9 May 2006 Vision-based terrain learning
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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.
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Robert E. Karlsen, Gary Witus, "Vision-based terrain learning", Proc. SPIE 6230, Unmanned Systems Technology VIII, 623005 (9 May 2006); doi: 10.1117/12.664427; https://doi.org/10.1117/12.664427
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