|
![]() |
|||
|
||||
OverviewIntegrating 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. Full Product DetailsAuthor: Mark R. Stevens , J. Ross BeveridgePublisher: Springer-Verlag New York Inc. Imprint: Springer-Verlag New York Inc. Edition: Softcover reprint of hardcover 1st ed. 2001 Volume: 589 Dimensions: Width: 15.50cm , Height: 1.00cm , Length: 23.50cm Weight: 0.314kg ISBN: 9781441948601ISBN 10: 1441948600 Pages: 184 Publication Date: 07 December 2010 Audience: Professional and scholarly , Professional & Vocational Format: Paperback Publisher's Status: Active Availability: Awaiting stock ![]() The supplier is currently out of stock of this item. It will be ordered for you and placed on backorder. Once it does come back in stock, we will ship it out for you. Table of Contents1. 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.ReviewsAuthor InformationTab Content 6Author Website:Countries AvailableAll regions |