On a Parallel Implementation of Geometric Hashing on the Connection Machine
Language: English
Published by Forgotten Books, 2018
- Softcover
- New
Seller: Forgotten Books, London, United KingdomForgotten Books
AbeBooks seller since June 2, 2021
Condition: New
US$ 17.15
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Print on Demand. This book explores the creation of a parallel implementation for geometric hashing on the Connection Machine. Geometric hashing is a powerful method for model-based recognition, allowing for the fast, accurate detection of objects in complex scenes by creating a hash table data structure that encodes information about the models in a highly redundant, multiple-viewpoint way. The author provides a scalable parallel algorithm (requiring Mn3 processors), a novel remapping function to achieve uniform distribution of entries over the rectangular hash table, and an innovative technique to fully exploit symmetries due to combinations of basis points, proving that no more than a few tens of probes are needed to achieve accurate recognition. This book is a significant contribution to the field of computer vision, providing valuable insights into the design and implementation of parallel algorithms for geometric hashing. This book is a reproduction of an important historical work, digitally reconstructed using state-of-the-art technology to preserve the original format. In rare cases, an imperfection in the original, such as a blemish or missing page, may be replicated in the book.…
Seller Inventory # 9781332092260_0
- Title
- On a Parallel Implementation of Geometric Hashing on the Connection Machine
- Author
- Isidore Rigoutsos, Robert Hummel
- Publisher
- Forgotten Books
- Publication year
- 2018
- Condition
- New
- Book Type
- print-on-demand item
- Binding
- Paperback
- Language
- English
- ISBN 10
- 1332092268
- ISBN 13
- 9781332092260
- Seller catalogs
- Statistics
In this study, the authors describe a parallel implementation of geometric hashing on the Connection Machine. The work covers how a model database is prepared off-line and how scene features are matched quickly in real time. It highlights the balance between computation and storage, showing how parallelism reduces the number of tasks while increasing memory use. The report also discusses how the approach handles rigid and similarity transforms and what happens when noise appears in the input.
- How the hash-based approach distributes work across many processors for fast recognition
- The role of rehashing, foldings, and symmetries in saving time and memory
- Performance results under different data distributions, scene sizes, and noise levels
- Practical insights for implementing parallel vision algorithms on large, multi-processor systems
Ideal for readers of high‑performance computing, parallel algorithms, and model-based vision who want a concrete look at scalable object recognition techniques.
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Forgotten Books
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Forgotten Books’ Classic Reprint Series utilizes the latest technology to regenerate facsimiles of historically important writings. Careful attention has been made to accurately preserve the original format of each page whilst digitally enhancing the quality of the aged text.
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