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The Linear Fuzzy Space: Theory and Applications

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Artificial Intelligence: Theory and Applications

Part of the book series: Studies in Computational Intelligence ((SCI,volume 973))

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Abstract

The basic properties of dynamic systems include imprecisions and uncertainties in both spatial and temporal data which require adequate representations and further manipulations. Existing literature covers various approaches that address the representations and manipulations of such data, with fuzzy theory being one of the most widely and most frequently used. The linear fuzzy space is an original fuzzy-based approach that provides an effective and compact model of imprecise data. In this chapter we present the main theoretical results of the linear fuzzy space and analyze its capacity to facilitate the modelling of spatial and spatio-temporal systems through representative examples of its applications in selected real-world problems. The chapter consists of five sections. The first section is an introduction which states the problem and briefly presents relevant research. The second section contains a brief, yet comprehensive, overview of linear fuzzy space theory. The third section presents an analysis of fuzzy linear space theory concerning the modelling of spatial and/or temporal systems. The fourth section presents two examples which show its applications in image analysis and time series modelling. The fifth section concludes the chapter and recommends some directions for future research.

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References

  1. Air Quality Index.: Wikipedia. https://en.wikipedia.org/wiki/Air_quality_index (2020)

  2. Andree, B.P.J.: Theory and Application of Dynamic Spatial Time Series Models. (Tinbergen Institute, Research Series; vol. 762). Rozenberg Publishers and the Tinbergen Institute, Amsterdam (2020)

    Google Scholar 

  3. Barro, S., Marín, R., Mira, J., Patón, A.R.: A model and a language for the fuzzy representation and handling of time. Fuzzy Sets Syst. 61(2), 153–175 (1994). ISSN 0165-0114. https://doi.org/10.1016/0165-0114(94)90231-3

  4. Buckley, J.J., Eslami, E.: Fuzzy plane geometry I: points and lines. Fuzzy Sets Syst. 86(2), 179–187 (1997)

    Article  MathSciNet  Google Scholar 

  5. Galambos, C., Kittler, J., Matas, J.: Using gradient information to enhance the progressive probabilistic Hough transform. In: Proceedings 15th International Conference on Pattern Recognition. ICPR-2000, Barcelona, Spain, pp. 560–563 (2000)

    Google Scholar 

  6. Galambos, C., Matas J., Kittler, J.: Progressive probabilistic Hough transform for line detection. In: IEEE Computer Society Conference Computer Vision and Pattern Recognition, p. 1 (1999)

    Google Scholar 

  7. Guibas, L., Salesin, D., Stolfi, J.: Epsilon geometry: building robust algorithms from imprecise computations. In: Proceedings of the 5th Annual Symposium on Computational Geometry, New York, NY, USA, pp. 208–217 (1989)

    Google Scholar 

  8. Hadžić, O., Pap, E.: Fixed Point Theory in Probabilistic Metric Spaces. Kluwer Academic Publishers, Dordecht (2001)

    Book  Google Scholar 

  9. Hudelot, C., Atif, J., Bloch, I.: Fuzzy spatial relation ontology for image interpretation. Fuzzy Sets Syst. 159(15), 1929–1951 (2008). ISSN 0165-0114. https://doi.org/10.1016/j.fss.2008.02.011

  10. Jocić M.: Digital image segmentation and feature extraction based on geometric shapes in linear fuzzy space. Master thesis, Faculty of Technical Sciences University of Novi Sad (in Serbian) (2012)

    Google Scholar 

  11. Jocić, M., Dimitrijević, D., Pantović, M., Madić, D., Konjović, Z.: Linear fuzzy space based scoliosis screening. In: Proceedings of the ICIST 2014, Kopaonik, Serbia, pp. 180–187 (2014)

    Google Scholar 

  12. Jocić, M., Obradović, Đ, Konjović, Z., Pap, E.: 2D fuzzy spatial relations and their applications to DICOM medical images. In: IEEE 11th International Symposium on Intelligent Systems and Informatics (SISY), pp. 39–44 (2013)

    Google Scholar 

  13. Jocić, M., Pap, E., Szakál, A., Obradović, Đ., Konjović, Z.: Managing big data using fuzzy sets by directed graph node similarity. Acta Polytech. Hung. 14(2), 183–200 (2017)

    Google Scholar 

  14. Kingma, D.P., Ba, J.: Adam. A Method for Stochastic Optimization. Computer Science, Mathematic (2015)

    Google Scholar 

  15. Kiryati, N., Eldar, Y., Bruckstein, A.M.: A probabilistic Hough transform. Pattern Recognit. 24(4), 303–316 (1991)

    Article  MathSciNet  Google Scholar 

  16. Löffler, M.: Data imprecision in computational geometry. Ph.D. thesis, Utrecht University, Utrecht (2009)

    Google Scholar 

  17. Löffler, M., Van Kreveld, M.: Geometry with imprecise lines. In: 24th European Workshop on Computational Geometry (2008)

    Google Scholar 

  18. Löffler, M., Van Kreveld, M.: Largest and smallest convex hulls for imprecise points. Algorithmica 56(2), 235–269 (2008)

    Article  MathSciNet  Google Scholar 

  19. Marín, R., Barro S., Bosch, A., Mira, J.: Modeling the representation of time from a fuzzy perspective. Cybern. Syst. 25(2), 217–231 (1994). https://doi.org/10.1080/01969729408902325

  20. Matas, J., Galambos, C., Kittler J.: Robust detection of lines using the progressive probabilistic hough transform. Comput. Vis. Image Understand. 78(1), 119–137 (2000)

    Google Scholar 

  21. Obradović, Đ.: Model of the computer simulation system for managing geospace under uncertainty conditions. Ph.D. dissertation, Faculty of Technical Sciences University of Novi Sad (in Serbian) (2011)

    Google Scholar 

  22. Obradović, Đ., Konjović, Z., Pap, E.: Extending PostGIS by imprecise point objects. In: IEEE 8th international symposium on intelligent systems and informatics, Subotica, Serbia, pp. 23–28 (2010)

    Google Scholar 

  23. Obradović, Đ., Konjović, Z., Pap, E., Jocić, M.: Linear fuzzy space polygon based image segmentation and feature extraction. In: Proceedings of the IEEE 10th Jubilee International Symposium on Intelligent Systems and Informatics (SISY), pp. 543–548 (2012)

    Google Scholar 

  24. Obradović, Đ., Konjovic, Z., Pap, E., Ralević, N.M.: The maximal distance between imprecise point objects. Fuzzy Sets Syst 170(1), 76–94 (2011)

    Google Scholar 

  25. Obradović, Đ., Konjović, Z., Pap, E., Rudas, I.J.: Linear Fuzzy Space Based Road Lane Model and Detection. Knowl.-Based Syst. 38, 37–47 (2012)

    Google Scholar 

  26. Obradović, Đ., Konjović, Z., Pap, E., Rudas, I.: Fuzzy geometry in linear fuzzy space, intelligent systems: models and applications.iN: Revised and Selected Papers from the 9th IEEE International Symposium on Intelligent Systems and Informatics SISY 2011. Springer, Berlin, Heidelberg, PP. 137–153 (2013)

    Google Scholar 

  27. Obradović, Đ., Konjović, Z., Segedinac, M.: Extensible software simulation system for imprecise geospatial process. In: Konjović, Z. (Eds.) ICIST 2011 Proceedings, pp. 71–77 (2011)

    Google Scholar 

  28. Panigrahi, S., Behera, H.S.: Fuzzy time series forecasting: a survey. In: Behera, H., Nayak, J., Naik B., Pelusi, D. (eds.) Computational Intelligence in Data Mining. Advances in Intelligent Systems and Computing, vol. 990. Springer, Singapore (2020). https://doi.org/10.1007/978-981-13-8676-3_54

  29. Rosenfeld, A.: The fuzzy geometry of image subsets. Pattern Recogn. Lett. 2(5), 311–317 (1984)

    Article  Google Scholar 

  30. Sahin, A., Dodurka, M.F., Kumbasar, T., Yesil, E., Siradag, S.: Review study on fuzzy time series and their applications in the last fifteen years. In: International Fuzzy Systems Symposium (FUZZYSS’15), Istanbul, pp. 166–171 (2015)

    Google Scholar 

  31. Sahin, A., Kumbasar, T., Yesil, E., Dodurka, M.F., Karasakal, O., Siradag, S.: An enhanced fuzzy linguistic term generation and representation for time series forecasting. In: IEEE International Conference on Fuzzy Systems (FUZZ-IEEE), Istanbul, pp. 1–8 (2015). https://doi.org/10.1109/FUZZ-IEEE.2015.7337904

  32. Samet, H., Tamminen, M.: Efficient component labeling of images of arbitrary dimension represented by linear bintrees. In: IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 10, issue 4, p. 579 (1988). https://doi.org/10.1109/34.3918

  33. Schneider, M.: Spatial Data Types for Database Systems: Finite Resolution Geometry for Geographic Information Systems, 1st edn. Springer, Berlin (1997)

    Book  Google Scholar 

  34. Schneider, M.: Fuzzy topological predicates, their properties, and their integration into query languages. In: Proceedings of the 9th ACM International Symposium on Advances in Geographic Information Systems, pp. 9–14 (2001)

    Google Scholar 

  35. Schneider, M.: Design and implementation of finite resolution crisp and fuzzy spatial objects. Data Knowl. Eng. 44, 81–108 (2003)

    Article  Google Scholar 

  36. Schockaert, S., Cock, M.D.: Temporal reasoning about fuzzy intervals. Artif. Intell. 172, 1158–1193 (2008)

    Article  MathSciNet  Google Scholar 

  37. Singh, P.: A brief review of modelling approaches based on fuzzy time series. Int. J. Mach. Learn. Cyber. 8, 397–420 (2017)

    Article  Google Scholar 

  38. Song, Q., Chissom, B.S.: Fuzzy time series and its model. Fuzzy Sets Syst. 54(3), 269–277 (1993). https://doi.org/10.1016/0165-0114(93)90372-O

  39. Stephens, R.S.: Probabilistic approach to the Hough transform. Image Vis. Comput. 9(1), 66–71 (1991)

    Article  Google Scholar 

  40. Trajdos, P., Kurzynski, M.: A dynamic model of classifier competence based on the local fuzzy confusion matrix and the random reference classifier. Int. J. Appl. Math. Comput. Sci. 26(1), 175–189 (2016). https://doi.org/10.1515/amcs-2016-0012

  41. Verstraete, J., Tré, G., Caluwe, R., Hallez, A.: Field based methods for the modeling of fuzzy spatial data. In: Fuzzy Modeling with Spatial Information for Geographic Problems, pp. 41–69 (2005)

    Google Scholar 

  42. Wei, W.W.S.: Multivariate time series analysis and applications. Wiley Series in Probability and Statistic. Wiley, Hoboken (2019). ISBN:9781119502951. https://doi.org/10.1002/9781119502951

  43. Yang, K., Sam, Ge S., He, H.: Robust line detection using two-orthogonal direction image scanning. Comput. Vis. Image Understand. 115(8), 1207–1222 (2011)

    Google Scholar 

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Acknowledgements

The authors acknowledge funding provided by the Science Fund of the Republic of Serbia #GRANT No. 6524105, AI—ATLAS.

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Correspondence to Đorđe Obradović .

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Obradović, Đ., Konjović, Z., Pap, E., Šoštarić, A. (2021). The Linear Fuzzy Space: Theory and Applications. In: Pap, E. (eds) Artificial Intelligence: Theory and Applications. Studies in Computational Intelligence, vol 973. Springer, Cham. https://doi.org/10.1007/978-3-030-72711-6_13

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