Introduction.to.Stochastic.Processes.with.R
An introduction to stochastic processes through the use of RIntroduction to Stochastic Processes with R is an accessible and well-balanced presentation of the theory of stochastic processes, with an emphasis on real-world applications of probability theory in the natural and social sciences. The uINTRODUCTIONTO STOCHASTICPROCESSES WITH RINTRODUCTIONTO STOCHASTICPROCESSES WITH RROBERT P DOBROWWILEYCopyright o 2016 by John Wiley Sons, Inc. All rights reservedPublished by John Wiley Sons, Inc, Hoboken, New JerseyPublished simultaneously in CanadaNo part of this publication may be reproduced, stored in a retrieval system, or transmitted in any form orby any means, electronic, mechanical, photocopying, recording, scanning, or otherwise, except aspermitted under Section 107 or 108 of the 1976 United States Copyright Act, without either the priorwritten permission of the Publisher, or authorization through payment of the appropriate per-copy fee tothe Copyright Clearance Center, Inc, 222 Rosewood Drive, Danvers, MA,(978)750-8400, fax978)750-4470,oronthewebatwww.copyright.comRequeststothePublisherforpermissionshouldbe addressed to the Permissions Department, John Wiley sons, Inc, lll River Street, Hoboken, NJ07030,(201)748-6011,fax(201)748-6008,oronlineathttp://www.wiley.com/go/permissionsLimit of liability/ Disclaimer of warranty While the publisher and author have used their best efforts inpreparing this book, they make no representations or warranties with respect to the accuracy orcompleteness of the contents of this book and specifically disclaim any implied warranties ofmerchantability or fitness for a particular purpose. No warranty may be created or extended by salesrepresentatives or written sales materials. The advice and strategies contained herein may not be suitablefor your situation. You should consult with a professional where appropriate. Neither the publisher norauthor shall be liable for any loss of profit or any other commercial damages, including but not limited tospecial, incidental, consequential, or other damagesFor general information on our other products and services or for technical support, please contact ourCustomer Care Department within the United States at(800)762-2974, outside the United States at(317)572-3993 or fax(317)572-4002Wiley also publishes its books in a variety of electronic formats. Some content that appears in print maynot be available in electronic formats. For more information about Wiley products, visit our web site atwww.wiley.comLibrary of Congress Cataloging-in-Publication Data:Dobrow. Robert p. authorIntroduction to stochastic processes with r/ Robert P. Dobrowpages cmIncludes bibliographical references and indexISBN978-1-118-74065-1( cloth)1. Stochastic processes. 2. R( Computer program language)I. TitleQC20.7.S8D6320165192′302855133-dc232015032706Set in 10/12pt, Times-Roman by SPi Global, Chennai, IndiaPrinted in the united states of america1098765432112016To my familyCONTENTSPrefaceAcknowledgmentsList of Symbols and Notationabout the companion Website1 Introduction and review1.1 Deterministic and stochastic models. 11. 2 What is a Stochastic Process? 61. 3 Monte Carlo Simulation. 91.4 Conditional Probability, 101. 5 Conditional Expectation, 18Exercises. 342 Markov Chains: First Steps402.1 Introduction. 402.2 Markov Chain Cornucopia, 422.3 Basic Computations, 522. 4 Long-Term behavior-the Numerical evidence, 592.5 Simulation. 652.6 Mathematical Induction*. 68Exercises. 70CONTENTS3 Markov Chains for the long term763.1 Limiting Distrib763.2 Stationary Distribution, 803.3 Can you find the way to state a? 943.4 Irreducible markov Chains. 1033.5 Periodicity, 1063.6 Ergodic Markov Chains, 1093.7 Time Reversibility, 1143.8 Absorbing Chains, 1199 Regeneration and the strong markov property 1333.10 Proofs of limit Theorems*, 135Exercises. 1444 Branching processes1584.1 Introduction. 1584.2 Mean Generation Size. 1604.3 Probability Generating Functions, 1644.4 Extinction is Forever. 168Exercises. 1755 Markov Chain Monte Carlo1815.1 Introduction. 1815.2 Metropolis-Hastings Algorithm, 1875.3 Gibbs Sampler, 1975.4 Perfect Sampling*, 20.55.5 Rate of Convergence: the Eigenvalue Connection*, 2105.6 Card Shuffing and Total Variation Distance. 212Exercises. 2196 Poisson process2236.1 Introduction. 2236.2 Arrival. Interarrival Times. 2276.3 Infinitesimal Probabilities. 2346.4 Thinning, Superposition, 2386.5 Uniform Distribution. 2436.6 Spatial Poisson Process, 2496.7 Nonhomogeneous Poisson Process. 2536.8 Parting Paradox, 255Exercises. 2587 Continuous- Time markov Chains2657.1 Introduction. 265
- 2020-12-10下载
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SIFT算法详解及应用(讲的很详细)
SIFT算法入门时看的一篇文章，SIFT算法详解及应用(讲的很详细)SIFT简介SIFTScale Invariant Feature Transform传统的特征提取方法成像匹配的核心问题是将同一目标在不同时间、不同分辨率、不同光照、不同位姿情况下所成的像相对应。传统的匹配算法往往是直接提取角点或边缘,对环境的适应能力较差,急需提出一种鲁棒性强、能够适应不同光照、不同位姿等情况下能够有效识别目标的方法。己0]/3/己7彐SIFT简介SIFTScale Invariant Feature TransformSIFT提出的目的和意义分1999年 British columbia大学大卫.劳伊( David g.Lowe)教授总结了现有的基于不变量技术的特征检测方法,并正式提出了一种基于尺度空间的、对图像缩放、旋转甚至仿射变换保持不变性的图像局部特征描述算子一SIFT(尺度不变特征变换),这种算法在2004年被加以完善己0]/3/己7SIFT简介SIFTScale Invariant Feature Transform将一幅图像映射(变换)为一个局部特征向量集;特征向量具有平移、缩放、旋转不变性,同时对光照变化、仿射及投影变换也有一定不变性。己0]/3/己7SIFT简介SIFTScale Invariant Feature TransformSIFT算法特点SIFT特征是图像的局部特征,其对旋转、尺度缩放、亮度变化保持不变性,对视角变化、仿射变换、噪声也保持一定程度的稳定性。独特性( Distinctiveness)好,信息量丰富,适用于在海量特征数据库中进行快速、准确的匹配。多量性,即使少数的几个物体也可以产生大量SIFT特征向量。经过优化的SIFT算法可满足一定的速度需求。可扩展性,可以很方便的与其他形式的特征向量进行联合。己0]/3/己7SIFT简介SIFTScale Invariant Feature TransformSIFT算法可以解决的问题目标的自身状态、场景所处的环境和成像器材的成像特性等因素影响图像配准/目标识别跟踪的性能。而SIFT算法在一定程度上可解决:目标的旋转、缩放、平移(RST)图像仿射/投影变换(视点 viewpoint)光照影响(111 amination)目标遮挡( occlusion)杂物场景(c1 utter)噪声己0]/3/己7SIFT算法实现细节SIFTScale Invariant Feature TransformSIFT算法实现步骤简述SIFT算法的实质可以归为在不同尺度空间上查找特征点(关键点)的问题。原图像特征点特征点目标的特检测描述征点集特征点匹匹配点矫配正目标图像特征点特征点目标的特检测描述征点集SIFT算法实现物体识别主要有三大工序,1、提取关键点;2、对关键点附加详细的信息(局部特征)也就是所谓的描述器;3、通过两方特征点(附带上特征向量的关键点)的两两比较找出相互匹配的若干对特征点,也就建立了景物间的对应关系。SIFT算法实现细节SIFTScale Invariant Feature TransformSIFT算法实现步骤关键点检测己。关键点描述彐·关键点匹配4·消除错配点己0]/3/己7关键点检测的相关概念SFTiant Feature Transfor1.哪些点是SIFT中要查找的关键点(特征点)?这些点是一些十分突出的点不会因光照条件的改变而消失,比如角点边缘点、暗区域的亮点以及亮区域的暗点,既然两幅图像中有相同的景物,那么使用某种方法分别提取各自的稳定点,这些点之间会有相互对应的匹配点。所谓关键点,就是在不同尺度空间的图像下检测出的具有方向信息的局部极值点。根据归纳,我们可以看出特征点具有的三个特征:尺度方向大小己0]/3/己7
- 2020-05-27下载
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