CBR-Based Ultra Sonic Image Interpretation

Petra Perner

Institute of Computer Vision and Applied Computer Sciences
Arno-Nitzsche-Str. 45, 04277 Leipzig, Germany
Email: ibaiperner@aol.com http://members.aol.com/ibaiperner



Abstract. The existing image interpretation systems lack robustness and
accuracy. They cannot adapt to changing environmental conditions or to new
objects. The application of machine learning to image interpretation is the next
logical step. Our proposed approach aims at the development of dedicated
machine learning techniques at all levels of image interpretation in a systematic
fashion. In this paper we propose a system which uses Case-Based Reasoning
(CBR) to optimize image segmentation at the low level according to changing
image acquisition conditions and image quality. The intermediate-level unit
extracts the case representation used by the high-level unit for further
processing. At the high level, CBR is employed to dynamically adapt image
interpretation.
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