide statisticians with a practical understanding of those methods, and there is no better way to develop intuition and skills for simulation than to use simulation to solve statistical problems. Introducing Monte Carlo Methods with R covers the main tools used in statistical simulation from a programmer's point of view, explaining the R implementation of each simulation technique and providing the output for better understanding and comparison. While this book constitutes a comprehensive treatment of simulation methods, the theoretical justification of those methods has been considerably reduced, compared with Robert and Casella (2004). Similarly, the more exploratory and less stable solutions are not covered here. This book does not require a preliminary exposure to the R programming language or to Monte Carlo methods, nor an advanced mathematical background. While many examples are set within a Bayesian framework, advanced expertise in Bayesian statistics is not required. The book covers basic random generation algorithms, Monte Carlo techniques for integration and optimization, convergence diagnoses, Markov chain Monte Carlo methods, including Metropolis {Hastings and Gibbs algorithms, and adaptive algorithms. All chapters include exercises and all R programs are available as an R package called mcsm. The book appeals to anyone with a practical interest in simulation methods but no previous exposure. It is meant to be useful for students and practitioners in areas such as statistics, signal processing, communications engineering, control theory,

7.5 聚类法 思路 将像素投射到特征空间成为样本点，根据样本点在特征空间的分布特性进行聚类。将类别标号投射回图像空间作为 像素的标号，进而实现分割。 哪些视觉元素容易被聚为同一类（1F2S2P4C） Proximity : 空间相邻性 Similarity : 特征相似性 Common fate : 运动同向性 Common region : 区域归属 Closure : 趋向于闭合 Parallelism : 平行性 Symmetry : 对称性 Continuity : 连续性 Familiar pattern : 组合后的熟悉程度 代表性的聚类分割算法 合成聚类与分裂聚类 每个样本点作为一个独立的簇；将所有样本作为一个簇 K-means 算法 模糊 C 均值聚类 Meanshift 算法 SLIC 超像素 K-means 的基本思想 将图像中所有的元素视为来源于 k 个类别，根据样本到类别中心的特征距离判断像素的归属，通过迭代更新的方式 在逼近类别模型参数的同时实现像素的分类。 K-means 的步骤 1. 为像素选择特征向量（比如 YUV 色彩特征），将所有像素映射为特征空间中的样本点。 2. 选择类别数量 k，在特征空间随机初始化 k个类的中心。 3. 根据样本点到类中心的距离，为每一个样本点选择距离最近类作为类别标号 4. 根据新的分类结果，以同一类样本点的特征均值更新类中心。 5. 重复步骤 3-4， 直到类中心的位置不再发生变化。