Abstract
This paper presents a methodology for object recognition in complex scenes by learning multiple feature object representations in second generation Forward Looking InfraRed (FLIR) images. A hierarchical recognition framework is developed which solves the recognition task by performing classification using decisions at the lower levels and the input features. The system uses new algorithms for detection and segmentation of objects and a Bayesian formulation for combining multiple object features for improved discrimination. Experimental results on a large database of FLIR images is presented to validate the robustness of the system, and its applicability to FLIR imagery obtained from real scenes.
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© 1999 Springer-Verlag Berlin Heidelberg
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Shah, S., Aggarwal, J.K. (1999). Hierarchical Multifeature Integration for Automatic Object Recognition in Forward Looking Infrared Images. In: Imam, I., Kodratoff, Y., El-Dessouki, A., Ali, M. (eds) Multiple Approaches to Intelligent Systems. IEA/AIE 1999. Lecture Notes in Computer Science(), vol 1611. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-48765-4_63
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DOI: https://doi.org/10.1007/978-3-540-48765-4_63
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-66076-7
Online ISBN: 978-3-540-48765-4
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