书籍

参阅书籍汇总 [Related Monograph]

图像处理与计算机视觉 (IMAGE PROCESS AND COMPUTER VISION):

[1] Rafael C. Gonzalez, Richard E. Woods. Digital Image Processing (3rd Edition)[M]. New York: Pearson Prentice Hall, 2007.
         Digital Image Processing is a third generation book that builds on two highly successful earlier editions and the authors' twenty years of academic and industrial experience in image processing. The book provides an introduction to basic concepts and methodologies for image processing and develops the foundation for further study in this diverse and rapidly evolving field. The topics covered range from enhancement and restoration to image encoding, segmentation, description, recognition, and interpretation. These topics are illustrated by numerous computer-processed images.

[2] Rafael C. Gonzalez, Richard E. Woods, Steven L. Eddins. Digital Image Processing Using MATLAB[M]. New York: Pearson Prentice Hall, 2003.
         For Image and Computer Vision, Image Processing, and Computer Vision courses. This is the first text that provides a balanced treatment of image processing fundamentals and an introduction to software principles used in the practical application of image processing. A seamless integration of material from the leading text, Digital Image Processing by Gonzalez and Woods and the Image Processing Toolbox from Mathworks, Inc. This text works in the MATLAB computing environment; the Toolbox provides a stable, well-supported set of software tools capable of addressing a broad spectrum of applications in digital image processing. The major areas covered include intensity transformations, linear and nonlinear spatial filtering, filtering in the frequency domain, image restoration and registration, color image processing, wavelets, image data compression, morphological image processing, image segmentation, regions and boundary representation and description, and object recognition. 


[3] Rafael C. Gonzalez, Richard E. Woods, Steven L. Eddins 阮秋琦等译数字图像处理 (第三版)[M]. 北京电子工业出版社, 2011.
         在数字图像处理领域,本书作为主要教材已有30多年。这一版本是作者在前两版的基础上修订而成的,是前两版的发展与延续。除保留了前两版的大部分内容外,根据读者的反馈,作者在13个方面对本书进行了修订,新增了400多幅图像、200多幅图表及80多道习题,融入了近年来数字图像处理领域的重要进展,因而本书特色鲜明且与时俱进。全书仍分为12章,即绪论、数字图像基础、灰度变换与空间滤波、频率域滤波、图像复原与重建、彩色图像处理、小波和多分辨率处理、图像压缩、形态学图像处理、图像分割、表示与描述、目标识别。


[4] Rafael C. Gonzalez, Richard E. Woods, Steven L. Eddins 阮秋琦等译数字图像处理 (Matlab)[M]. 北京电子工业出版社, 2005.
         《数字图像处理(MATLAB版)》是把图像处理基础理论论述与软件实践方法相结合的第一本书,它集成了冈萨雷斯和伍兹所著的《数字图像处理》一书中的重要内容和MathWorks公司的图像处理工具箱。本书的特色在于它重点强调了怎样通过开发新代码来增强这些软件工具。本书在介绍MATLAB编程基础知识之后,讲述了图像处理的主要内容,具体包括亮度变换、线性和非线性空间滤波、频率域滤波、图像复原与配准、彩色图像处理、小波、图像数据压缩、形态学图像处理、图像分割、区域和边界表示与描述以及对象识别等。








[5] Milan Sonka, Vaclav Hlavac, Roger Boyle, Image Processing, Analysis, and Machine Vision (3rd Edition)[M]. Rome: CL Engineering, 2007.
         This robust text provides deep and wide coverage of the full range of topics encountered in the field of image processing and machine vision. As a result, it can serve undergraduates, graduates, researchers, and professionals looking for a readable reference. The book's encyclopedic coverage of topics is wider than that found in any competing book, and it can be used in more than one course (both image processing and machine vision classes). In addition, while advanced mathematics is not needed to understand basic concepts (making this a good choice for undergraduates), rigorous mathematical coverage is included for more advanced readers. This text is especially strong and up-to-date in its treatment of 3D vision, with many topics not covered at all in competing books. It is also distinguished by its easy-to-understand algorithm descriptions of difficult concepts, and a wealth of carefully selected problems and examples that can be worked with any general-purpose image processing software package or programming environment.


[6] Milan Sonka, Vaclav Hlavac, Roger Boyle 艾海舟武勃等译图像处理、分析与机器视觉(3) [M]. 北京清华大学出版社, 2011.
      本书针对图像处理、图像分析和机器视觉领域的有关原理与技术展开了广泛而深入的讨论,包括图像预处理、图像分割、形状表示与描述、物体识别与图像理解、三维视觉、数学形态学图像处理技术、离散图像变换、图像压缩、纹理描述、运动分析等。本书力图将复杂的概念通过具体示例用易于理解的算法来描述,提供了大量包含图示和处理结果的插图,特别有助于读者的学习和理解。此外,本书还提供了丰富的参考文献,既列出了那些经过时间考验的经典论文,也列出了能反映未来发展方向的最新进展,适于读者进一步深入探索。本书覆盖了十分广泛的领域,包括人工智能、信号处理、人工神经网络、模式识别、机器学习、模糊数学等一系列相关学科。读者通过学习本书,可以学到很多具有普遍价值的知识和具体的应用方法。
[7] 章毓晋图像工程(): 图像处理 (3) [M]. 北京清华大学出版社, 2012.
      《图像工程(上册):图像处理(第3版)》为《图像工程》第3版的上册,主要介绍图像工程的第一层次--图像处理的基本概念、基本原理、典型方法、实用技术以及国际上有关研究的新成果。本册书主要分为4个单元。第1单元(包含第2~4章)介绍图像增强技术,其中第2章介绍基于点操作的空域增强技术,第3章介绍基于模板操作的空域增强技术,第4章介绍频域增强技术。第2单元(包含第5~7章)介绍图像恢复技术,其中第5章介绍图像消噪和恢复技术,第6章介绍图像校正和修补技术,第7章介绍图像投影重建技术。第3单元(包含第8~10章)介绍图像编码技术,其中第8章介绍图像编码基础,第9章介绍图像变换编码技术,第10章介绍其他编码技术。第4单元(包含第11~14章)介绍图像拓展技术,其中第11章介绍图像水印技术,第12章介绍彩色图像处理技术,第13章介绍视频图像处理技术,第14章介绍多尺度图像处理技术。书中的附录A介绍了图像方面的一些国际标准,主要与第3单元相关。书中提供了大量例题、思考题和练习题,并对部分练习题提供了解答。书末还给出了主题索引。
[8] 章毓晋图像工程(): 图像分析 (3) [M]. 北京清华大学出版社, 2012.
      《图像工程(中册):图像分析(第3版)》为《图像工程》第3版的中册,主要介绍图像工程的第二层次——图像分析的基本概念、基本原理、典型方法、实用技术以及国际上有关研究的新成果。本册书主要分为4个单元。第1单元(包含第2~5章)介绍图像分割技术,其中第2章介绍图像分割的基础知识和基本方法,第3章介绍一些典型的图像分割技术,第4章介绍对基本分割技术的推广,第5章介绍对图像分割的评价研究。第2单元(包含第6~8章)介绍对分割出目标的表达描述技术,其中第6章介绍目标表达技术,第7章介绍目标描述技术,第8章介绍进一步的测量和误差分析内容。第3单元(包含第9~11章)介绍目标特性分析技术,其中第9章介绍纹理分析技术,第10章介绍形状分析技术,第11章介绍运动分析技术。第4单元(包含第12-14章)介绍一些相关的数学工具,其中第12章介绍二值图像数学形态学,第13章介绍灰度图像数学形态学,第14章介绍图像模式识别原理和方法。书中的附录介绍了人脸和表情识别的原理和技术,是与第14章相关的应用和扩展。书中还提供了大量例题、思考题和练习题,并对部分练习题提供了解答。书末还给出了主题索引。
[9] 章毓晋图像工程(): 图像理解 (2) [M]. 北京清华大学出版社, 2007.
      本册书为《图像工程》的下册,主要介绍图像工程的第三层次——图像理解的基本概念、基本原理、典型方法、实用技术以及国际上有关研究的新成果。本册书主要内容归纳在四个单元中。第一个单元(包含第1,2,3,4章)主要介绍图像工程的整体发展状况和图像理解与其他相关学科的联系,基本的视觉感知原理和过程,高维图像采集以及3-d目标表达方法等。这些也为进一步学习后面单元的内容打下了基础。第二个单元(包含第5,6,7,8章)论述景物恢复(重建)的各种典型技术,对应图像理解的较低层次。这里主要涉及立体视觉技术(包括双目和多目),以及由单目图像恢复深度信息的技术(包括立体光度学、从运动求取结构、从阴影恢复形状、从纹理变化确定表面朝向等)。第三个单元(包含第9,10,11,12章)论述场景解释的概念和原理,对应图像理解的较高层次。这里论述知识和表达基础及常用方法、广义匹配的多种技术,以及图像模式识别的基础工具、图像理解理论的内容发展和图像信息系统的概况比较。第四个单元(包含附录a,b,c)分别介绍了三个典型图像理解技术的应用领域:多传感器图像信息融合、人脸和表情识别、基于内容的图像和视频检索。书中还提供了大量例题、思考题和练习题,并对近半数练习题提供了解答或解题思路。
[10] 章毓晋等著基于子空间的人脸识别[M]. 北京清华大学出版社, 2009.
      人脸识别是近年信息科学领域里一个备受关注的热点,基于子空间的人脸识别方法是一类主流的方法。本书结合作者自身的相关研究工作,回顾该领域的发展过程,介绍基本的原理和关键技术,总结已有的丰富成果,探索深入研究的方向。全面系统地介绍人脸识别的主要概念、基本原理、典型方法、实用技术,以及国际上有关研究的新成果和新动向。全书可分为4部分: 第1部分(包含第1~4章)介绍人脸识别的预备内容(发展概述,人脸检测、跟踪、描述); 第2部分(包含第5~8章)介绍人脸识别的各种典型的子空间方法(既有基本的线性方法,也有特殊的非线性方法); 第3部分(包含第9、10章)介绍人脸识别分类器设计和一些实验结果; 第4部分(包含4个附录)介绍人脸识别的相关基础和扩展。考虑到人脸识别涉及的学科多、范围广,本书选取了一些比较有特色的技术方法进行介绍,并结合科研成果给出形象的实例,以使该书既能较好地反映该领域的全貌,也有一定的层次,方便读者学习和使用。
[11] 高隽谢昭图像理解理论与方法[M]. 北京科学出版社, 2009.
       图像理解”是近年来计算机科学的热点研究领域,本书对图像理解的前沿理论与方法进行了详细论述。主要内容包括分类判别模型、生成模型、图像信息表示与特征提取、场景中的目标识别、场景中目标之间的关系、场景描述与理解、场景中的句法语义、图像理解开发环境和图像数据集等。本书紧跟上述内容的国内外发展现状和最新成果,阐述作者对图像理解理论方法的理解和认识。
[12] Richard Szeliski. Computer Vision: Algorithms and Applications[M]. New York: Springer, 2010.
      Humans perceive the three-dimensional structure of the world with apparent ease. However, despite all of the recent advances in computer vision research, the dream of having a computer interpret an image at the same level as a two-year old remains elusive. Why is computer vision such a challenging problem and what is the current state of the art? Computer Vision: Algorithms and Applications explores the variety of techniques commonly used to analyze and interpret images. It also describes challenging real-world applications where vision is being successfully used, both for specialized applications such as medical imaging, and for fun, consumer-level tasks such as image editing and stitching, which students can apply to their own personal photos and videos. More than just a source of 'recipes,' this exceptionally authoritative and comprehensive textbook/reference also takes a scientific approach to basic vision problems, formulating physical models of the imaging process before inverting them to produce descriptions of a scene. These problems are also analyzed using statistical models and solved using rigorous engineering techniques. Topics and features: Structured to support active curricula and project-oriented courses, with tips in the Introduction for using the book in a variety of customized courses; Presents exercises at the end of each chapter with a heavy emphasis on testing algorithms and containing numerous suggestions for small mid-term projects; Provides additional material and more detailed mathematical topics in the Appendices, which cover linear algebra, numerical techniques, and Bayesian estimation theory; Suggests additional reading at the end of each chapter, including the latest research in each sub-field, in addition to a full Bibliography at the end of the book; and, Supplies supplementary course material for students at the associated website. Suitable for an upper-level undergraduate or graduate-level course in computer science or engineering, this textbook focuses on basic techniques that work under real-world conditions and encourages students to push their creative boundaries. Its design and exposition also make it eminently suitable as a unique reference to the fundamental techniques and current research literature in computer vision.
[13] 罗四维等著视觉感知系统信息处理理论[M]. 北京电子工业出版社, 2006.
      本书从理论和应用的角度讨论研究神经感知和机器学习之间的关系。这种从模拟人类的神经模式出发来指导机器学习的方法,即结合神经科学的理论来探讨计算机处理信息的能力,具有重要的科学意义。作者力求向读者展示这方面的最新研究成果和热点问题,希望读者,特别是青年读者,能关注那些可能对计算机科学带来突破的课题。
[14] 罗四维等著视觉信息认知计算理论[M]. 北京科学出版社, 2010.
      在众多的生物系统中,人脑被认为是最高级的生物智能系统,它具有感知、识别、学习、联想、记忆、推理等功能。而在人脑感知的信息中,大部分来自视觉。视觉是人类获取信息的重要途径,也是人类对自身研究认识最深刻的部分。因此,研究生物体的视知觉功能,解析其内在机理,并用机器来实现,成为科学研究领域的一个重要方面,它可以为提高机器的智能与解决问题的能力提供新的思路。《视觉信息认知计算理论》系统地讨论了基于视觉感知和有效编码假说的特征表示、计算模型,从认知心理学出发讨论了半监督学习、聚类、知觉组织,从人类视觉的注意机理角度讨论了模拟视觉注意机制的视觉感知模型等。
[15] 焦李成等著智能目标识别与分类[M]. 北京科学出版社, 2010.
      《智能目标识别与分类》较为全面地介绍了模式识别的一个分支——机器学习的最新进展,深入分析了机器学习中的多个关键问题及多种快速稀疏学习方法,具体描述了机器学习在大规模数据识别与分类的工程设计与实现问题。全书共10章,内容包括:绪论,统计学习理论、再生核技术与支撑矢量机算法,支撑矢量机理论基础,先进支撑矢量机,核学习机,稀疏核支撑矢量机,快速大规模支撑矢量机,高分辨距离像识别,谱集成学习机,基于核学习的图像识别。

模式识别与机器学习:

[16] Vladimir N. Vapnik. The Nature of Statistical Learning Theory (2nd Edition)[M]. New York: Springer, 1999.
      The aim of this book is to discuss the fundamental ideas which lie behind the statistical theory of learning and generalization. It considers learning as a general problem of function estimation based on empirical data. Omitting proofs and technical details, the author concentrates on discussing the main results of learning theory and their connections to fundamental problems in statistics. This second edition contains three new chapters devoted to further development of the learning theory and SVM techniques. Written in a readable and concise style, the book is intended for statisticians, mathematicians, physicists, and computer scientists.
[17] Vladimir N. Vapnik张学工统计学习理论的本质[M]. 北京清华大学出版社, 2000.
      统计学习理论是针对小样本情况研究统计学习规律的理论,是传统统汁学的重要发展和补充,为研究有限样本情况下机器学习的理论和方法提供了理论框架,其核心思想是通过控制学习机器的容量实现对推广能力的控制。在这一理论中发展出的支持向量机方法是一种新的通用学习机器,较以往方法表现出很多理论和实践上的优势。《统计学习理论的本质》是该领域的权威著作,着重介绍了统计学习理论和支持向量机的关键思想、结论和方法,以及该领域的最新进展。《统计学习理论的本质》的读者对象是在信息科学领域或数学领域从事有关机器学习和函数估计研究的学者和科技人员,也可作为模式识别、信息处理、人工智能、统计学等专业的研究生教材。
[18] Richard O. Duda, Peter E. Hart, David G.Stock, Pattern Classification (2nd Edition)[M]. New York: Wiley-Interscience, 2000.
      The first edition, published in 1973, has become a classic reference in the field. Now with the second edition, readers will find information on key new topics such as neural networks and statistical pattern recognition, the theory of machine learning, and the theory of invariances. Also included are worked examples, comparisons between different methods, extensive graphics, expanded exercises and computer project topics.
[19] Richard O. Duda, Peter E. Hart, David G.Stock 李宏东姚天翔等译模式分类 [M]. 北京机械工业出版社, 2003.
      本书的第1版《模式分类与场景分析》出版于1973年,是模式识别和场景分析领域奠基性的经典名著。在第2版中,除了保留了第1版的关于统计模式识别和结构模式识别的主要内容以外,读者将会发现新增了许多近25年来的新理论和新方法,其中包括神经网络、机器学习、数据挖掘、进化计算、不变量理论、隐马尔可夫模型、统计学习理论和支持向量机等。作者还为来来25年的模式识别的发展指明了方向。书中包含许多实例,各种不同方法的对比,丰富的图表,以及大量的课后习题和计算机练习。本书作为流行和经典的教材,主要面向电子工程、计算机科学、数学和统计学、媒体处理、模式识别、计算机视觉、人工智能和认知科学等领域的研究生和高年级本科生,也可作为相关领域科技人员的重要参考书。
[20] Christopher M Bishop. Pattern Recognition and Machine Learning[M]. New York: Springer, 2006.
      This is the first textbook on pattern recognition to present the Bayesian viewpoint. The book presents approximate inference algorithms that permit fast approximate answers in situations where exact answers are not feasible. It uses graphical models to describe probability distributions when no other books apply graphical models to machine learning. No previous knowledge of pattern recognition or machine learning concepts is assumed. Familiarity with multivariate calculus and basic linear algebra is required, and some experience in the use of probabilities would be helpful though not essential as the book includes a self-contained introduction to basic probability theory.
[21] Trevor Hastie, Robert Tibshirani, Jerome Friedman. The Elements of Statistical Learning: Data Mining, Inference, and Prediction (2nd Edition)[M]. New York: Springer, 2009.
      During the past decade there has been an explosion in computation and information technology. With it have come vast amounts of data in a variety of fields such as medicine, biology, finance, and marketing. The challenge of understanding these data has led to the development of new tools in the field of statistics, and spawned new areas such as data mining, machine learning, and bioinformatics. Many of these tools have common underpinnings but are often expressed with different terminology. This book describes the important ideas in these areas in a common conceptual framework. While the approach is statistical, the emphasis is on concepts rather than mathematics. Many examples are given, with a liberal use of color graphics. It is a valuable resource for statisticians and anyone interested in data mining in science or industry. The book's coverage is broad, from supervised learning (prediction) to unsupervised learning. The many topics include neural networks, support vector machines, classification trees and boosting---the first comprehensive treatment of this topic in any book. This major new edition features many topics not covered in the original, including graphical models, random forests, ensemble methods, least angle regression & path algorithms for the lasso, non-negative matrix factorization, and spectral clustering. There is also a chapter on methods for wide data (p bigger than n), including multiple testing and false discovery rates.
[22] Damphne Koller, Nir Friedman. Probabilistic Graphical Models: Principles and Techniques[M]. Massachusetts: The MIT Press, 2009.
      Most tasks require a person or an automated system to reason--to reach conclusions based on available information. The framework of probabilistic graphical models, presented in this book, provides a general approach for this task. The approach is model-based, allowing interpretable models to be constructed and then manipulated by reasoning algorithms. These models can also be learned automatically from data, allowing the approach to be used in cases where manually constructing a model is difficult or even impossible. Because uncertainty is an inescapable aspect of most real-world applications, the book focuses on probabilistic models, which make the uncertainty explicit and provide models that are more faithful to reality. Probabilistic Graphical Models discusses a variety of models, spanning Bayesian networks, undirected Markov networks, discrete and continuous models, and extensions to deal with dynamical systems and relational data. For each class of models, the text describes the three fundamental cornerstones: representation, inference, and learning, presenting both basic concepts and advanced techniques. Finally, the book considers the use of the proposed framework for causal reasoning and decision making under uncertainty. The main text in each chapter provides the detailed technical development of the key ideas. Most chapters also include boxes with additional material: skill boxes, which describe techniques; case study boxes, which discuss empirical cases related to the approach described in the text, including applications in computer vision, robotics, natural language understanding, and computational biology; and concept boxes, which present significant concepts drawn from the material in the chapter. Instructors (and readers) can group chapters in various combinations, from core topics to more technically advanced material, to suit their particular needs.
[23] Andrew Blake, Pushmeet Kohli, Carsten Rother. Markov Random Fields for Vision and Image Processing[M]. Massachusetts: The MIT Press, 2011.
      This volume demonstrates the power of the Markov random field (MRF) in vision, treating the MRF both as a tool for modeling image data and, utilizing recently developed algorithms, as a means of making inferences about images. These inferences concern underlying image and scene structure as well as solutions to such problems as image reconstruction, image segmentation, 3D vision, and object labeling. It offers key findings and state-of-the-art research on both algorithms and applications. After an introduction to the fundamental concepts used in MRFs, the book reviews some of the main algorithms for performing inference with MRFs; presents successful applications of MRFs, including segmentation, super-resolution, and image restoration, along with a comparison of various optimization methods; discusses advanced algorithmic topics; addresses limitations of the strong locality assumptions in the MRFs discussed in earlier chapters; and showcases applications that use MRFs in more complex ways, as components in bigger systems or with multiterm energy functions. The book will be an essential guide to current research on these powerful mathematical tools.
[24] Stan Z. Li, Markov Random Field Modeling In Image Analysis (3rd Edition)[M]. New York: Springer, 2009.
      Markov random field (MRF) theory provides a basis for modeling contextual constraints in visual processing and interpretation. It enables systematic development of optimal vision algorithms when used with optimization principles. This detailed and thoroughly enhanced third edition presents a comprehensive study / reference to theories, methodologies and recent developments in solving computer vision problems based on MRFs, statistics and optimisation. It treats various problems in low- and high-level computational vision in a systematic and unified way within the MAP-MRF framework. Among the main issues covered are: how to use MRFs to encode contextual constraints that are indispensable to image understanding; how to derive the objective function for the optimal solution to a problem; and how to design computational algorithms for finding an optimal solution.
[25] Stephen Boyd, Lieven Vandenberghe. Convex Optimization[M]. London: Cambridge University Press, 2004.
      Convex optimization problems arise frequently in many different fields. This book provides a comprehensive introduction to the subject, and shows in detail how such problems can be solved numerically with great efficiency. The book begins with the basic elements of convex sets and functions, and then describes various classes of convex optimization problems. Duality and approximation techniques are then covered, as are statistical estimation techniques. Various geometrical problems are then presented, and there is detailed discussion of unconstrained and constrained minimization problems, and interior-point methods. The focus of the book is on recognizing convex optimization problems and then finding the most appropriate technique for solving them. It contains many worked examples and homework exercises and will appeal to students, researchers and practitioners in fields such as engineering, computer science, mathematics, statistics, finance and economics.


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