Computer Vision: A Modern Approach

About

Features

• Broad coverage—Coverage of a wide range of topics allows customization to fit instructor, student, and course needs.

  • Allows instructors to select the most relevant topics for their students and encourages students to enrich their coursework by reading information on other computer vision topics.

• Most comprehensive and up-to-date text on computer vision—Includes essential topics that either reflect practical significance or are of theoretical importance.

  • Provides students with the most coherent synthesis of current views and teaches them successful techniques for building applications.

• Depth of the material accessible to various levels of students—Topics are discussed in substantial and increasing depth.

  • While the first half of each chapter is accessible to undergraduates, a good grasp of each chapter provides students with a professional level of skill and knowledge.

• Application surveys—Describe numerous important application areas such as image based rendering and digital libraries.

  • Teaches students about practical use of techniques and helps them gain insight into the demands of applications.

• Many important algorithms broken down and illustrated in pseudo code.

  • Enables students to build working systems easily as they can understand the construction of the final application.

• Excellent pedagogy throughout the text—Includes numerous worked examples, 150 exercises, programming assignments, and extensive illustrations.

  • Provides students with ample opportunity to apply the concepts in the text.

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Description

• Copyright 2003
• Edition: 1st

• Book
• ISBN-10: 0-13-085198-1
• ISBN-13: 978-0-13-085198-7

Computer Vision:
A Modern Approach

This extraordinary book gives a uniquely modern view of computer vision. Offering a general survey of the whole computer vision enterprise along with sufficient detail for readers to be able to build useful applications, this book is invaluable in providing a strategic overview of computer vision. With extensive use of probabalistic methods— topics have been selected for their importance, both practically and theoretically—the book gives the most coherent possible synthesis of current views, emphasizing techniques that have been successful in building applications. Readers engaged in computer graphics, robotics, image processing, and imaging in general will find this text an informative reference.

KEY FEATURES

• Application Surveys—Numerous examples, including Image Based Rendering and Digital Libraries
• Boxed Algorithms—Key algorithms broken out and illustrated in pseudo code
• Self-Contained—No need for other references
• Extensive, Detailed Illustrations—Examples of inputs and outputs for current methods
• Programming Assignments—50 programming assignments and 150 exercises

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Sample Content

Table of Contents

I. IMAGE FORMATION AND IMAGE MODELS.

 1. Cameras.
 2. Geometric Camera Models.
 3. Geometric Camera Calibration.
 4. Radiometry - Measuring Light.
 5. Sources, Shadows and Shading.
 6. Color.

II. EARLY VISION: JUST ONE IMAGE.

 7. Linear Filters.
 8. Edge Detection.
 9. Texture.

III. EARLY VISION: MULTIPLE IMAGES.

10. The Geometry of Multiple Views.
11. Stereopsis.
 12. Affine Structure from Motion.
13. Projective Structure from Motion.

IV. MID-LEVEL VISION.

14. Segmentation By Clustering.
15. Segmentation By Fitting a Model.
16. Segmentation and Fitting Using Probabilistic Methods.
17. Tracking with Linear Dynamic Models.

V. HIGH-LEVEL VISION: GEOMETRIC MODELS.

18. Model-Based Vision.
19. Smooth Surfaces and Their Outlines.
20. Aspect Graphs.
21. Range Data.

VI. HIGH-LEVEL VISION: PROBABILISTIC AND INFERENTIAL METHODS.

22. Finding Templates Using Classifiers.
23. Recognition By Relations Between Templates.
24. Geometric Templates From Spatial Relations.

VII. APPLICATIONS.

25. Application: Finding in Digital Libraries.
26. Application: Image-Based Rendering.

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