【计算机视觉:算法与应用】Computer Vision: Algorithms and Application

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Computer Vision Algorithms and Applications
This book is dedicated to my parents, Zdzistaw and jadwiga and my family, Lyn, Anne, and Stephen Computer Vision: Algorithms and Applications(May 17, 2010 draft) Overview 1 Introduction 2 Image formation 29 3 Image processing 97 4 Feature detection and matching 205 5 Segmentation 267 6 Feature-based alignment 309 7 Structure from motion 343 Dense motion estimation 381 9 Image stitching 427 10 Computational photography 465 11 Stereo correspondence 529 12 3D reconstruction 57l 13 Image-based rendering 613 14 Recognition 649 15 Conclusion 723 Computer Vision: Algorithms and Applications(May 17, 2010 draft) Preface The seeds for this book were first planted in 2001 when Steve Seitz at the University of washington invited me to co-teach a course called"Computer vision for Computer Graphics. At that time computer vision techniques were increasingly being used in computer graphics to create image based models of real-world objects, to create visual effects, and to merge real-world imagery using computational photography techniques. Our decision to focus on the applications of computer vision to fun problems such as image stitching and photo-based 3 D modeling from personal photos seemed to resonate well with our students Since that time, a similar syllabus and project-oriented course structure has been used to teach general computer vision courses both at the University of Washington and at Stanford. (The latter was a coursc I co-taught with david Flect in 2003. Similar curricula have also been adopted at a number of other universities and also incorporated into more specialized courses on computational photography.(For ideas on how to use this book in your own course, please see Table 1. 1 in the Introduction§1.2.) This book also reflects my twenty years'experience doing computer vision research in cor- porate research labs, mostly at Digital Equipment Corporation's Cambridge Research Lab and at Microsoft Research. In pursuing my work, I have mostly focused on problems and solution techniques(algorithms)that have practical real-world applications and that work well in practice Thus, this book has more emphasis on basic techniques that work under real-world conditions, and less on more esoteric mathematics that has intrinsic elegance but less practical applicability This book is suitable for teaching a senior-level undergraduate course in computer vision to students in both computer science and electrical engineering. I prefer students to have either an image processing or a computer graphics course as a prerequisite so that they can spend less time learning general background mathematics and more time studying computer vision techniques The book is also suitable for teaching graduate-level courses in computer vision(by delving into the more demanding application and algorithmic areas), and as a general reference to fundamental techniques and recent research literature. 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 Computer Vision: Algorithms and Applications(May 17, 2010 draft) 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 morc opcn-cnded problcms 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 image formation process and develop mathematical techniques to invert these in order to recover the quantities of interest(where necessary making Simplifying assumption to make the mathematics 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 undcrstand their limitation and failure modes, as well as their expected computational costs (run-time performance) These thrcc approaches build on cach othcr and are used throughout the book 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 such as photos found on the web. 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 materialwhichcanbefoundonthebookwebsitehttp://szeliski.org/book.THismaterialwhich is described in Appendix C, includes Preface Pointers to commonly used data sets for the problems, which can be found on the Web 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 instructors consent, of course - Slide sets corresponding to the material covered in this book. (Until these sets are ready your best bet is to look at the slides from the courses we have taught at the University of Washingtonsuchashttp://www.cs.washingtonedu/education/courses/cse576/08sp/.) 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, and presentation. They fired up my interest in visual processing, 3D modeling, and statistical methods, while Larry Matthies introduced me to Kalman filtering and stereo matching Demetri Terzopoulos was my mentor at my first industrial research job and taught me the ropes of successful publishing. Yvan Leclerc and Pascal Fua, colleagues from my brief interlude at SRI International, gave me new perspectives on alternative approaches to computer vision 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 Stephane Lavallee, 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 collaboration with Daniel Scharstein, now at Middlebury college At Microsoft Research, Ive 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, Anandan, Phil Torr, Antonio Criminisi Computer Vision: Algorithms and Applications(May 17, 2010 draft) Ramin Zabih, Shai Avidan, Sing Bing Kang, Matt Uyttendaele, Larry Zitnick, Richard Hartley, Simon Winder, Drew Steedly, Dani Lischinski, Matthew Brown, Simon Baker, Michael Goesele, Eric Stollnitz, Sudipta Sinha, Johannes Kopf, and Neel Joshi. I was also lucky to have as interns such great students as Polina Golland, Simon Baker, Mei Han, Arno Schodl, Ron Dror, Ashley Eden, Jinxiang Chai, Rahul Swaminathan, Yanghai Tsin, Sam Hasinotf, Anat Levin, Matthew Brown, Vaibhav vaish, Jan-Michacl Frahm, James Dicbcl, Cc Liu, Joscf Sivic, Necl Joshi, Sudipta Sinha, Zeev Farbman, Rahul garg, and Tim Cho 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-timc collaborators Brian Curless, Steve Scitz, Manccsh Agrawala, Samccr Agarwal and Yasu Furukawa, as well as the students i have had the privilege to supervise and interact with including frederic Pighin, Yung-Yu Chuang, Colin Zheng, Aseem Agarwala, Noah Snavely rahul Garg, and ryan Kaminsky. In particular, 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 Steves encouragement, course notes, and editorial input Im also grateful to the many other computer vision researchers who have given me so many constructive suggestions about the book, including Sing Bing Kang, who was my informal book editor, Vladimir Kolmogorov, who contributed Appendix B.5.5 on linear programming techniques for MRF inference, Daniel Scharstein, Richard Hartley, Simon Baker, Noah Snavely, Bill Freeman Svetlana Lazcbnik, Matthew Turk, Jitendra Malik, Alyosha Efros, Michael Black, Steve Scitz Brian Curless, Sameer Agarwal, Li Zhang, Deva Ramanan, Olga Veksler, Yuri Boykov, Carsten Rother, Phil torr, Bill Triggs, Bruce Maxwell, Jana Kosecka, Eero Simoncelli, Aaron Hertzmann Antonio Torralba, Tomaso Poggio, Theo Pavlidis, Baba Vemuri, Nando de Freitas, Chuck Dyer Song Yi, Falk Schubert, Roman Pflugfelder, Marshall Tappen, Sammy Rogmans, Klaus Strobel Shanmuganathan, Andreas Siebert, Yongjun Wu, Fred Pighin, Juan Cockburn, Ronald Mallet, Tim Soper, Georgios evangelidis, Dwight Fowler, and Itzik bayaz If you have any suggestions for improving the book, please send me an e-mail, as I would like to keep the book as accurate, informative, and timely as possible. Keith Prices annotated Computervisionbibliographyathttp://iris.uscedu/vision-noteS/bibliography/contenTs.htmlhas proven invaluable in tracking down references and finding relatcd work Lastly, this book would not have been possible or worthwhile without the incredible support and encouragement of my family. i dedicate this book to my parents, Zdzistaw 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

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Moneycui 谢谢!资源不错
2016-09-23
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qq_22073961 对于有一定机器视觉技术基础的人来说,这本教材无疑是进阶的必备资料。认真领会书中的精髓后你会发现更广阔的天空。
2015-09-10
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bjz91 英文原版,非常感谢,但是这个draft在他的个人网站上有下载的~
2015-06-18
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qq_28431479 谢谢,还不错
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七喜糖 英文原版的, 还不错, 锻炼一下阅读能力.
2015-04-30
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kidmingkit 原版的不错
2014-03-27
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lossrual 英文原版,谢谢
2013-03-14
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Tank207_2012 英文原版的 还不错
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您会向同学/朋友/同事推荐我们的CSDN下载吗?
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