On a Parallel Implementation of Geometric Hashing on the Connection Machine (Classic Reprint): Technical Report 554: Technical Report 554 (Classic Reprint) - Tapa blanda
Sinopsis
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.
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Excerpt from On a Parallel Implementation of Geometric Hashing on the Connection Machine: Technical Report 554
In geometric hashing, the collection of models are used in a preprocessing phase (executed off-line and only once) in order to build a hash table data structure. The data structure encodes the information about the models in a highly redundant multiple-viewpoint way. During the recognition phase, when presented with a scene and extracted features, the hash table data structure is used to index geometric properties of the scene features to candidate matching models. A search is still required over features in the scene. However, the geometric hashing scheme no longer requires a search over the features in the model sets. The result is that the recognition phase offers computational efficiencies over more traditional model-based vision methods. As we describe elsewhere there is parallelism available in the search over scene features, and there is parallelism in the indexing process inherent within the hashing scheme. Further, the computational efficiencies afforded by geometric hashing translate into a reduction in the number of independent tasks that may be simultaneously conducted (at the expense of increased storage requirements), thereby decreasing the number of processors that are needed in a parallel implementation.
In this paper, we report on an implementation on the Connection Machine of one of the algorithms described in We also describe a number of modifications that are useful for efficient parallel implementation of the method for the particular case of point features. The last-section of the paper presents the results of a number of experiments that were carried out using this implementation. These experiments had a two-fold purpose: first, we evaluated the real time performance of the implementation with a large database of models; second, the behavior of the method in the case of rigid or similarity transforms was examined, as a function of the noise present in the input. Other statistics are also presented.
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Forgotten Books publishes hundreds of thousands of rare and classic books. Find more at www.forgottenbooks.com
This book is a reproduction of an important historical work. Forgotten Books uses state-of-the-art technology to digitally reconstruct the work, preserving the original format whilst repairing imperfections present in the aged copy. In rare cases, an imperfection in the original, such as a blemish or missing page, may be replicated in our edition. We do, however, repair the vast majority of imperfections successfully; any imperfections that remain are intentionally left to preserve the state of such historical works.
Reseña del editor
Excerpt from On a Parallel Implementation of Geometric Hashing on the Connection Machine: Technical Report 554
We report on a scalable parallel implementation of geometric hashing on a Connection Machine. The algorithm that is employed has been described in [9]. Using the resulting implementation, it is possible to recognize models consisting of patterns of points embedded in scenes, independent of translation, rotation, and scale changes, when there are thousands of models containing approximately 16 points each, with scenes consisting of hundreds of points, where most of the scene points are spurious noise points, and where embedded model points in the scene may be obscured or misplaced. With 1024 models and a scene of 200 points, the implementation yields an execution time of 70 milliseconds per probe on a 64K-processor Cm-2 parallel computer. Most of the execution time is taken in performing histograms of (model, basis-set) records. The algorithm is scalable, yielding an expected execution time that is O(log² M + log S log M) in a Mn³-processor hypercube-connected SIMD machine such as the Connection Machine. M is the number of models, n is the number of points per model, and S is the number of scene points. We also describe and report on a series of experiments for both the similarity and rigid transformation cases; these experiments provide information about detection and false alarm rates for varying amounts of noise in the input.
About the Publisher
Forgotten Books publishes hundreds of thousands of rare and classic books. Find more at www.forgottenbooks.com
This book is a reproduction of an important historical work. Forgotten Books uses state-of-the-art technology to digitally reconstruct the work, preserving the original format whilst repairing imperfections present in the aged copy. In rare cases, an imperfection in the original, such as a blemish or missing page, may be replicated in our edition. We do, however, repair the vast majority of imperfections successfully; any imperfections that remain are intentionally left to preserve the state of such historical works.
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On a Parallel Implementation of Geometric Hashing on the Connection Machine
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Paperback. Condición: New. 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. print-on-demand item. Nº de ref. del artículo: 9781332092260_0
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On a Parallel Implementation of Geometric Hashing on the Connection Machine Technical Report 554 Classic Reprint
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On a Parallel Implementation of Geometric Hashing on the Connection Machine Technical Report 554 Classic Reprint
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On a Parallel Implementation of Geometric Hashing on the Connection Machine : Technical Report 554 (Classic Reprint)
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ISBN 13: 9781332092260
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