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Integrating Graphics and Vision for Object Recognition

By: Mark R. Stevens, J. Ross Beveridge

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Hardcover | 31 October 2000

At a Glance

Hardcover
204 Pages

Dimensions(cm)
23.39 x 15.6 x 1.27

Hardcover

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Integrating Graphics and Vision for Object Recognition serves as a reference for electrical engineers and computer scientists researching computer vision or computer graphics.
Computer graphics and computer vision can be viewed as different sides of the same coin. In graphics, algorithms are given knowledge about the world in the form of models, cameras, lighting, etc., and infer (or render) an image of a scene. In vision, the process is the exact opposite: algorithms are presented with an image, and infer (or interpret) the configuration of the world. This work focuses on using computer graphics to interpret camera images: using iterative rendering to predict what should be visible by the camera and then testing and refining that hypothesis.
Features of the book include:

Many illustrations to supplement the text;
A novel approach to the integration of graphics and vision;
Genetic algorithms for vision;
Innovations in closed loop object recognition.

Integrating Graphics and Vision for Object Recognition will be of interest to research scientists and practitioners working in fields related to the topic. It may also be used as an advanced-level graduate text.

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You Can Find This Book In

Non-Fiction Computing & I.T.Graphical & Digital Media Applications Computer Science Artificial Intelligence Computer Vision Image Processing

List of Figures	p. vii
List of Tables	p. xi
Introduction	p. 1
What Does it Mean to Interpret a Scene?	p. 2
Using Partial Scene Models for Object Recognition	p. 4
Example Problem Domains	p. 7
Assumptions Made	p. 8
Overview	p. 9
Previous Work	p. 11
Contributing Individuals	p. 13
Our Place in the Pipeline	p. 15
Geometric Feature Matching	p. 15
Augmented Geometric Matching	p. 21
Appearance Matching	p. 25
RMR: Fusing Geometry and Appearance	p. 29
Render: Predicting Scenes	p. 33
Overview of the RMR Dataset	p. 35
Formally Defining the Scene Configuration	p. 37
The Model Representation	p. 44
Rendering Objects Using the Scene Configuration	p. 48
Predicting a Simple Background Model	p. 55
Match: Comparing Images	p. 57
Defining Specific Error Functions	p. 59
Comparing Error Functions	p. 80
Refine: Iterative Search	p. 97
Desirable Properties of a Search Algorithm	p. 98
Defining Specific Search Algorithms	p. 101
Comparing Search Algorithms	p. 110
Evaluation	p. 131
An Overview of the Results	p. 132
Evaluating Performance	p. 133
Relating Dependent and Independent Variables	p. 145
Summary	p. 150
Conclusions	p. 153
Future Work	p. 155
Appendices	p. 157
Generating Scene Hypotheses	p. 157
Object Detection and Pose Indexing	p. 157
Detection based on Color Decision Trees	p. 159
Pose Indexing	p. 163
Index	p. 179
Table of Contents provided by Syndetics. All Rights Reserved.

Integrating Graphics and Vision for Object Recognition

At a Glance

Hardcover

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