Fusion of multiple-sensor imagery based on target motion characteristics

Thomas R. Tsao; John M. Libert

doi:10.1117/12.44838

Abstract

Fusion of multiple sensor imagery is an effective approach to clutter rejection in target detection and recognition. However, image registration at the pixel level and even at the feature level poses significant problems. We are developing a neural network computational schemes that will permit fusion of multiple sensor information according to target motion characteristics. One such scheme implements the Law of Common Fate to differentiate moving targets from dynamic background clutter on the basis of homogeneous velocity; spatiotemporal frequency analysis is applied to time-varying sensor imagery to detect and locate individual moving objects. Another computational scheme applies Gabor filters and differential Gabor filters to calculate image flow and then employs a Lie group-based neural network to interpret the 2D image flow in terms of 3D motion, and to delineate regions of homogeneous 3D motion; the motion-keyed regions may be correlated among sensor types to associate multiattribute information with the individual targets in the scene and to exclude clutter.

Citation Download Citation

Thomas R. Tsao, John M. Libert, "Fusion of multiple-sensor imagery based on target motion characteristics", Proc. SPIE 1470, Data Structures and Target Classification, (1 August 1991); doi: 10.1117/12.44838; https://doi.org/10.1117/12.44838

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