On a Parallel Implementation of Geometric Hashing on the Connection Machine : Technical Report 554 (Classic Reprint)
Language: English
Published by Forgotten Books, 2015
- Softcover
- Used
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Zustand: Sehr gut | Sprache: Englisch | Produktart: Bücher | Keine Beschreibung verfügbar.
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- Title
- On a Parallel Implementation of Geometric Hashing on the Connection Machine : Technical Report 554 (Classic Reprint)
- Author
- Isidore Rigoutsos
- Publisher
- Forgotten Books
- Publication year
- 2015
- Condition
- Sehr gut
- Binding
- Soft cover
- Language
- English
- ISBN 10
- 1332092268
- ISBN 13
- 9781332092260
- Seller catalogs
- Bücher
Explore how geometric hashing speeds object recognition on parallel hardware.
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.
JSON_OBJECT_PLACEHOLDER
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.
JSON_OBJECT_PLACEHOLDER
"Synopsis" may belong to another edition of this title.
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