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An Invitation to 3-D Vision: From Images to Geometric Models (Interdisciplinary Applied Mathematics)
Get Free Ebook An Invitation to 3-D Vision: From Images to Geometric Models (Interdisciplinary Applied Mathematics)
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Review
From the reviews: "Computer vision is invading our daily lives … . Covering all the aspects would be too vast an area to cover in one book, so here, the authors concentrated on the specific goal of recovering the geometry of a 3D object … . The 22 pages of references form a good guide to the literature. The authors found an excellent balance between a thorough mathematical treatment and the applications themselves. … the text will be a pleasure to read for students … ." (Adhemar Bultheel, Bulletin of the Belgian Mathematical Society, Vol. 12 (2), 2005) "This is primarily a textbook of core principles, taking the reader from the most basic concepts of machine vision … to detailed applications, such as autonomous vehicle navigation. … It is a clearly written book … . Everything that is required is introduced … . an entirely self-contained work. … The book is aimed at graduate or advanced undergraduate students in electrical engineering, computer science, applied mathematics, or indeed anyone interested in machine vision … . is highly recommended." (D.E. Holmgren, The Photogrammetric Record, 2004) "This very interesting book is a great book teaching how to go from two-dimensional (2D)-images to three-dimensional (3D)-models of the geometry of a scene. … A good part of this book develops the foundations of an appropriate mathematical approach necessary for solving those difficult problems. … Exercises (drill exercises, advanced exercises and programming exercises) are provided at the end of each chapter." (Hans-Dietrich Hecker, Zentralblatt MATH, Vol. 1043 (18), 2004) "This book gives senior undergraduate and beginning graduate students and researchers in computer vision, applied mathematics, computer graphics, and robotics a self-contained introduction to the geometry of 3D vision. That is the reconstruction of 3D models of objects from a collection of 2D images. … Exercises are provided at the end of each chapter. Software for examples and algorithms are available on the author’s website." (Daniel Leitner, Simulation News Europe, Vol. 16 (1), 2006)
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From the Back Cover
Endowing machines with a sense of vision has been a dream of scientists and engineers alike for over half a century. Only in the past decade, however, has the geometry of vision been understood to the point where this dream becomes attainable, thanks also to the remarkable progress in imaging and computing hardware. This book addresses a central problem in computer vision -- how to recover 3-D structure and motion from a collection of 2-D images -- using techniques drawn mainly from linear algebra and matrix theory. The stress is on developing a unified framework for studying the geometry of multiple images of a 3-D scene and reconstructing geometric models from those images. The book also covers relevant aspects of image formation, basic image processing, and feature extraction. The authors bridge the gap between theory and practice by providing step-by-step instructions for the implementation of working vision algorithms and systems. Written primarily as a textbook, the aim of this book is to give senior undergraduate and beginning graduate students in computer vision, robotics, and computer graphics a solid theoretical and algorithmic foundation for future research in this burgeoning field. It is entirely self-contained with necessary background material covered in the beginning chapters and appendices, and plenty of exercises, examples, and illustrations given throughout the text.
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Product details
Series: Interdisciplinary Applied Mathematics (Book 26)
Hardcover: 528 pages
Publisher: Springer; 1st Edition edition (June 17, 2005)
Language: English
ISBN-10: 9780387008936
ISBN-13: 978-0387008936
ASIN: 0387008934
Product Dimensions:
6.1 x 1.2 x 9.2 inches
Shipping Weight: 2 pounds (View shipping rates and policies)
Average Customer Review:
3.8 out of 5 stars
8 customer reviews
Amazon Best Sellers Rank:
#567,994 in Books (See Top 100 in Books)
Chap 11 is good, but the rest of the book is very heavy on the math and proofs. Many exercises are to prove something, e.g. ...left as an exercise for the reader. Fundamental concepts get drown out by the mathematics.
Although the mathematical info is relevant, the figures to show results come in low quality.
After going through a computer vision class with a very sub-par textbook, I still did not have a good grasp of epipolar geometry and camera calibration. This book gives a very nice treatment of these subjects.
We used this book at Duke University for our graduate course in 3D reconstruction. It is well written, although sometimes the notation is flawed.
After read the first pages of this book, I found it a littlehard to read, It would be a good idea to read first "IntroductoryTechniques for 3-D Computer Vision" and then this book.
I really liked this book. However, I use it for vision issues as they relate to robotics rather than as an introductory text on 3D vision. If a general or introductory textbook on 3D computer vision is what you desire, then you might be better off with "Multiple View Geometry in Computer Vision" by Hartley or my personal favorite, "Introductory Techniques for 3-D Computer Vision" by Trucco and Verri. For individuals studying robotic vision, many parts of this book are useful not only for characterizing vision, but for putting together algorithms and equations that are useful for describing robotic motion in general. For example, chapter two of the book collects equations and algorithms that are very useful in describing forward kinematics. Chapters five through ten cover all of the considerations and algorithms needed to produce a 3D image from a collection of images taken from different viewpoints. Chapter eleven applies this knowledge with sequential instructions on building a 3D image from a group of images. Chapter twelve has a second application that shows how to perform autonomous control of a moving vehicle via video feedback. The appendices have some very good information on linear algebra as it relates to computer vision as well as details on the Kalman filter, which is also of great interest to those of us who are interested in computational robotics. Algorithms are blocked out and explained in logical steps throughout the book, and it also has very good exercises at the end of each chapter as well as short examples throughout each chapter, although the notation can sometimes be a little confusing. I would therefore recommend this book especially to those readers who are interested in merging their knowledge of robotics with their knowledge of basic computer vision into creating sophisticated applications. However, this book is by no means an introduction to computer vision. The table of contents is as follows:Ch 1 - IntroductionCh 2 - Representation of a 3D Moving SceneCh 3 - Image FormationCh 4 - Image Primitives and CorrespondenceCh 5 - Reconstruction from Two Calibrated ViewsCh 6 - Reconstruction from Two Uncalibrated ViewsCh 7 - Segmentation of Multiple Moving Objects from Two ViewsCh 8 - Multiple View Geometry of Points and LinesCh 9 - Extension to General Incidence RelationsCh 10- Geometry and Reconstruction From SymmetryCh 11- Step by Step Building of a 3D Model from ImagesCh 12- Visual Feedback
This book views the underlying mathematics as the purpose, and the subject of 3D vision as some application; that makes the book hard do read. I am saying that as a PhD in applied mathematics who knows very well the math in the book. The book can rant on and on with pagefuls of formulas and derivations before explaining in a technical language what that means for 3D vision, instead of stating what the problem is, making the case for what needs to be done, and then using the mathematics to get there.This is a good reference book if you already know the subject and want to deepen your knowledge, rather than something you'd use as an entry point into the field of 3D vision.
This is a poor rewrite of a much better book, Multiple View Geometry in Computer Vision, by Hartley & Zisserman. It fails to clarify any of the more difficult concepts presented by Hartley & Zisserman and is far less complete in the treatment of the subject matter. I can not recommend this book when a better written, more thorough treatment is already available from Hartley & Zisserman.
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