GPU-enabled high performance feature modeling for ATR applications

Computational methods for automatic target recognition are constrained by the need to analyze increasingly high-dimensional sensor data in real time. Parallel processing has the potential to speed up computational bottlenecks in many automatic target recognition (ATR) methods. We will implement para...

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Bibliographic Details
Main Authors: Dessauer, M P, Hitchens, J, Dua, S
Format: Conference Proceeding
Language:English
Subjects:
Computer vision
Feature extraction
Gabor filters
Graphics processing unit
Histograms
machine vision
object recognition
Optical filters
parallel processing
Target tracking
tracking
Online Access:Request full text
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Description
Summary:Computational methods for automatic target recognition are constrained by the need to analyze increasingly high-dimensional sensor data in real time. Parallel processing has the potential to speed up computational bottlenecks in many automatic target recognition (ATR) methods. We will implement parallelized versions of target tracking methods and discuss gains in algorithm completion time.
ISSN:0547-3578
2379-2027
DOI:10.1109/NAECON.2010.5712930