Abstract
Spatial relations play important role in computer vision, scene analysis, geographic information systems (GIS) and content based image retrieval. Analyzing spatial relations by Force histogram was introduced by Miyajima et al [1] and largely developed by Matsakis [2] who used a quantitative representation of relative position between 2D objects. Fuzzy Allen relations are used to define the fuzzy topological relations between different objects and to detect object positions in images. Concept for combined extraction of topological and directional relations by using histogram was developed by J.Malki and E.Zahzah [3], and further improved by Matsakis [4]. This algorithm has high computational and temporal complexity due to its limitations of object approximations. In this paper fuzzy aggregation operators are used for information integration along with polygonal approximation of objects. This approach gives anew, with low temporal and computational complexity of algorithm for the extraction of topological and directional relations.
Chapter PDF
Similar content being viewed by others
Keywords
References
Miyajima, K., Ralescu, A.: Spatial Organization in 2D Images, Fuzzy Systems. In: IEEE World Congress on Computational Intelligence, vol. 1, pp. 100–105 (1994)
Matsakis, P., Laurent Wendling, J.D.: Représentation de La Position Relative d’ Objets 2D au Moyen D′ Un Histogramme de Forces. Traitement du Signal 15, 25–38 (1998)
Malki, J., Zahzah, E., Nikitenko, D.: Indexation et Recherche d’ Image Fondées Sur Les Relations Spatiales Entre Objets. Traitement du Signal 19(4), 235–250 (2002)
Matsakis, P.: Combined Extraction of Directional and Topological Relationship Information from 2D Concave Objects, in fuzzy modeling with spatial informations for geographic problems, New York, pp. 15–40 (2005)
Egenhofer, M.J., Franzosa, R.D.: Point Set Topological Relations. International Journal of Geographical Information Systems 5(2), 161–174 (1991)
Egenhofer, M.J., Sharma, J., Mark, D.M.: A Critical Comparison of The 4-Intersection and 9-Intersection Models for Spatial Relations: Formal Analysis. Auto-Carto 11, 1–12 (1993)
Li, M.D.: A Statistical Model for Directional Relations Between Spatial Objects. GeoInformatica 12(2), 193–217 (2008)
Wang, Y., Makedon, F.: R-histogram:quantitative representation of spatial relations for similarity-based image retrieval. In: MULTIMEDIA 2003, pp. 323–326. ACM, New York (2003)
Pascal Matsakis, D.N.: Applying Soft Computing in Defining Spatial Relations, Understanding the Spatial Organization of Image Regions by Means of Force Histograms A Guided Tour, pp. 99–122. Springer, New York (2002)
Allen, J.F.: Maintaining Knowledge about Temporal Intervals. Communications of the ACM 26(11), 832–843 (1983)
Chi, K.-H., No-Wook Park, C.J.C.: Fuzzy Logic Intergration for Landslide Hazard Mapping using Spatial Data from Boeun, Korea. In: Symposium on geospatial theory, processing and application, ottawa
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2009 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Salamat, N., Zahzah, Eh. (2009). Spatial Relations Analysis by Using Fuzzy Operators. In: Allen, G., Nabrzyski, J., Seidel, E., van Albada, G.D., Dongarra, J., Sloot, P.M.A. (eds) Computational Science – ICCS 2009. ICCS 2009. Lecture Notes in Computer Science, vol 5545. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-01973-9_44
Download citation
DOI: https://doi.org/10.1007/978-3-642-01973-9_44
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-01972-2
Online ISBN: 978-3-642-01973-9
eBook Packages: Computer ScienceComputer Science (R0)