Application of parallel-hierarchical transformations for rapid recognition of dynamic images based on GPU technology

DOI

10.2991/cse.2013.51How to use a DOI?

Keywords

parallel-hierarchical (PH) network, laser beam spot images, parallel-hierarchical (PH) transformation, fast recognition of dynamic images, GPU technology.

Abstract

The paper presents a method of parallel-hierarchical transformations for rapid recognition of dynamic images using GPU technology. Direct parallel-hierarchical transformations based on cluster CPU-and GPU-oriented hardware platform. Mathematic models of training of the parallel hierarchical (PH) network for the transformation are developed, as well as a training method of the PH network for recognition of dynamic images. This research is most topical for problems on organizing high-performance computations of super large arrays of information designed to implement multi-stage sensing and processing as well as compaction and recognition of data in the informational structures and computer devices. This method has such advantages as high performance through the use of recent advances in parallelization, possibility to work with images of ultra dimension, ease of scaling in case of changing the number of nodes in the cluster, auto scan of local network to detect compute nodes.

Copyright

© 2013, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - CONF
AU  - Leonid Timchenko
AU  - Nataliya Kokriatskaia
AU  - Andriy Yarovyy
AU  - Igor Ivasyuk
AU  - Alyona Denysova
PY  - 2013/07
DA  - 2013/07
TI  - Application of parallel-hierarchical transformations for rapid recognition of dynamic images based on GPU technology
BT  - Proceedings of the 2nd International Conference on Advances in Computer Science and Engineering (CSE 2013)
PB  - Atlantis Press
SP  - 226
EP  - 230
SN  - 1951-6851
UR  - https://doi.org/10.2991/cse.2013.51
DO  - 10.2991/cse.2013.51
ID  - Timchenko2013/07
ER  -