logo资料库

Markov Random Field Modeling in Image Analysis 3rd edition.pdf

发布时间：2022-06-09 发布人：admin 分类：说明书 资料大小：4.63M 资料格式：pdf 举报 版权申诉
第1页 / 共372页
第2页 / 共372页
第3页 / 共372页
第4页 / 共372页
第5页 / 共372页
第6页 / 共372页
第7页 / 共372页
第8页 / 共372页
资料共372页，剩余部分请下载后查看
    Advances in Pattern Recognition For other titles published in this series, go to http://www.springer.com/series/4205
    Stan Z. Li Markov Random Field Modeling in Image Analysis Third Edition 1 3
    Stan Z. Li Center for Biometrics and Security Research & National Laboratory of Pattern Recognition Institute of Automation Chinese Academy of Science Beijing 100190, China Stan.ZQ.Li@gmail.com Series editor Professor Sameer Singh, PhD Research School of Informatics, Loughborough University, Loughborough, UK ISBN: 978-1-84800-278-4 DOI: 10.1007/978-1-84800-279-1 e-ISBN: 978-1-84800-279-1 Advances in Pattern Recognition Series ISSN 1617-7916 British Library Cataloguing in Publication Data A catalogue record for this book is available from the British Library Library of Congress Control Number: 2008943235 c Springer-Verlag London Limited 2009 Apart from any fair dealing for the purposes of research or private study, or criticism or review, as per- mitted under the Copyright, Designs and Patents Act 1988, this publication may only be reproduced, stored or transmitted, in any form or by any means, with the prior permission in writing of the publish- ers, or in the case of reprographic reproduction in accordance with the terms of licences issued by the Copyright Licensing Agency. Enquiries concerning reproduction outside those terms should be sent to the publishers. The use of registered names, trademarks, etc. in this publication does not imply, even in the absence of a specific statement, that such names are exempt from the relevant laws and regulations and therefore free for general use. The publisher makes no representation, express or implied, with regard to the accuracy of the information contained in this book and cannot accept any legal responsibility or liability for any errors or omissions that may be made. Printed on acid-free paper Springer Science+Business Media springer.com
    In Memory of My Mother v
    “An excellent book — very thorough and very clearly written.” — Stuart Geman “I have found the book to be a very valuable reference. I am very impressed by both the breadth and depth of the coverage. This must have been a truly monumental undertaking.” — Charles A. Bouman vii
    Foreword by Anil K. Jain The objective of mathematical modeling in image processing and computer vision is to capture the intrinsic character of the image in a few parame- ters so as to understand the nature of the phenomena generating the image. Models are also useful to specify natural constraints and general assump- tions about the physical world; such constraints and assumptions are neces- sary to solve the “inverse” problem of three-dimensional scene interpretation given two-dimensional image(s) of the scene. The introduction of stochastic or random field models has led to the development of algorithms for image restoration, segmentation, texture modeling, classification, and sensor fusion. In particular, Gibbs and Markov random fields for modeling spatial context and stochastic interaction among observable quantities have been quite useful in many practical problems, including medical image analysis and interpreta- tion of remotely sensed images. As a result, Markov random field models have generated a substantial amount of excitement in image processing, computer vision, applied statistics, and neural network research communities. This monograph presents an exposition of Markov random fields (MRF’s) that is likely to be extensively used by researchers in many scientific disci- plines. In particular, those investigating the applicability of MRF’s to process their data or images are bound to find its contents very useful. The main fo- cus of the monograph, however, is on the application of Markov random fields to computer vision problems such as image restoration and edge detection in the low-level domain, and object matching and recognition in the high-level domain. Using a variety of examples, the author illustrates how to convert a specific vision problem involving uncertainties and constraints into essentially an optimization problem under the MRF setting. In doing so, the author in- troduces the reader to the various special classes of MRF’s, including MRF’s on the regular lattice (e.g., auto-models and multilevel logistic models) that are used for low-level modeling and MRF’s on relational graphs that are used for high-level modeling. The author devotes considerable attention to the problems of parameter estimation and function optimization, both of which are crucial in the MRF paradigm. Specific attention is given to the estimation of MRF parameters in the context of object recognition, and to the issue of algorithm selection ix
    x Foreword by Anil K. Jain for MRF-based function optimization. Another contribution of the book is a study on discontinuities, an important issue in the application of MRF’s to image analysis. The extensive list of references, high-level descriptions of algorithms, and computational issues associated with various optimization algorithms are some of the attractive features of this book. On the whole, the contents of this monograph nicely complement the material in Kindermann and Snell’s book Markov Random Fields and Their Applications and Chellappa and Jain’s edited volume entitled Markov Ran- dom Fields: Theory and Applications. In my opinion, the main contribution of this book is the manner in which significant MRF-related concepts are lucidly illustrated via examples from computer vision. Anil K. Jain East Lansing, Michigan June 8, 1995
    分享到：
    赞 收藏

    相关推荐

    资料库

    课程资源

    共收录17145份资料，累计13个分类，关注成员有19位，主要包括：PHP,网络管理,网页制作,Java,.Net,数据库,3G/移动开发,C/C++,游戏开发,嵌入式,讲义,软件测试,专业指导

    热门标签

    PHP 网络管理 网页制作 Java .Net 数据库 3G/移动开发 C/C++ 游戏开发 嵌入式 讲义 软件测试 专业指导

    最新资料