Towards general measures of comparison of objects
Fuzzy Sets and Systems - Special issue dedicated to the memory of Professor Arnold Kaufmann
Knowledge-based image understanding systems: a survey
Computer Vision and Image Understanding
Fuzzy Relative Position Between Objects in Image Processing: A Morphological Approach
IEEE Transactions on Pattern Analysis and Machine Intelligence
Content-Based Image Retrieval at the End of the Early Years
IEEE Transactions on Pattern Analysis and Machine Intelligence
A reference ontology for biomedical informatics: the foundational model of anatomy
Journal of Biomedical Informatics - Special issue: Unified medical language system
Fuzzy spatial relationships for image processing and interpretation: a review
Image and Vision Computing
Adaptive pyramid and semantic graph: knowledge driven segmentation
GbRPR'05 Proceedings of the 5th IAPR international conference on Graph-Based Representations in Pattern Recognition
3D brain tumor segmentation using fuzzy classification and deformable models
WILF'05 Proceedings of the 6th international conference on Fuzzy Logic and Applications
Fuzzy spatial relation ontology for image interpretation
Fuzzy Sets and Systems
Fuzzy and Bipolar Mathematical Morphology, Applications in Spatial Reasoning
ECSQARU '09 Proceedings of the 10th European Conference on Symbolic and Quantitative Approaches to Reasoning with Uncertainty
IPMI'07 Proceedings of the 20th international conference on Information processing in medical imaging
Computer Vision and Image Understanding
Fuzzy spatial constraints and ranked partitioned sampling approach for multiple object tracking
Computer Vision and Image Understanding
A constraint propagation approach to structural model based image segmentation and recognition
Information Sciences: an International Journal
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In several domains of spatial reasoning, such as medical image interpretation, spatial relations between structures play a crucial role since they are less prone to variability than intrinsic properties of structures. Moreover, they constitute an important part of available knowledge. We show in this paper how this knowledge can be appropriately represented by graphs and fuzzy models of spatial relations, which are integrated in a reasoning process to guide the recognition of individual structures in images. However pathological cases may deviate substantially from generic knowledge. We propose a method to adapt the knowledge representation to take into account the influence of the pathologies on the spatial organization of a set of structures, based on learning procedures. We also propose to adapt the reasoning process, using graph based propagation and updating.