Pragmatic Genetic Programming strategy for the problem of vehicle detection in airborne reconnaissance

A Genetic Programming (GP) method uses multiple runs, data decomposition stages, to evolve a hierarchical set of vehicle detectors for the automated inspection of infrared line scan imagery that has been obtained by a low flying aircraft. The performance on the scheme using two different sets of GP...

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Bibliographic Details
Published in:Pattern recognition letters 2006-08, Vol.27 (11), p.1275-1288
Main Authors: Howard, Daniel, Roberts, Simon C., Ryan, Conor
Format: Article
Language:English
Subjects:
Discrete Fourier transform
Genetic Programming
Machine vision
Method of stages
Object detection
Reconnaissance
Vehicle detection
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Summary:A Genetic Programming (GP) method uses multiple runs, data decomposition stages, to evolve a hierarchical set of vehicle detectors for the automated inspection of infrared line scan imagery that has been obtained by a low flying aircraft. The performance on the scheme using two different sets of GP terminals (all are rotationally invariant statistics of pixel data) is compared on 10 images. The discrete Fourier transform set is found to be marginally superior to the simpler statistics set that includes an edge detector. An analysis of detector formulae provides insight on vehicle detection principles. In addition, a promising family of algorithms that take advantage of the GP method’s ability to prescribe an advantageous solution architecture is developed as a post-processor. These algorithms selectively reduce false alarms by exploring context, and determine the amount of contextual information that is required for this task.
ISSN:0167-8655
1872-7344
DOI:10.1016/j.patrec.2005.07.025