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I'm searching for Computer Vision: A Modern Approach (2nd Edition) pdf?
It depends on what you want to learn in computer vision. Even then, this is difficult to answer because there is no one book to rule them all. For a good overview and simple explanation of methods with references (if you want to go deeper), try Computer Vision. Algorithms and Applications, by Richard Szeliski. Many algorithms described in the book can be implemented straight after reading, but for the more advanced methods the details are lacking. For epipolar geometry side, the quintessential book that pretty much every CV person has on their desk is "MVG", Multiple View Geometry in Computer Vision by Hartley and Zisserman. Some beginners find this book to be challenging, but if you read and understand the first 3 or 4 chapters, and read the appendices, I think the rest of the book is fairly straight forward until you get into the deeper chapters, but by then you should be comfortable with the style and material. A solid companion book for Multiple-view Geometry would be the "MaSKS" book, An Invitation to 3-D Vision. Yi Ma, Stefano Soatto, Jana Kosecka, Shankar Sastry. Springer Verlag, 2003 . This book walks you through all the details that MVG alludes to but leaves as an exercise to the reader. However, the details can be cumbersome, and the beauty of MVG is it gives you very elegant explanations. Sometimes an elegant explanation is more enlightening than a detailed one. This is why I recommend both, and not just one. For image processing, I have a few IP books both specific and general, but I really think the Gonzalez and Woods book is the best for beginners. Check it out here. ImageProcessingPlace. I didn't learn from this book, but I TA'd a class that used it and after reading it myself I promptly bought a copy. It is meant for beginners in image processing, and explains things on the pixel level without getting too abstract. The abstractions, however, are definitely helpful if you have an electrical engineering background and are already used to the math involved. From there I feel you have to get specific to find that good books that cover IP material well ... There are books on morphology, contours, etc. The machine learning part of CV is tough. It's still an area very much in the research and exploration phase, so it's kind of hard to find a book dedicated specifically to Machine Learning applications to Computer Vision. Just stick with the Szeliski book... I covers all the common ML applications to vision. I don't know much about machine learning as it's own field other than the Bishop book already mentioned, so I'll leave that to others.
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However... For more specific machine learning, if you are going to get into it, take a look at the book by Srinivasan and Rizzo: Computer Vision: Machine Learning Methods by S.R., M.R. and M.S. This is a “hands on” book, full of math examples, but a great beginner's book. If you're doing CV because you are interested in Machine Learning, I think this is the best book you can get. Other books I'd recommend are the other Srinivasan-Rizzo one I mentioned as well: Computer Vision, Vision Processing and Pattern Recognition Using Machine Learning Techniques by S. R., M. R. and M. S. There are some related books that cover other aspects of AI... I was in a class when Srinivasan was our instructor and talked about deep learning, and while its nice stuff, there are less accessible and a little intimidating books on machine learning that I would recommend to.
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