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July 30, 2014 | History

Representations and techniques for 3D object recognition and scene interpretation 1 edition

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Representations and techniques for 3D object recognition and scene int ...
Derek Hoiem

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Subjects
Computer vision, Three-dimensional imaging
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Representations and techniques for 3D object recognition and scene interpretation
Derek Hoiem, Silvio Savarese

Published 2011 by Morgan & Claypool in San Rafael, Calif. (1537 Fourth Street, San Rafael, CA 94901 USA) .
Written in English.

About the Book

One of the grand challenges of artificial intelligence is to enable computers to interpret 3D scenes and objects from imagery. This book organizes and introduces major concepts in 3D scene and object representation and inference from still images, with a focus on recent efforts to fuse models of geometry and perspective with statistical machine learning.

Table of Contents

Preface
Acknowledgments
Figure credits
Part I. Interpretation of physical space from an image
1. Background on 3D scene models
1.1 Theories of vision
1.1.1 Depth and surface perception
1.1.2 Awell-organized scene
1.2 Early computer vision and AI
1.3 Modern computer vision
2. Single-view geometry
2.1 Consequences of projection
2.2 Perspective projection with pinhole camera: 3D to 2D
2.3 3D measurement from a 2D image
2.4 Automatic estimation of vanishing points
2.5 Summary of key concepts
3. Modeling the physical scene
3.1 Elements of physical scene understanding
3.1.1 Elements
3.1.2 Physical interactions
3.2 Representations of scene space
3.2.1 Scene-level geometric description
3.2.2 Retinotopic maps
3.2.3 Highly structured 3D models
3.2.4 Loosely structured models: 3D point clouds and meshes
3.3 Summary
4. Categorizing images and regions
4.1 Overview of image labeling
4.2 Guiding principles
4.2.1 Creating regions
4.2.2 Choosing features
4.2.3 Classifiers
4.2.4 Datasets
4.3 Image features
4.3.1 Color
4.3.2 Texture
4.3.3 Gradient-based
4.3.4 Interest points and bag of words
4.3.5 Image position
4.3.6 Region shape
4.3.7 Perspective
4.4 Summary
5. Examples of 3D scene interpretation
5.1 Surface layout and automatic photo pop-up
5.1.1 Intuition
5.1.2 Geometric classes
5.1.3 Approach to estimate surface layout
5.1.4 Examples of predicted surface layout
5.1.5 3D reconstruction using the surface layout
5.2 Make3D: depth from an image
5.2.1 Predicting depth and orientation
5.2.2 Local constraints and priors
5.2.3 Results
5.3 The room as a box
5.3.1 Algorithm
5.3.2 Results
5.4 Summary
Part II. Recognition of 3D objects from an image
6. Background on 3D recognition
6.1 Human vision theories
6.1.1 The Geon theory
6.1.2 2D-view specific templates
6.1.3 Aspect graphs
6.1.4 Computational theories by 3D alignment
6.1.5 Conclusions
6.2 Early computational models
7. Modeling 3D objects
7.1 Overview
7.2 Single instance 3D category models
7.2.1 Single instance 2D view-template models
7.2.2 Single instance 3D models
7.3 Mixture of single-view models
7.4 2-1/2D layout models
7.4.1 2-1/2D layout by ISM models
7.4.2 2-1/2D layout by view-invariant parts
7.4.3 2-1/2D hierarchical layout models
7.4.4 2-1/2D layout by discriminative aspects
7.5 3D layout models
7.5.1 3D layout models constructed upon 3D prototypes
7.5.2 3D layout models without 3D prototypes
8. Recognizing and understanding 3D objects
8.1 Datasets
8.2 Supervision and initialization
8.3 Modeling, learning and inference strategies
9. Examples of 2D 1/2 layout models
9.1 Linkage structure of canonical parts
9.1.1 The view-morphing formulation
9.1.2 Supervision
9.2 View-morphing models
9.2.1 Learning the model
9.2.2 Detection and viewpoint classification
9.2.3 Results
9.3 Conclusions
Part III. Integrated 3D scene interpretation
10. Reasoning about objects and scenes
10.1 Objects in perspective
10.1.1 Object size
10.1.2 Appearance features
10.1.3 Interaction between objects and scene via object scale and pose
10.2 Scene layout
10.3 Occlusion
10.4 Summary
11. Cascades of classifiers
11.1 Intrinsic images revisited
11.1.1 Intrinsic image representation
11.1.2 Contextual interactions
11.1.3 Training and inference
11.1.4 Experiments
11.2 Feedback-enabled cascaded classification models
11.2.1 Algorithm
11.2.2 Experiments
11.3 Summary
12. Conclusion and future directions
Bibliography
Authors' biographies.

Edition Notes

Part of: Synthesis digital library of engineering and computer science.

Series from website.

Includes bibliographical references (p. 125-146).

Abstract freely available; full-text restricted to subscribers or individual document purchasers.

Also available in print.

Mode of access: World Wide Web.

System requirements: Adobe Acrobat Reader.

Series
 Synthesis lectures on artificial intelligence and machine learning -- # 15
Other Titles
 Synthesis digital library of engineering and computer science.

Classifications

Dewey Decimal Class
 006.37
Library of Congress
 TA1634 .H657 2011

The Physical Object

Format
 [electronic resource] /

ID Numbers

Open Library
 OL25567671M
Internet Archive
 representationst00hoie
ISBN 13
 9781608457298, 9781608457281

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