计算机视觉：算法与应用详解

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"《Computer Vision Algorithms and Applications》是Richard Szeliski撰写的一本关于计算机视觉的教材，书中详尽地介绍了计算机视觉领域的各种算法和实际应用，对学习计算机视觉的人来说具有很高的参考价值。" 正文: 计算机视觉是一门多学科交叉的领域，涉及图像处理、模式识别、机器学习等多个方面。本书《Computer Vision: Algorithms and Applications》通过丰富的篇章结构，全面覆盖了计算机视觉的基础知识和前沿技术。以下是对书中的关键章节和知识点的概述： 1. **概述** (Overview) - 引入计算机视觉的基本概念和历史背景，以及该领域的发展趋势。 2. **介绍** (Introduction) - 提供计算机视觉的基本定义，阐述其在现实世界中的重要性和应用范围。 3. **图像形成** (Image formation) - 解释光线如何与物体交互，以及相机如何捕捉这些交互形成图像，讨论光学原理、相机模型和成像过程中的噪声。 4. **图像处理** (Image processing) - 展示了图像增强、滤波、边缘检测等基本图像处理技术，这些都是后续特征提取和分析的基础。 5. **特征检测与匹配** (Feature detection and matching) - 介绍如何在图像中检测关键点（如SIFT、SURF）和描述符，以及如何将这些特征在不同图像间进行匹配，这对于图像配准和三维重建至关重要。 6. **几何校准与对齐** (Geometrical alignment and calibration) - 讨论相机标定的方法，以及如何通过几何变换来纠正图像的失真和进行图像对齐。 7. **从运动中获取结构** (Structure from motion) - 介绍如何从一系列图像中恢复场景的三维结构，包括基于运动的三角测量方法。 8. **稠密运动估计** (Dense motion estimation) - 讨论如何计算图像序列中每个像素的运动，例如光流法。 9. **图像拼接** (Image stitching) - 阐述如何将多张照片无缝拼接成全景图，涉及图像变换、曝光校正和遮挡处理。 10. **计算摄影学** (Computational photography) - 探讨新型的摄影技术，如高动态范围（HDR）图像、光照建模和光场摄影。 11. **立体对应** (Stereo correspondence) - 讨论如何通过两幅或更多图像来计算深度信息，实现立体视觉。 12. **形状和外观建模** (Shape and appearance modeling) - 描述如何构建和使用模型来表示物体的形状和表面属性。 13. **基于图像的渲染** (Image-based rendering) - 介绍如何利用一组图像生成新的视角或光照条件下的图像。 14. **基于视频的渲染** (Video-based rendering) - 讨论如何从连续的视频流中实时地创建虚拟视图。 15. **识别** (Recognition) - 阐述物体、场景和行为的识别方法，包括特征表示、分类器设计和深度学习的应用。 16. **结论** (Conclusions) - 对整个领域的总结，可能还探讨了未来研究的方向和挑战。这本书不仅深入浅出地讲解了计算机视觉的基本概念，还涵盖了实际应用中的关键技术，是学习和研究计算机视觉的宝贵资料。对于想要进入这个领域的初学者，以及需要进一步提升专业技能的从业者来说，都极具价值。

xvi Computer Vision: Algorithms and Applications (March 30, 2008 AM draft)

areas), and as a general reference to the fundamental techniques and most recent research literature

in computer vision. To this end, I have attempted wherever possible to at least cite the newest

research in each sub-ﬁeld, even if the technical details are too complex to cover in the book itself.

In teaching our courses, we have found it useful for the students to attempt a number of small

implementation projects, which often build on one another, in order to get them used to working

with real-world images and the challenges that these present. The students are then asked to choose

an individual topic for each of their small group ﬁnal projects. (Sometimes these projects even turn

into conference papers!) The exercises at the end of each chapter contain numerous suggestions

for both smaller mid-term projects, as well as more open-ended problems whose solution is still

an active research area. Wherever possible, I encourage students to try their algorithms on their

own personal photographs, since this better motivates them, often leads to creative variants on the

problems, and better acquaints them with the variety and complexity of real-world imagery.

In formulating and solving computer vision problems, I have often found it useful to draw

inspiration from three different high-level approaches:

1. Scientiﬁc: build detailed models of the phenomena that created the images under consider-

ation and develop mathematical techniques to invert these processes in order to recover the

quantities of interest (where necessary, making simplifying assumption to make the mathe-

matics more tractable).

2. Statistical: use probabilistic models to quantify the (prior) likelihood of your unknowns and

the noisy measurement processes that produce the input images, then infer the best possible

estimates of your desired quantities and analyze their resulting uncertainties. The inference

algorithms used are often closely related to the optimization techniques used to invert the

(scientiﬁc) image formation processes.

3. Engineering: develop techniques that are simple to describe and implement, but that are

also known to work well in practice. Test these techniques to understand their limitation and

failure modes, as well as their expected computational costs (run-time performance).

All three approaches are used in the book, and they all build on each other.

My personal research and development philosophy (and hence the exercises in the book) have

a strong emphasis on testing algorithms. It’s too easy in computer vision to develop an algorithm

that does something plausible on a few images rather than something correct. The best way to

validate your algorithms is to use a three part strategy.

First, test your algorithm on clean synthetic data, for which the exact results are known. Sec-

ond, add noise to data, and evaluate how the performance degrades as a function of noise level.

Finally, test the algorithm on real-world data, preferably drawn from a widely variable source,

Preface xvii

such as photos found on the Internet. Only then can you truly know if your algorithm can deal

with real-world complexity (i.e., images that do not ﬁt some simpliﬁed model or assumptions).

In order to help students in this process, this books comes with a large amount of supplementary

material, which can be found on the book Web site http://add-URL-here. This material,

which is described in Appendix C, includes:

 Sample data sets for the problems, including both inputs and desired (or plausible) outputs,

as well as all of the images and ﬁgures used in this book.

 Pointers to software libraries, which can help students get started with basic tasks such as

reading/writing images or creating and manipulating images. Some of these libraries also

contain implementations of a wide variety of computer vision algorithms, which can enable

you to tackle more ambitious projects (with your instructor’s consent, of course :-).

 Slide sets corresponding to the material covered in this book. [ Note: Until these sets are

ready, your best bet is to look at the slides from the courses we have taught at the Uni-

versity of Washington, such as http://www.cs.washington.edu/education/

courses/cse576/05sp/. ]

 A BibTeX bibliography of the papers cited in this book.

The latter two resources may be of more interest to instructors and researchers publishing new

papers in this ﬁeld, but they will probably come in handy even with regular students.

Acknowledgements

I would like to gratefully acknowledge all of the people whose passion for research and inquiry as

well as encouragement have helped me write this book.

Steve Zucker at McGill University ﬁrst introduced me to computer vision, taught all of his

students to question and debate research results and techniques, and encouraged me to pursue a

graduate career in this area.

Takeo Kanade and Geoff Hinton, my Ph. D. thesis advisors at Carnegie Mellon University,

taught me the fundamentals of good research, writing, presentation. They further ﬁred up my

interest in visual processing, 3D modeling, and statistical methods, while Larry Matthies ﬁred up

my interests in Kalman ﬁltering and stereo matching.

Demetri Terzopoulos was my mentor at my ﬁrst industrial research job and taught me the

ropes of successful conference publishing. Yvan Leclerc and Pascal Fua, colleagues from my brief

interlude at SRI International, gave me new perspectives on alternative approaches to computer

vision.

xviii Computer Vision: Algorithms and Applications (March 30, 2008 AM draft)

During my six years of research at Digital Equipment Corporation’s Cambridge Research Lab,

I was fortunate to work with a great set of colleagues, including Ingrid Carlbom, Gudrun Klinker,

Keith Waters, Richard Weiss, and St

ephane Lavall

ee, as well as to supervise the ﬁrst of a long string

of outstanding summer interns, including David Tonnessen, Sing Bing Kang, James Coughlan, and

Harry Shum. This is also where I began my long-term fruitful collaboration with Daniel Scharstein

(now at Middlebury College).

At Microsoft Research, I’ve had the outstanding fortune to work with some of the world’s best

researchers in computer vision and computer graphics, including Michael Cohen, Hugues Hoppe,

Stephen Gortler, Steve Shafer, Matthew Turk, Harry Shum, Phil Torr, Antonio Criminisi, Ramin

Zabih, Shai Avidan, Sing Bing Kang, Matt Uyttendaele, Larry Zitnick, Richard Hartley, Drew

Steedly, Matthew Brown, Simon Baker, and Dani Lischinski. I was also lucky to have as interns

such great students as Polina Golland, Simon Baker, Mei Han, Arno Sch

odl, Ron Dror, Ashley

Eden, Jinxiang Chai, Rahul Swaminathan, Yanghai Tsin, Sam Hasinoff, Anat Levin, Matthew

Brown, Vaibhav Vaish, Jan-Michael Frahm, James Diebel, Ce Liu, Josef Sivic, Neel Joshi, Sudipta

Sinha, and Zeev Farbman.

While working at Microsoft, I’ve also had the opportunity to collaborate with wonderful col-

leagues at the University of Washington, where I hold an Afﬁliate Professor appointment. I’m

indebted to David Salesin, who ﬁrst encouraged me to get involved with the research going on at

UW, my long-time collaborators Brian Curless, Steve Seitz, and Maneesh Agrawala, as well as the

student’s I’ve had the privilege to supervise and interact with, including Fr

ederic Pighin, Yung-Yu

Chuang, Colin Zheng, Aseem Agarwala, Noah Snavely, Rahul Garg, and Ryan Kaminsky. In par-

ticular, as I mentioned at the beginning of this preface, this book owes its inception to the vision

course that Steve Seitz invited me to co-teach, as well as to Steve’s encouragement, course notes,

and editorial input.

I’m also grateful to the many other computer vision researchers who have given me so many

constructive suggestions about the book, including Sing Bing Kang, Daniel Scharstein, Bill Free-

man, and add your name here. [ Note: See LaTeX source for list of other potential per-chapter

reviewers. ]

Lastly, this book would not have been possible, or even worthwhile, without the incredible sup-

port and encouragement of my family. I dedicate this book to my parents, Zdzisław and Jadwiga,

whose love, generosity, and accomplishments have always inspired me; to my sister Basia for her

lifelong friendship; and especially to Lyn, Anne, and Stephen, whose daily encouragement in all

matters (including this book project) makes it all worthwhile.

Lake Wenatchee

December, 2007

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