A Two Layer Case-Based Reasoning Architecture for Medical Image Understanding
EWCBR '96 Proceedings of the Third European Workshop on Advances in Case-Based Reasoning
Different Learning Strategies in a Case-Based Reasoning System for Image Interpretation
EWCBR '98 Proceedings of the 4th European Workshop on Advances in Case-Based Reasoning
CBR-Based Ultra Sonic Image Interpretation
EWCBR '00 Proceedings of the 5th European Workshop on Advances in Case-Based Reasoning
An Architecture for a CBR Image Segmentation System
ICCBR '99 Proceedings of the Third International Conference on Case-Based Reasoning and Development
CBR for the Reuse of Image Processing Knowledge: A Recursive Retrieval/ Adaptation
ICCBR '99 Proceedings of the Third International Conference on Case-Based Reasoning and Development
Case Based Reasoning for Image Interpretation
CAIP '95 Proceedings of the 6th International Conference on Computer Analysis of Images and Patterns
How Dissimilar Are Two Grey-Scale Images?
Mustererkennung 1995, 17. DAGM-Symposium
CBR-Based Ultra Sonic Image Interpretation
EWCBR '00 Proceedings of the 5th European Workshop on Advances in Case-Based Reasoning
Digital Image Similarity for Geo-spatial Knowledge Management
ECCBR '02 Proceedings of the 6th European Conference on Advances in Case-Based Reasoning
Watershed Segmentation Via Case-Based Reasoning
ICCBR '07 Proceedings of the 7th international conference on Case-Based Reasoning: Case-Based Reasoning Research and Development
A comparative study of catalogue-based classification
ECCBR'06 Proceedings of the 8th European conference on Advances in Case-Based Reasoning
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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.