Integrating Graphics and Vision for Object Recognition
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.
1100006771
Integrating Graphics and Vision for Object Recognition
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.
169.99 In Stock
Integrating Graphics and Vision for Object Recognition

Integrating Graphics and Vision for Object Recognition

Integrating Graphics and Vision for Object Recognition

Integrating Graphics and Vision for Object Recognition

Hardcover(2001)

$169.99 
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Overview

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.

Product Details

ISBN-13: 9780792372073
Publisher: Springer US
Publication date: 10/31/2000
Series: The Springer International Series in Engineering and Computer Science , #589
Edition description: 2001
Pages: 184
Product dimensions: 6.10(w) x 9.25(h) x 0.02(d)

Table of Contents

1. Introduction.- 2. Previous Work.- 3. Render: Predicting Scenes.- 4. Match: Comparing Images.- 5. Refine: Iterative Search.- 6. Evaluation.- 7. Conclusions.- Appendices.- A— Generating Scene Hypotheses.- 1. Object Detection and Pose Indexing.- 2. Detection based on Color Decision Trees.- 3. Pose Indexing.
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