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Author(s): Carpenter, G.A. | Martens, S. |
Year: 2005
Citation: Proceedings of the International Joint Conference on Neural Networks (IJCNN05), Montreal.
Abstract: Classifying terrain or objects may require the resolution of conflicting information from sensors working at different times, locations, and scales, and from users with different goals and situations. Current fusion methods can help resolve such inconsistencies, as when evidence variously suggests that an object is a car, a truck, or an airplane. The methods described here define a complementary approach to the information fusion problem, considering the case where sensors and sources are both nominally inconsistent and reliable, as when evidence suggests that an object is a car, a vehicle, and man-made. Underlying relationships among classes are assumed to be unknown to the automated system or the human user. The ARTMAP self-organizing rule discovery procedure is illustrated with an image example, but is not limited to the image domain.
Topics:
Image Analysis,
Machine Learning,
Applications:
Information Fusion,
Models:
ARTMAP,
