Integrating Graphics and Vision for Object Recognition

Stevens, Mark R.; Beveridge, J. Ross (Department of Computer Science, Colorado State University, USA)

ISBN 10: 0792372077 / ISBN 13: 9780792372073

Editore: Kluwer Academic Publishers, 2000

Riguardo questo articolo

Serves as a reference for electrical engineers and computer scientists researching computer vision or computer graphics. 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. Series: The Springer International Series in Engineering and Computer Science. Num Pages: 184 pages, biography. BIC Classification: UYQV. Category: (P) Professional & Vocational; (UP) Postgraduate, Research & Scholarly; (UU) Undergraduate. Dimension: 235 x 155 x 12. Weight in Grams: 1030. . 2000. Hardback. . . . .

Codice articolo V9780792372073

Dati bibliografici

• Titolo: Integrating Graphics and Vision for Object Recognition
• Editore: Kluwer Academic Publishers
• Data di pubblicazione: 2000
• Lingua: Inglese
• ISBN 10: 0792372077
• ISBN 13: 9780792372073
• Rilegatura: Rilegato
• Condizione: New

Descrizione articolo

Riassunto

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.

Contenuti

List of Figures. List of Tables. 1. Introduction. 2. Previous Work. 3. Render: Predicting Scenes. 4. Match: Comparing Images. 5. Refine: Iterative Search. 6. Evaluation. 7. Conclusions. Appendices. Index.