Computer Vision: Algorithms and Applications (Texts in Computer Science) 2011th Edition by Richard Szeliski (PDF)

Description

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; supplies supplementary course material for students at the associated website, http://szeliski.org/Book/.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.

User’s Reviews

Editorial Reviews: Review From the reviews:“This large work by Szeliski (Microsoft Research), an experienced computer vision researcher and instructor, contains hundreds of glossy color photos that illustrate the variety of techniques used to analyze and interpret images. … It is suitable for teaching a senior-level undergraduate course in computer vision or graduate courses covering the more demanding material. Its primary use will be as a general reference to the fundamental techniques and recent research literature for graduate students, faculty/researchers, and professionals. Summing Up: Recommended. Upper-division undergraduates and above.” (C. Tappert, Choice, Vol. 48 (9), May, 2011)“The aim of this book is to provide a course in computer vision for undergraduate students in computer science or electrical engineering. … The focus is on algorithms and applications. … The mathematics covered is nicely presented … . Each chapter contains exercises and references to additional reading. … The book also contains many references to resources on the Internet.” (Lisbeth Fajstrup, Zentralblatt MATH, Vol. 1219, 2011)“The main interests of Richard Szeliski’s book is to give a … up-to-date overview of the state of the art. … a valuable resource for teaching computer vision at either the undergraduate or graduate level. … an interesting read for any student or engineer who wants a broad introduction to the field of computer vision. … From a teaching point of view, the book is a valuable resource, offering an extended list of exercises, project proposals, and appealing applications of computer vision techniques.” (Sebastien Lefevre, ACM Computing Reviews, July, 2011) From the Back Cover 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 techniquesTopics 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 coursesPresents exercises at the end of each chapter with a heavy emphasis on testing algorithms and containing numerous suggestions for small mid-term projectsProvides additional material and more detailed mathematical topics in the Appendices, which cover linear algebra, numerical techniques, and Bayesian estimation theorySuggests 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 bookSupplies supplementary course material for students at the associated website, http://szeliski.org/Book/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.Dr. Richard Szeliski has more than 25 years’ experience in computer vision research, most notably at Digital Equipment Corporation and Microsoft Research. This text draws on that experience, as well as on computer vision courses he has taught at the University of Washington and Stanford. About the Author ​Dr. Richard Szeliski has more than 25 years’ experience in computer vision research, most notably at Digital Equipment Corporation and Microsoft Research. This text draws on that experience, as well as on computer vision courses he has taught at the University of Washington and Stanford. Read more

Reviews from Amazon users which were colected at the time this book was published on the website:

⭐The book acts as a good high level introduction to various significant sub-fields inside of computer vision. It is also one of the more up to date books (as of 2012) discussing more recent advances. However, because it is so high level and attempts to cover so much information, it is not a good book to try to learn from alone and provides no practical information on implementation details or problems. The best way to use the book, in my opinion, is to skim through it and learn the keywords to search for and use the references as a starting point. If you are a self learner like myself, one of the more frustrating problems is when you search for the wrong keywords and can’t find the material.On the whole the writing quality is good in terms of clarity and insight. There are some spots that I felt that the book did little more than restate what was said by other authors of highly cited papers. While this is not always a bad thing, there were times that I disagreed with statements due to various practical considerations. Yes the original author was technically correct, but in the years since publication very few people actually do that since it’s too computationally expensive or turned out to be less stable than advertised.The layout and organization of the book is well done and contains many full color pictures. For a new text book it is also very reasonably priced. I suspect that it would be more expensive to print the book’s PDF out in color rather than buying it! I bought the book instead of just skimming through the older drafts (available online from the author’s website) primarily because I prefer printed books and to support the authors publication approach.My rating is 4 stars based upon it being a high level introduction. As mentioned before, if you want a practical book that goes into how to implement all the techniques it discusses and issues that will arise, look elsewhere.

⭐This book is very good to learn computer vision as well for those who work in the area. Though it is a technical book, it is written in an interesting way so that one can read it like a story book.

⭐I was introduced to this book in my undergrad course on Computer Vision. As it was only a semester long course was not able to thoroughly dive into and understand all the topics as much as I would like to. As I am currently getting ready to pursue a graduate degree focusing on machine learning and computer vision I feel this book is a great help towards having a deeper understanding of these topics.

⭐For anyone looking for comprehensive coverage of all the fundamentals of computer vision, this is the book for you. The style of the book is that he gives you the general concept of a method and the required equations, and then provides you with the title of the paper it is sourced from (which are almost all available online as PDFs) in case you want more detail. Because of this approach, he has managed to fit into 800 pages what it could easily have taken 2000 to explain in fine detail.Don’t take this as the book being too vague, because I have yet to need to refer to the papers to understand a concept (although it has been useful when I wanted more information into *how* the result was arrived at). It does help if you have a baseline understanding of how to do general operations on images (e.g. array manipulation in code) and whatnot, but a beginner could still use this book with a little more effort.There’s no book that covers this breadth of information on computer vision, so I can’t recommend it more highly.

⭐A great introduction to Computer Vision, a nice review of the history of Computer Vision, and an enlightening survey of current and ongoing research.Richard Szeliski is a great teacher, at the top of his game, who gives motivation for the problems we may need to solve using Computer Vision.The algorithms are not provided as software code, but as descriptions with plenty of mathematical equations, references to papers, and copious diagrams and color photos.An enjoyable read, there is something for everyone interested in Computer Vision in this book. But although it is very broad, packing 700 textbook-sized pages with information, it does not always go very deep. And there is no source code. So you’re on your own if you want to turn the discussed algorithms into working code. It’s apparently intended to be used as a textbook, as there are questions at the end of each section. So reading each of the relevant papers and producing working software algorithms is left up to the reader.The example applications are motivating and there are a huge number of paper references (the footnotes section takes up 100 pages at the end of the book, just before the relatively-small 20-page index.)

⭐It’s an excellent textbook on learning the Opencv technology. Hence I found one issue:In Table 3.3, Fourier transforms of the separable kernels shown in Figure 3.14″, the X-axis should be labeled as “Omega/pi”. Therefore, the marker “0.5” indicates “0.5 pi”.On page 119, the top paragraph, the text said that “Reversal: The Fourier transform of a reversed signal is the complex conjugate of the signal’s transform”. This text indicates that if we extend the plot of Table 3.3 from “0.5 pi to 1.0 pi” by mirroring the plot of “0.0 to 0.5 pi”. Also, the plots from “-1.0 pi to 0.0 pi” could be the mirror image from “0.0 pi to 1.0 pi”.I assume that the author agreed with my judgement.

⭐to really understand vision you need to understand projective geometry and how to project a scene from 3-d to a 2-d screen. This book covers various pre-deep learning techniques. I especially loved the sections on photogrammetry which is about going from a 2-d image to a 3-d scene which lets you do stuff like import your furniture into VR so that you don’t bump into stuff.

⭐I bought this book in order to have a more thorough understanding of the algorithms I was covering in my Computer Vision class. I was told this was ‘the bible’ in CV so was very excited to read it. I could not have been more disappointed. This book simply reviews all the techniques that exist in the field without going in any detail for most of them (from the couple of chapters I have read). It only covers very general aspects of the algorithms and summarises entire techniques in 1 or 2 paragraphs (eg. SIFT descriptor).Furthermore, it is impossible to read a paragraph without being constantly interrupted by references. I bought this book to learn the intuition and details of the techniques used in CV, not to get references to the authors that originally developed the algorithms. If I want to have a look at the original papers or the work of their authors, I can simply google it.Although I have been severely criticising this book in the last two paragraphs, I want to remind the reader that I have only read 2 to 3 chapters and that it may not be representative of the entire book.

⭐The list of content is impressive but the content itself is deficient and incomplete. Author describes a lot of algorithms, but the description is incomplete. It is not possible to implement the most of the algorithms described in this book based only on this book. You will need to refer to the source papers, mainly in IEEE Xplore. Some parts of the algorithms are omitted, and some figures and equations are not accurate, or even are not at all explained. If You will treat this book as a huge collection of references than this book is OK. It gives a huge overview to the CV algorithms.At the end I have to say that the book is very well published. Hard cover, very thick paper and a print itself (inc. figures) is impressive.

⭐Contains most if not all relevant CV stuff for my courses explained in a proper way

⭐I am a masters student of computer science.For me, this book was mostly a waste of money, as it never goes into details. I mostly don’t see any derivations of methods. Hardly any math details.The amount of intuition this book gives is comparable to reading wikipedia. Everything is very broad with lots of citations in every sentence, which makes it even harder to read. Often there are even citations where you could very well just leave it without citations.Basically you can treat this book as a giant review of the state of the art in Computer Vision 10 years ago.And the fact that it is not anymore state of the art would probably make it worthless to buy now, even for researchers. So it feels like you can just read recent review papers instead of this book.I don’t really understand why this is 4-5 stars rated. Seems most of the positive reviews are from when this book was new. Now, it’s outdated.

⭐Disclaimer: I’m just a senior engineering undergrad who has had some relevant experiences read up all over the web and recently started taking a CV courseI just started to do some sensor fusion and calibration stuffs and have been scratching my head trying to look up references on technical details all over the web, but unfortunately it’s been more difficult than I thought. Most online stuffs aren’t very organized or designed for somewhat beginner, barely touches any technical details required to understand inner workings of sensors and maybe do implementation from scratch. As soon as I got this book, I flipped to a random page, and it’s “damn I’ve been looking up on this all over the web and couldn’t understand anything beyond the surface of what seems easy but doesn’t provide enough detail to get me started on implementation or even just at least connect stuffs together” and this book is everything I wished for! And damn it’s beautifully printed!

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