%0 Journal Article %1 Bloch-05-89 %A Bloch, I. %D 2005 %J Image and Vision Computing %K recognition %N 2 %P 89-110 %R 10.1016/j.imavis.2004.06.013 %T Fuzzy spatial relationships for image processing and interpretation: A review %U http://www.scopus.com/inward/record.url?eid=2-s2.0-10044249956&partnerID=40&md5=55643ab60430134698fff2bbedf7956f %V 23 %X In spatial reasoning, relationships between spatial entities play a major role. In image interpretation, computer vision and structural recognition, the management of imperfect information and of imprecision constitutes a key point. This calls for the framework of fuzzy sets, which exhibits nice features to represent spatial imprecision at different levels, imprecision in knowledge and knowledge representation, and which provides powerful tools for fusion, decision-making and reasoning. In this paper, we review the main fuzzy approaches for defining spatial relationships including topological (set relationships, adjacency) and metrical relations (distances, directional relative position). © 2004 Elsevier B.V. All rights reserved.

@article{Bloch-05-89, abbrev_source_title = {Image Vision Comput}, abstract = {In spatial reasoning, relationships between spatial entities play a major role. In image interpretation, computer vision and structural recognition, the management of imperfect information and of imprecision constitutes a key point. This calls for the framework of fuzzy sets, which exhibits nice features to represent spatial imprecision at different levels, imprecision in knowledge and knowledge representation, and which provides powerful tools for fusion, decision-making and reasoning. In this paper, we review the main fuzzy approaches for defining spatial relationships including topological (set relationships, adjacency) and metrical relations (distances, directional relative position). © 2004 Elsevier B.V. All rights reserved.}, added-at = {2012-06-02T22:47:28.000+0200}, affiliation = {Ecl. Natl. Sup. des Telecom., Département TSI, CNRS VMR 5141 LTCI, 46 rue Barrault, 75013 Paris, France}, author = {Bloch, I.}, author_keywords = {Degree of adjacency; Degree of inclusion; Degree of intersection; Directional relative position; Distances; Fuzzy spatial relationships; Image interpretation; Spatial reasoning; Structural pattern recognition}, avail = {y}, biburl = {https://www.bibsonomy.org/bibtex/2500061a64e9bc3764a4fba0c0dd746ea/diemsi}, coden = {IVCOD}, correspondence_address = {Bloch, I.; Ecl. Natl. Sup. des Telecom., Département TSI, CNRS VMR 5141 LTCI, 46 rue Barrault, 75013 Paris, France; email: isabelle.bloch@enst.fr}, document_type = {Conference Paper}, doi = {10.1016/j.imavis.2004.06.013}, file = {:!literatura\\fuzzy-spatial\\Bloch-Fuzzy spatial relationships for image processing and interpretation-a review.pdf:PDF}, interhash = {5fa96dc0af779090d4c1398e87fd57d5}, intrahash = {500061a64e9bc3764a4fba0c0dd746ea}, issn = {02628856}, journal = {Image and Vision Computing}, keywords = {recognition}, language = {English}, note = {cited By (since 1996) 36}, number = 2, pages = {89-110}, printed = {y/r}, references = {Bloch, I., Fuzzy spatial relationships from mathematical morphology for model-based pattern recognition and spatial reasoning (2003) Discrete Geometry for Computer Imagery DGCI 2003, LNCS 2886, pp. 16-33. , Naples, Italy; Freeman, J., The modelling of spatial relations (1975) Computer Graphics and Image Processing, 4 (2), pp. 156-171; Kuipers, B., Modeling spatial knowledge (1978) Cognitive Science, 2, pp. 129-153; Kuipers, B.J., Levitt, T.S., Navigation and mapping in large-scale space (1988) AI Magazine, 9 (2), pp. 25-43; Dubois, D., Prade, H., (1980) Fuzzy Sets and Systems: Theory and Applications, , Academic Press, New York; Serra, J., (1982) Image Analysis and Mathematical Morphology, , Academic Press, London; Bloch, I., Maître, H., Fuzzy mathematical morphologies: A comparative study (1995) Pattern Recognition, 28 (9), pp. 1341-1387; Zadeh, L.A., The concept of a linguistic variable and its application to approximate reasoning (1975) Information Sciences, 8, pp. 199-249; Zadeh, L., Fuzzy sets and information granularity (1979) Advances in Fuzzy Set Theory and Applications, pp. 3-18. , M. 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