Jimi Shanahan, James Baldwin, Barry Thomas, Trevor Martin, Neill Campbell, Majid Mirmehdi
The proceedings of the Intn'l conference of the North American Fuzzy Information Processing Society, NAFIPS 1999, New York, pp 710-714
Here we propose an approach to object recognition that facilitates the transition from recognition to
understanding. The proposed approach begins by segmenting the images into regions using standard image
processing approaches, which are subsequently classified using a discovered fuzzy Cartesian granule feature
classifier. Understanding is made possible through the transparent and succinct nature of the discovered
models. The recognition of roads in images is taken as an illustrative problem in the vision domain. The
discovered fuzzy models while providing high levels of accuracy (97%), also provide understanding of the
problem domain through the transparency of the learnt models. The learning step in the proposed approach is
compared with other techniques such as decision trees, naïve Bayes and neural networks.
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