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A new approach to detection of changes in multidimensional patterns. Part 2

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Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
In the paper we develop an algorithm based on the Parzen kernel estimate for detection of sudden changes in 3-dimensional shapes which happen along the edge curves. Such problems commonly arise in various areas of computer vision, e.g., in edge detection, bioinformatics and processing of satellite imagery. In many engineering problems abrupt change detection may help in fault protection e.g. the jump detection in functions describing the static and dynamic properties of the objects in mechanical systems. We developed an algorithm for detecting abrupt changes which is nonparametric in nature and utilizes Parzen regression estimates of multivariate functions and their derivatives. In tests we apply this method, particularly but not exclusively, to the functions of two variables.
Rocznik
Strony
217--227
Opis fizyczny
Bibliogr. 53 poz., rys.
Twórcy
  • Institute of Computational Intelligence Czestochowa University of Technology Czestochowa, al. Armii Krajowej 36, PL-42-200 Cze¸stochowa, Poland
  • Department of Computer Science and Software Engineering Concordia University, Montreal, Quebec, Canada H3G 1M8
  • Department of Electrical Engineering, Westpomeranian University of Technology, 70-310 Szczecin, Poland
  • Management Department University of Social Sciences, 90-113 Łódź
  • Information Technology Institute University of Social Sciences, 90-113 Łódź
  • Clark University Worcester, MA 01610, USA
autor
  • Nanyang Technological University School of Electrical and Electronic Engineering, Singapore
Bibliografia
  • [1] S. Alpert, M. Galun, B. Nadler, R. Basri, Detecting faint curved edges in noisy images, Daniilidis K., Maragos P., Paragios N. (eds) Computer Vision ECCV 2010, Lecture Notes in Computer Science, vol 6314. Springer, Berlin, Heidelberg, 2010, pp. 750-763.
  • [2] D. Bazazian, J.R. Casas, J. Ruiz-Hidalgo, Fast and robust edge extraction in unorganized point clouds, No. 11, 2015, pp 1-8.
  • [3] A. Berlinet, G. Biau, L. Rouviere, Optimal L1 bandwidth selection for variable kernel density estimates, Statistics and Probability Letters, Elsevier, Vol. 74, No. 2, 2005, pp. 116-128.
  • [4] S. Bhardwaj, A. Mittal, A survey on various edge detector techniques, Elseiver, SciVerse ScienceDirect, Procedia Technology 4, 2nd International Conference on Computer, Communication, Control and Information Technology, 2012, pp. 220-226.
  • [5] A. Borkowski, Surface breaklines modeling on the basis of laser scanning data, Archiwum Fotogrametrii, Kartografii i Teledetekcji, Vol. 17a, 2007, pp. 73-82.
  • [6] J.F. Canny, A computational approach to edge detection, IEEE Trans. Pattern Analysis and Machine Intelligence, Vol. 8, No. 6, 1986, pp. 679-698.
  • [7] G.W. Corder, D.I. Foreman, Nonparametric Statistics: A Step-by-Step Approach. Wiley, New York, 2014.
  • [8] K. Cpałka, L. Rutkowski, Evolutionary learning of flexible neuro-fuzzy systems, Proc. of the 2008 IEEE Int. Conference on Fuzzy Systems (IEEE World Congress on Computational Intelligence, WCCI 2008), Hong Kong June 1-6, CD, 2008, pp. 969-975.
  • [9] T. Dasu, S. Krishnan, S. Venkatasubramanian, K. Yi, An information-theoretic approach to detecting changes in multi-dimensional data streams, Proc. Symp. on the Interface of Statistics, Computing Science, and Applications, 2006.
  • [10] L. Devroye, G. Lugosi, Combinatorial Methods in Density Estimation. Springer-Verlag, New York, 2001.
  • [11] J.R Dim, T. Takamura, Alternative approach for satellite cloud classification: edge gradient application, Advances in Meteorology, 2013, pp. 1-8.
  • [12] P. Duda, M. Jaworski, L. Rutkowski, Convergent time-varying regression models for data streams: tracking concept drift by the recursive Parzen-based generalized regression neural networks, International Journal of Neural Systems, Vol. 28, No. 2, 1750048, 2018.
  • [13] P. Duda, M. Jaworski, L. Rutkowski, Knowledge discovery in data streams with the orthogonal series-based generalized regression neural networks, Information Sciences, Vol. 460-461, 2018, pp. 497-518.
  • [14] P. Duda, L. Rutkowski, M. Jaworski, D. Rutkowska, On the Parzen kernel-based probability density function learning procedures over time-varying streaming data with applications to pattern classification, IEEE Transactions on Cybernetics, 2018, pp. 1-14.
  • [15] R.L. Eubank, Nonparametric Regression and Spline Smoothing. 2nd edition, Marcel Dekker, New York, 1999.
  • [16] W.J. Faithfull, J.J. Rodríguez, L.I. Kuncheva, Combining univariate approaches for ensemble change detection in multivariate data, Elseiver, Information Fusion, Vol. 45, 2019, pp. 202-214.
  • [17] T. Gałkowski, L. Rutkowski, Nonparametric recovery of multivariate functions with applications to system identification, Proceedings of the IEEE, Vol. 73, 1985, pp. 942-943.
  • [18] T. Gałkowski, L. Rutkowski, Nonparametric fitting of multivariable functions, IEEE Transactions on Automatic Control, Vol. AC-31, 1986, pp. 785-787.
  • [19] T. Gałkowski, On nonparametric fitting of higher order functions derivatives by the kernel method - a simulation study, Proceedings of the 5-th Int. Symp. on Applied Stochastic Models and data Analysis, Granada, Spain, 1991, pp. 230-242.
  • [20] T. Gałkowski, A. Krzyżak and Z. Filutowicz, A new approach to detection of changes in multidimensional patterns, Journal of Artificial Intelligence and Soft Computing Research, Vol. 10, Issue 2, 2020, pp. 125-136.
  • [21] T. Gasser, H.-G. Müller, Kernel estimation of regression functions, Lecture Notes in Mathematics, Vol. 757. Springer-Verlag, Heidelberg, 1979, pp. 23-68.
  • [22] T. Gasser, H.-G. Müller, Estimating regression functions and their derivatives by the kernel method, Scandinavian Journal of Statistics, Vol. 11, No. 3, 1984, pp. 171-185.
  • [23] R.C. Gonzales, R.E. Woods, Digital Image Processing, 4th Edition, Pearson, 2018.
  • [24] A. Gramacki, J. Gramacki, FFT-based fast bandwidth selector for multivariate kernel density estimation. Computational Statistics & Data Analysis, Elsevier, Vol. 106, 2017, pp. 27-45.
  • [25] R. Grycuk, R. Scherer, M. Gabryel, New image descriptor from edge detector and blob extractor. Journal of Applied Mathematics and Computational Mechanics, Vol. 14, No.4, 2015, pp. 31-39.
  • [26] R. Grycuk, M. Knop, S. Mandal, Video key frame detection based on SURF algorithm. International Conference on Artificial Intelligence and Soft Computing, ICAISC’2015, Springer, Cham, 2015, pp. 566-576.
  • [27] R. Grycuk, M. Gabryel, M. Scherer, S. Voloshynovskiy, Image descriptor based on edge detection and crawler algorithm. In International Conference on Artificial Intelligence and Soft Computing, ICAISC’2016, Springer, 2016, pp. 647-659.
  • [28] L. Györfi, M. Kohler, A. Krzyżak, H. Walk, A Distribution-Free Theory of Nonparametric Regression. Springer, 2002.
  • [29] I. Horev, B. Nadler, E. Arias-Castro, M. Galun, R. Basri, Detection of long edges on a computational budget: A sublinear approach, SIAM Journal Imaging Sciences, Vol. 8, No. 1, 2015, pp. 458-483.
  • [30] M. Jaworski, P. Duda, L. Rutkowski, New splitting criteria for decision trees in stationary data streams, IEEE Transactions on Neural Networks and Learning Systems, Vol. 29, No. 6, 2018, pp. 2516-2529.
  • [31] Z. Jin, T. Tillo, W. Zou, X. Li, E.G. Lim, Depth image-based plane detection, Big Data Analytics, Vol. 3, No. 10, 2018, pp. n/a.
  • [32] M. Kolomenkin, I. Shimshoni, A. Tal, On edge detection on surfaces, 2009 IEEE Conference on Computer Vision and Pattern Recognition, 2009, pp. 2767-2774.
  • [33] S. Kullback, R.A. Leibler, On information and sufficiency, The Annals of Mathematical Statistics. Vol. 22, No. 1, 1951, pp. 79-86.
  • [34] S.A. Ludwig, Applying a neural network ensemble to intrusion detection, Journal of Artificial Intelligence and Soft Computing Research, Volume 9, Issue 3, 2019, pp. 177-188.
  • [35] Z. Ma, X. Zhao, Y. Hou, Y. Man, W. Wang, An approach to extract straight lines with subpixel accuracy. In: Zhang Y., Zhou ZH., Zhang C., Li Y. (eds) Intelligent Science and Intelligent Data Engineering. IScIDE 2011. Lecture Notes in Computer Science, vol 7202. Springer, Berlin, Heidelberg, 2012, pp. n/a.
  • [36] D. Marr, E. Hildreth, Theory of edge detection, Proc. R. Soc. London, B-207, 1980, pp. 187-217.
  • [37] W.K. Pratt, Digital Image Processing, 4th Edition, John Wiley Inc., New York, 2007.
  • [38] N. Ofir, M. Galun, B. Nadler, R. Basri, Fast detection of curved edges at low SNR, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Las Vegas, NV, 2016, pp. 213-221.
  • [39] P. Qiu, Nonparametric estimation of jump surface, The Indian Journal of Statistics, Series A, Vol. 59, No. 2, 1997, pp. 268-294.
  • [40] P. Qiu, Jump surface estimation, edge detection, and image restoration, Journal of the American Statistical Association, No. 102, 2007, pp. 745-756.
  • [41] L. Romani, M. Rossini, D. Schenone, Edge detection methods based on RBF interpolation, Journal of Computational and Applied Mathematics, Vol. 349, 2019, pp. 532-547.
  • [42] L. Rutkowski, Sequential pattern recognition procedures derived from multiple Fourier series, Pattern Recognition Letters, Vol. 8, Issue 4, 1988, pp. 213-216.
  • [43] L. Rutkowski, Multiple Fourier series procedures for extraction of nonlinear regressions from noisy data, IEEE Transactions on Signal Processing, Vol. 41, No. 10, 1993, pp. 3062-3065.
  • [44] T. Rutkowski, J. Romanowski, P. Woldan, P. Staszewski, R. Nielek, L. Rutkowski, A content-based recommendation system using neuro-fuzzy approach, International Conference on Fuzzy Systems: FUZZ-IEEE, 2018, pp. 1-8.
  • [45] L. Rutkowski, M. Jaworski, P. Duda, Stream Data Mining: Algorithms and Their Probabilistic Properties, Springer, 2019.
  • [46] S. Singh, R. Singh, Comparison of various edge detection techniques, in: 2nd International Conference on Computing for Sustainable Global Development, 2015, pp. 393-396.
  • [47] C. Steger, Subpixel-precise extraction of lines and edges, ISPRS International Society for Photogrammetry and Remote Sensing, Journal of Photogrammetry and Remote Sensing, Vol. XXXIII, Amsterdam, 2000, pp. n/a.
  • [48] M.P. Wand, M.C. Jones, Kernel Smoothing. CRC Press, 1994.
  • [49] D. Ruppert, S. Sheather, M.P. Wand, An effective bandwidth selector for local least squares regression. Journal of the American Statistical Association, Taylor & Francis Group Pub., Vol. 90, No. 432, 1995, pp. 1257-1270.
  • [50] D. Ruppert, M.P. Wand, Multivariate locally weighted least squares regression. The Annals of Statistics, 1994, pp. 1346-1370.
  • [51] Y.-Q. Wang, A. Trouvé, Y. Amit, B. Nadler, Detecting curved edges in noisy images in sublinear time, Journal of Mathematical Imaging and Vision, November 2017, Vol. 59, Issue 3, 2017, pp 373-393.
  • [52] Y.G. Yatracos, Rates of convergence of minimum distance estimators and Kolmogorov’s entropy. The Annals of Statistics, Vol. 13, 1985, pp. 768-774.
  • [53] D. Ziou, S. Tabbone, Edge detection techniques -An overview, Pattern Recognition and Image Analysis, Vol. 8, No. 4, 1998, pp. 537-559.
Uwagi
Opracowanie rekordu ze środków MNiSW, umowa Nr 461252 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2021).
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-3f5b0642-1a87-42ee-baea-51912adb54a8
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