实例介绍
【实例简介】
这个是英文原版，2011的，是最经典的，和网上的两页一起pdf不一样！ Computer Vision: Algorithms and Applications 2011 你值得拥有
Richard szeliski Computer vision Algorithms and applications pringer Dr Richard Szelisk Microsoft research One microsoft way 98052-6399 Redmond washington USA szeliski@microsoft.com Series editors David gries Fred b. schneider Department of Computer Science Department of Computer Science Upson lall Upson hall Cornell Univcrsity Cornell university Ithaca. NY14853-7501USA Ithaca.NY14853-7501 USA ISSN 1868-0941 e-ISSN 1868-095X ISBN978-1-84882-934-3 e-ISBN978-1-84882935-0 DOI10.1007/978-1-84882935-0 Springer London Dordrecht Heidelberg New York British Library Cataloguing in Publication Data A catalogue record for this book is available from the british Library Library of Congress Control Numbcr: 2010936817 ag Apart from any fair dcaling for the purposes of rescarch or private study, or criticism or revicw, as permitted under the Copyright, Designs and Patents Act 1988, this publication may only be reproduced stored or transmitted, in any form or by any means, with the prior permission in writing of the publishers, or inl the case of reprographic reproduction in accordance with the leris of licenses issued b the Copyright Licensing Agency. Enquiries concerning reproduction outside those terms should be sent to the publishers The use of registered names, trademarks, etc, in this publication does not imply, even in the absence of a specific sliateInenl, that such naines are exenpt fruin the relevant laws and regulations and therefore free The publisher makes no representation, express or implied, with regard to the accuracy of the information contained in this book and cannot accept any legal responsibility or liability for any errors or omissions that may be made Printed on acid-free paper SpringerispartofSpringerScicncc+busincssMedia(www.springcr.com) This book is dedicated to my parents, Zdzistaw and Jadwiga ana iny family Lyn, Anne, and stephen 1 Introduction What is computer vision? a brief history Book overview· Sample syllabus· Notation MOUTI 2 Image formation 27 Geometric primitives and transformations o Photometric image formation o The digital camera Image processin g Point operators Linear filtering. More neighborhood operators Fourier transforms Pyramids and wavelets Geometric transformations Global optimization 4 Feature detection and matching 181 Points and patches o edges● Lines 5 Segmentation 235 Active contours Split and merge Mean shift and mode finding· Normalized cuts· Graph cuts and energy-based method 6 Feature-based alignment 273 2D and 3D feature-based alignment Pose estimation o Geometric intrinsic calibration 7 Structure from motion 303 Triangulation Two-frame structure from motion o Factorization· Bundle adjustment· Constrained structure and motion 8 Dense motion estimation 335 Translational alignment Parametric motion o Spline-based moti Optical fl Layered motion 9 Image stitching 375 Motion models Global alignment Compositing 10 Computational photography Photometric calibration High dynanic range imaging Super-resolution and blur removal Image matting and compositing Texture analysis and synthesis 11 Stereo correspondence 467 Epipolar geometry· Sparse correspondence· Dense correspondence· Local methods· Global optimization Multi-view stereo 12 3 reconstruction 505 Shape from x Active rangefinding Surface representations Point-based representations Volumetric representations Model-based reconstruction Recovering texture maps and albedos 13 Image-based rendering 543 View interpolation Layered depth images Light fields and Lumigraphs Environment mattes o Video-based rendering 14 Recognition 575 Object detection● Face recognition● Instance recognition Category recognition Context and scene understanding Recognition databases and test sets Preface The seeds for this book were first planted in 2001 when Steve Seitz at the university of Wash ington 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 3D 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 course I Co-taught with David Fleet in 2003. ) Similar curricula have 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 lI in Section 1.4.) This book also reflects mv 20 years experience doing computer vision research in corpo rate 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 solu tion 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 ither 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 gen eral reference to fundamental techniques and the 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 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 smaller mid-term projects, as well as more open-ended 【实例截图】
【核心代码】
这个是英文原版，2011的，是最经典的，和网上的两页一起pdf不一样！ Computer Vision: Algorithms and Applications 2011 你值得拥有
Richard szeliski Computer vision Algorithms and applications pringer Dr Richard Szelisk Microsoft research One microsoft way 98052-6399 Redmond washington USA szeliski@microsoft.com Series editors David gries Fred b. schneider Department of Computer Science Department of Computer Science Upson lall Upson hall Cornell Univcrsity Cornell university Ithaca. NY14853-7501USA Ithaca.NY14853-7501 USA ISSN 1868-0941 e-ISSN 1868-095X ISBN978-1-84882-934-3 e-ISBN978-1-84882935-0 DOI10.1007/978-1-84882935-0 Springer London Dordrecht Heidelberg New York British Library Cataloguing in Publication Data A catalogue record for this book is available from the british Library Library of Congress Control Numbcr: 2010936817 ag Apart from any fair dcaling for the purposes of rescarch or private study, or criticism or revicw, as permitted under the Copyright, Designs and Patents Act 1988, this publication may only be reproduced stored or transmitted, in any form or by any means, with the prior permission in writing of the publishers, or inl the case of reprographic reproduction in accordance with the leris of licenses issued b the Copyright Licensing Agency. Enquiries concerning reproduction outside those terms should be sent to the publishers The use of registered names, trademarks, etc, in this publication does not imply, even in the absence of a specific sliateInenl, that such naines are exenpt fruin the relevant laws and regulations and therefore free The publisher makes no representation, express or implied, with regard to the accuracy of the information contained in this book and cannot accept any legal responsibility or liability for any errors or omissions that may be made Printed on acid-free paper SpringerispartofSpringerScicncc+busincssMedia(www.springcr.com) This book is dedicated to my parents, Zdzistaw and Jadwiga ana iny family Lyn, Anne, and stephen 1 Introduction What is computer vision? a brief history Book overview· Sample syllabus· Notation MOUTI 2 Image formation 27 Geometric primitives and transformations o Photometric image formation o The digital camera Image processin g Point operators Linear filtering. More neighborhood operators Fourier transforms Pyramids and wavelets Geometric transformations Global optimization 4 Feature detection and matching 181 Points and patches o edges● Lines 5 Segmentation 235 Active contours Split and merge Mean shift and mode finding· Normalized cuts· Graph cuts and energy-based method 6 Feature-based alignment 273 2D and 3D feature-based alignment Pose estimation o Geometric intrinsic calibration 7 Structure from motion 303 Triangulation Two-frame structure from motion o Factorization· Bundle adjustment· Constrained structure and motion 8 Dense motion estimation 335 Translational alignment Parametric motion o Spline-based moti Optical fl Layered motion 9 Image stitching 375 Motion models Global alignment Compositing 10 Computational photography Photometric calibration High dynanic range imaging Super-resolution and blur removal Image matting and compositing Texture analysis and synthesis 11 Stereo correspondence 467 Epipolar geometry· Sparse correspondence· Dense correspondence· Local methods· Global optimization Multi-view stereo 12 3 reconstruction 505 Shape from x Active rangefinding Surface representations Point-based representations Volumetric representations Model-based reconstruction Recovering texture maps and albedos 13 Image-based rendering 543 View interpolation Layered depth images Light fields and Lumigraphs Environment mattes o Video-based rendering 14 Recognition 575 Object detection● Face recognition● Instance recognition Category recognition Context and scene understanding Recognition databases and test sets Preface The seeds for this book were first planted in 2001 when Steve Seitz at the university of Wash ington 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 3D 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 course I Co-taught with David Fleet in 2003. ) Similar curricula have 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 lI in Section 1.4.) This book also reflects mv 20 years experience doing computer vision research in corpo rate 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 solu tion 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 ither 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 gen eral reference to fundamental techniques and the 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 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 smaller mid-term projects, as well as more open-ended 【实例截图】
【核心代码】
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