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An Invitation to 3-D Vision

An Invitation to 3-D Vision
Authors: Yi Ma, Stefano Soatto, Jana Kosecka, S. Shankar Sastry
Publisher: Springer
Category: Book

List Price: $79.95
Buy New: $55.97
You Save: $23.98 (30%)



New (21) Used (14) from $37.45

Avg. Customer Rating: 3.5 out of 5 stars 3 reviews
Sales Rank: 293997

Media: Hardcover
Number Of Items: 1
Pages: 552
Shipping Weight (lbs): 2
Dimensions (in): 9.3 x 6.1 x 1.2

ISBN: 0387008934
Dewey Decimal Number: 006.37
EAN: 9780387008936
ASIN: 0387008934

Publication Date: June 17, 2005
Availability: Usually ships in 1-2 business days
Condition: SHIPS FAST! via UPS(AK/HI Priority Mail) within 24 hours/ NEW book

Also Available In:

  • Digital - An Invitation to 3-D Vision: From Images to Geometric Models (Interdisciplinary Applied Mathematics)

Accessories:

  • Metaheuristics for Hard Optimization: Methods and Case Studies
  • Genetic Programming Theory and Practice IV (Genetic and Evolutionary Computation)

Similar Items:

  • Multiple View Geometry in Computer Vision
  • Introductory Techniques for 3-D Computer Vision
  • Handbook of Mathematical Models in Computer Vision
  • Computer Vision: A Modern Approach
  • Pattern Recognition and Machine Learning (Information Science and Statistics)

Editorial Reviews:

Product Description
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. Following a brief introduction, Part I provides background materials for the rest of the book. The two fundamental transformations, namely rigid body motion and perspective projection are introduced and image formation and feature extraction discussed. Part II covers the classic theory of two view geometry based on the so-called epipolar constraint. Part III shows that a more proper tool for studying the geometry of multiple views is the so- called rank considtion on the multiple view matrix. Part IV develops practical reconstruction algorithms step by step as well as discusses possible extensions of the theory. Exercises are provided at the end of each chapter. Software for examples and algorithms are available on the author's website.


Customer Reviews:

4 out of 5 stars Good 3-D vision book with emphasis on applications   December 10, 2005
 6 out of 7 found this review helpful

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 - Introduction
Ch 2 - Representation of a 3D Moving Scene
Ch 3 - Image Formation
Ch 4 - Image Primitives and Correspondence
Ch 5 - Reconstruction from Two Calibrated Views
Ch 6 - Reconstruction from Two Uncalibrated Views
Ch 7 - Segmentation of Multiple Moving Objects from Two Views
Ch 8 - Multiple View Geometry of Points and Lines
Ch 9 - Extension to General Incidence Relations
Ch 10- Geometry and Reconstruction From Symmetry
Ch 11- Step by Step Building of a 3D Model from Images
Ch 12- Visual Feedback



2 out of 5 stars A Poor Rewrite of the book: Multiple View Geometry, by Hartley & Zisserman   September 28, 2005
 7 out of 11 found this review helpful

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.


4 out of 5 stars a must-have book for computer vision students & researchers   March 30, 2004
 14 out of 15 found this review helpful

The authors do an outstanding job of balancing theory and practice in this book. In particular, Ch. 11 (Step-by-Step Building of a 3-D Model from Images) is a gem. The new student of computer vision that finds Structure from Motion (SFM) daunting should read this chapter first to build motivation by means of seeing concrete examples.
What's most notable about this book is its thoroughness. The authors humbly get their hands dirty with crucial low-level matters like interest point detection and feature tracking. In Ch. 5 and 6 (calibrated and uncalibrated reconstruction) you'll get an excellent treatment of the necessary background in these areas, along with numerous new insights and `nuggets.' This is particularly true in their treatment of homographies. Ch. 2 has all the material on rigid body motion and the exponential map that students used to need to get from Murray, Li and Sastry, once again with substantial added value.
The exercises in all the chapters are very well thought out, and they -- together with the clearly written Algorithm Boxes -- greatly simplify the job of the instructor. (I am currently using this as the text for my graduate level class this quarter.) The appendices (especially the one on Linear Algebra) also help to make this a self-contained resource for SFM.
I haven't gotten into the material on multi-body motion and n>2 views, so I can't comment on those parts(...)In summary, this is a great book, well worth the money.


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