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A New Approach to Detection of Changes in Multidimensional Patterns - Part II Cover

A New Approach to Detection of Changes in Multidimensional Patterns - Part II

Open Access
|May 2021

References

  1. [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. 750-763.10.1007/978-3-642-15561-1_54
  2. [2] D. Bazazian, J.R. Casas, J. Ruiz-Hidalgo, Fast and robust edge extraction in unorganized point clouds, No. 11, 2015, pp 1-8.10.1109/DICTA.2015.7371262
  3. [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. 116-128.10.1016/j.spl.2005.04.036
  4. [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. 220-226.10.1016/j.protcy.2012.05.033
  5. [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. [6] J.F. Canny, A computational approach to edge detection, IEEE Trans. Pattern Analysis and Machine Intelligence, Vol. 8, No. 6, 1986, pp. 679-698.10.1109/TPAMI.1986.4767851
  7. [7] G.W. Corder, D.I. Foreman, Nonparametric Statistics: A Step-by-Step Approach. Wiley, New York, 2014.
  8. [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. 969-975.10.1109/FUZZY.2008.4630487
  9. [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. [10] L. Devroye, G. Lugosi, Combinatorial Methods in Density Estimation. Springer-Verlag, New York, 2001.10.1007/978-1-4613-0125-7
  11. [11] J.R Dim, T. Takamura, Alternative approach for satellite cloud classification: edge gradient application, Advances in Meteorology, 2013, pp. 1-8.10.1155/2013/584816
  12. [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. [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. 497-518.10.1016/j.ins.2017.07.013
  14. [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. [15] R.L. Eubank, Nonparametric Regression and Spline Smoothing. 2nd edition, Marcel Dekker, New York, 1999.10.1201/9781482273144
  16. [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. 202-214.10.1016/j.inffus.2018.02.003
  17. [17] T. Gałkowski, L. Rutkowski, Nonparametric recovery of multivariate functions with applications to system identification, Proceedings of the IEEE, Vol. 73, 1985, pp. 942-943.10.1109/PROC.1985.13223
  18. [18] T. Gałkowski, L. Rutkowski, Nonparametric fitting of multivariable functions, IEEE Transactions on Automatic Control, Vol. AC-31, 1986, pp. 785-787.10.1109/TAC.1986.1104399
  19. [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. [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. 125-136.10.2478/jaiscr-2020-0009
  21. [21] T. Gasser, H.-G. Müller, Kernel estimation of regression functions, Lecture Notes in Mathematics, Vol. 757. Springer-Verlag, Heidelberg, 1979, pp. 23-68.10.1007/BFb0098489
  22. [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. [23] R.C. Gonzales, R.E. Woods, Digital Image Processing, 4th Edition, Pearson, 2018.
  24. [24] A. Gramacki, J. Gramacki, FFT-based fast bandwidth selector for multivariate kernel density estimation. Computational Statistics & Data Analysis, Elsevier, Vol. 106, 2017, pp. 27-45.10.1016/j.csda.2016.09.001
  25. [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. 31-39.10.17512/jamcm.2015.4.04
  26. [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. 566-576.10.1007/978-3-319-19324-3_50
  27. [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. 647-659.10.1007/978-3-319-39384-1_57
  28. [28] L. Györfi, M. Kohler, A. Krzyżak, H. Walk, A Distribution-Free Theory of Nonparametric Regression. Springer, 2002.10.1007/b97848
  29. [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. 458-483.10.1137/140970331
  30. [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. 2516-2529.10.1109/TNNLS.2017.269820428500013
  31. [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.10.1186/s41044-018-0035-y
  32. [32] M. Kolomenkin, I. Shimshoni, A. Tal, On edge detection on surfaces, 2009 IEEE Conference on Computer Vision and Pattern Recognition, 2009, pp. 2767-2774.10.1109/CVPR.2009.5206517
  33. [33] S. Kullback, R.A. Leibler, On information and sufficiency, The Annals of Mathematical Statistics. Vol. 22, No. 1, 1951, pp. 79-86.10.1214/aoms/1177729694
  34. [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. 177-188.10.2478/jaiscr-2019-0002
  35. [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.10.1007/978-3-642-31919-8_85
  36. [36] D. Marr, E. Hildreth, Theory of edge detection, Proc. R. Soc. London, B-207, 1980, pp. 187-217.10.1098/rspb.1980.00206102765
  37. [37] W.K. Pratt, Digital Image Processing, 4th Edition, John Wiley Inc., New York, 2007.10.1002/0470097434
  38. [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. 213-221.10.1109/CVPR.2016.30
  39. [39] P. Qiu, Nonparametric estimation of jump surface, The Indian Journal of Statistics, Series A, Vol. 59, No. 2, 1997, pp. 268-294.
  40. [40] P. Qiu, Jump surface estimation, edge detection, and image restoration, Journal of the American Statistical Association, No. 102, 2007, pp. 745-756.10.1198/016214507000000301
  41. [41] L. Romani, M. Rossini, D. Schenone, Edge detection methods based on RBF interpolation, Journal of Computational and Applied Mathematics, Vol. 349, 2019, pp. 532-547.10.1016/j.cam.2018.08.006
  42. [42] L. Rutkowski, Sequential pattern recognition procedures derived from multiple Fourier series, Pattern Recognition Letters, Vol. 8, Issue 4, 1988, pp. 213-216.10.1016/0167-8655(88)90027-X
  43. [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. 3062-3065.10.1109/78.277809
  44. [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. 1-8.10.1109/FUZZ-IEEE.2018.8491543
  45. [45] L. Rutkowski, M. Jaworski, P. Duda, Stream Data Mining: Algorithms and Their Probabilistic Properties, Springer, 2019.10.1007/978-3-030-13962-9
  46. [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. [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. [48] M.P. Wand, M.C. Jones, Kernel Smoothing. CRC Press, 1994.10.1201/b14876
  49. [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. 1257-1270.10.1080/01621459.1995.10476630
  50. [50] D. Ruppert, M.P. Wand, Multivariate locally weighted least squares regression. The Annals of Statistics, 1994, pp. 1346-1370.10.1214/aos/1176325632
  51. [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 373-393.10.1007/s10851-016-0689-x
  52. [52] Y.G. Yatracos, Rates of convergence of minimum distance estimators and Kolmogorov’s entropy. The Annals of Statistics, Vol. 13, 1985, pp. 768-774.10.1214/aos/1176349553
  53. [53] D. Ziou, S. Tabbone, Edge detection techniques -An overview, Pattern Recognition and Image Analysis, Vol. 8, No. 4, 1998, pp. 537-559.
Language: English
Page range: 217 - 227
Submitted on: Aug 23, 2020
Accepted on: Apr 12, 2021
Published on: May 29, 2021
Published by: SAN University
In partnership with: Paradigm Publishing Services
Publication frequency: 4 issues per year

© 2021 Tomasz Gałkowski, Adam Krzyżak, Zofia Patora-Wysocka, Zbigniew Filutowicz, Lipo Wang, published by SAN University
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