%0 Journal Article %1 Hudelot2008 %A Hudelot, C�line %A Atif, Jamal %A Bloch, Isabelle %B From Knowledge Representation to Information Processing and Management - Selected papers from the French Fuzzy Days (LFA 2006) %D 2008 %J Fuzzy Sets and Systems %K Fuzzy Image Ontology, Semantic Spatial gap interpretation, relations, representations, %N 15 %P 1929--1951 %T Fuzzy spatial relation ontology for image interpretation %U http://www.sciencedirect.com/science/article/B6V05-4RY8SHJ-1/1/6c07101bbbf14668c490f8f8cb5d6424 %V 159 %X The semantic interpretation of images can benefit from representations of useful concepts and the links between them as ontologies. In this paper, we propose an ontology of spatial relations, in order to guide image interpretation and the recognition of the structures it contains using structural information on the spatial arrangement of these structures. As an original theoretical contribution, this ontology is then enriched by fuzzy representations of concepts, which define their semantics, and allow establishing the link between these concepts (which are often expressed in linguistic terms) and the information that can be extracted from images. This contributes to reducing the semantic gap and it constitutes a new methodological approach to guide semantic image interpretation. This methodological approach is illustrated on a medical example, dealing with knowledge-based recognition of brain structures in 3D magnetic resonance images using the proposed fuzzy spatial relation ontology.

@article{Hudelot2008, abstract = {The semantic interpretation of images can benefit from representations of useful concepts and the links between them as ontologies. In this paper, we propose an ontology of spatial relations, in order to guide image interpretation and the recognition of the structures it contains using structural information on the spatial arrangement of these structures. As an original theoretical contribution, this ontology is then enriched by fuzzy representations of concepts, which define their semantics, and allow establishing the link between these concepts (which are often expressed in linguistic terms) and the information that can be extracted from images. This contributes to reducing the semantic gap and it constitutes a new methodological approach to guide semantic image interpretation. This methodological approach is illustrated on a medical example, dealing with knowledge-based recognition of brain structures in 3D magnetic resonance images using the proposed fuzzy spatial relation ontology.}, added-at = {2009-09-12T19:19:34.000+0200}, author = {Hudelot, C�line and Atif, Jamal and Bloch, Isabelle}, biburl = {https://www.bibsonomy.org/bibtex/294cf768ca0a48d1a7ec09cd789ff219b/mozaher}, booktitle = {From Knowledge Representation to Information Processing and Management - Selected papers from the French Fuzzy Days (LFA 2006)}, file = {:Hudelot2008.pdf:PDF}, interhash = {b0dd5c450c7e468bf0bdf9645c12b487}, intrahash = {94cf768ca0a48d1a7ec09cd789ff219b}, journal = {Fuzzy Sets and Systems}, keywords = {Fuzzy Image Ontology, Semantic Spatial gap interpretation, relations, representations,}, month = {August}, number = 15, owner = {Mozaher}, pages = {1929--1951}, timestamp = {2009-09-12T19:19:39.000+0200}, title = {Fuzzy spatial relation ontology for image interpretation}, url = {http://www.sciencedirect.com/science/article/B6V05-4RY8SHJ-1/1/6c07101bbbf14668c490f8f8cb5d6424}, volume = 159, year = 2008 }