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Proceedings Paper

Vision-based terrain learning
Author(s): Robert E. Karlsen; Gary Witus
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Paper Abstract

This paper presents an algorithm for online image-based terrain classification that mimics a human supervisor's segmentation and classification of training images into "Go" and "NoGo" regions. The algorithm identifies a set of image chips (or exemplars) in the training images that span the range of terrain appearance. It then uses the exemplars to segment novel images and assign a Go/NoGo classification. System parameters adapt to new inputs, providing a mechanism for learning. System performance is compared to that obtained via offline fuzzy c-means clustering and support vector machine classification.

Paper Details

Date Published: 9 May 2006
PDF: 10 pages
Proc. SPIE 6230, Unmanned Systems Technology VIII, 623005 (9 May 2006); doi: 10.1117/12.664427
Show Author Affiliations
Robert E. Karlsen, U.S. Army - TARDEC (United States)
Gary Witus, Turing Associates (United States)


Published in SPIE Proceedings Vol. 6230:
Unmanned Systems Technology VIII
Grant R. Gerhart; Charles M. Shoemaker; Douglas W. Gage, Editor(s)

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