Abstract
The paper addresses an important and difficult problem of object recognition in poorly constrained environments and with objects having large variability. This research uses genetic programming (GP) to develop automatic object detectors. The task is to detect vehicles in infrared line scan (IRLS) images gathered by low flying aircraft. This is a difficult task due to the diversity of vehicles and the environments in which they can occur, and because images vary with numerous factors including fly-over, temporal and weather characteristics. A novel multi-stage approach is presented which addresses automatic feature detection, automatic object segregation, rotation invariance and generalisation across diverse objects whilst discriminating from a myriad of potential non-objects. The approach does not require imagery to be pre-processed.
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© 1999 Springer-Verlag Berlin Heidelberg
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Roberts, S.C., Howard, D. (1999). Evolution of Vehicle Detectors for Infrared Line Scan Imagery. In: Poli, R., Voigt, HM., Cagnoni, S., Corne, D., Smith, G.D., Fogarty, T.C. (eds) Evolutionary Image Analysis, Signal Processing and Telecommunications. EvoWorkshops 1999. Lecture Notes in Computer Science, vol 1596. Springer, Berlin, Heidelberg. https://doi.org/10.1007/10704703_9
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DOI: https://doi.org/10.1007/10704703_9
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-65837-5
Online ISBN: 978-3-540-48917-7
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