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Tytuł artykułu

The use of fuzzy logic for description of spatial relations between objects

Treść / Zawartość
Identyfikatory
Warianty tytułu
PL
Logika rozmyta w opisie relacji przestrzennych obiektów
Języki publikacji
PL
Abstrakty
PL
Celem artykułu jest przedstawienie zestawienie przeglądu najważniejszych metod określania relacji przestrzennych między zbiorami ostrymi i rozmytymi w obrazach 2D. Rozpatrywane typy relacji to punkt - punkt, punkt - obiekt, obiekt ostry - obiekt ostry oraz obiekt rozmyty - obiekt rozmyty. Artykuł porusza również kierunek przyszłych badań nad wykorzystaniem rozmytych kierunkowych relacji przestrzennych do modelowania procesów dynamicznych na bazie tomogramów i termogramów.
EN
The goal of this article is to present a review of major methods and algorithms for assessing directional spatial relations between fuzzy and crisp objects in 2D image. The types of relations mentioned are: point - point, point - object, crisp object - crisp object, fuzzy object - fuzzy object. Presented article also takes into account further research on use of fuzzy directional spatial relations for dynamical process modeling on base of tomograms and thermograms.
Wydawca
Rocznik
Strony
563--580
Opis fizyczny
Bibliogr. 40 poz., rys., wykr., tab.
Twórcy
autor
  • Computer Engineering Department, Technical University of Lodz, Poland
autor
  • Computer Engineering Department, Technical University of Lodz, Poland
Bibliografia
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  • [8] Bloch I., Fuzzy spatial relationships for image processing and interpretation: A review. Image and Vision Computing, 23(2), 2005, 89-110.
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  • [11] Bloch I., Ralescu A., Directional relativeposition between objects in image processing: A comparison between fuzzy approaches. Pattern Recognition, 36(7), 2003, 1563-1582.
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  • [14] Egenhofer M., Franzosa R., Point-set topological spatial relations. International Journal of Geographical Information Systems, 5(2), 1991, 161-174.
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  • [16] Frączyk A., Urbanek P., Kucharski J., Algorytm sterowania ruchem wzbudnika i mocą zapewniający równomierny rozkład temperatury nagrzewanego indukcyjnie obracającego się walca stalowego. Automatyka (półrocznik AGH), 13/3, 2009, 1075-1083.
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  • [18] Gader P. D., Fuzzy spatial relations based on fuzzy morphology, vol. 2. IEEE, Piscataway, NJ, Barcelona, Spain, 1997, 1179-1183.
  • [19] Kawade M., Object recognition system in a dynamie environment. In Fuzzy Systems, 1995. International Joint Conference of the Fourth IEEE International Conference on Fuzzy Systems and The Second International Fuzzy Engineering Symposium, Proc. of 1995 IEEE International Conference, vol. 3, 20-24 1995, 1285 -1290.
  • [20] Koczy L., On the description of relative position of fuzzy patterns. Pattern Recognition Letters, 8(1), 1988, 21-28.
  • [21] Keller J., Sztandera L., Spatial relations among fuzzy subsets of an image. In Uncertainty Modeling and Analysis, Proc. First International Symposium on, 3-5 1990, 207-211.
  • [22] Keller J.M., Wang X., Comparison of spatial relation definitions in computer vision. IEEE, College Park, MD, 1995, 679-684.
  • [23] Krishnapuram R., Keller J.M., Ma Y., Quantitative analysis of properties and spatial relations of fuzzy image regions. IEEE Transactions on Fuzzy Systems, 1(3), 1993, 222-233.
  • [24] Loiseau Y, Prade H., Boughanem M., Qualitative pattern matching with linguistic terms. AI Commun., 17(1), 2004, 25-34.
  • [25] Lomenie N., Racoceanu D., Spatial relationships over sparse representations. Wellington, 2009, 226-230.
  • [26] Matsakis P., Wendling L., A new way to represent the relative position between areał objects. IEEE Transactions on Pattern Analysis and Machinę Intelligence, 21(7), 1999, 634-643.
  • [27] Miyajima K., Ralescu A., Modeling of natura! objects including fuzziness and application to image understanding. In Fuzzy Systems, Second IEEE International Conference, vol. 2, 1993, 1049-1054.
  • [28] Miyajima K., Ralescu A., Spatial organization in 2d images, vol. 1. IEEE, Piscataway, NJ, Orlando, FL, 1994, 100-105.
  • [29] Miyajima K., Ralescu A., Spatial organization in 2d segmented images: Representation and recognition of primitive spatial relations. Fuzzy Sets and Systems, 65(2-3), 1994, 225-236.
  • [30] Nakagawa Y., Hirota K., Fundamentals of fuzzy knowledge base for image understanding. In Fuzzy Systems, International Joint Conference of the Fourth IEEE International Conference on Fuzzy Systems and The Second International Fuzzy Engineering Symposium., Proc. of IEEE International Conference, vol. 3, 20-24 1995, 1137 -1142.
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  • [37] Wang Y., Makedon F., R-histogram: Quantitative representation of spatial relations for similarity-based image retrieval. Berkeley, CA., 2003, 323-326.
  • [38] Wang Y, Makedon F., Ford J., Shen L., Goldin D., Generating fuzzy semantic metadata describing spatial relations from images using the r-histogram. Tucson, AZ, 2004, 202—211.
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Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-article-AGH1-0025-0088
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