Cite

[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 Search in Google Scholar

[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 Search in Google Scholar

[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 Search in Google Scholar

[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 Search in Google Scholar

[5] A. Borkowski, Surface breaklines modeling on the basis of laser scanning data, Archiwum Fotogrametrii, Kartografii i Teledetekcji, Vol. 17a, 2007, pp. 73-82. Search in Google Scholar

[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 Search in Google Scholar

[7] G.W. Corder, D.I. Foreman, Nonparametric Statistics: A Step-by-Step Approach. Wiley, New York, 2014. Search in Google Scholar

[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 Search in Google Scholar

[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. Search in Google Scholar

[10] L. Devroye, G. Lugosi, Combinatorial Methods in Density Estimation. Springer-Verlag, New York, 2001.10.1007/978-1-4613-0125-7 Search in Google Scholar

[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 Search in Google Scholar

[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. Search in Google Scholar

[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 Search in Google Scholar

[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. Search in Google Scholar

[15] R.L. Eubank, Nonparametric Regression and Spline Smoothing. 2nd edition, Marcel Dekker, New York, 1999.10.1201/9781482273144 Search in Google Scholar

[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 Search in Google Scholar

[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 Search in Google Scholar

[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 Search in Google Scholar

[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. Search in Google Scholar

[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 Search in Google Scholar

[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 Search in Google Scholar

[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. Search in Google Scholar

[23] R.C. Gonzales, R.E. Woods, Digital Image Processing, 4th Edition, Pearson, 2018. Search in Google Scholar

[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 Search in Google Scholar

[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 Search in Google Scholar

[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 Search in Google Scholar

[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 Search in Google Scholar

[28] L. Györfi, M. Kohler, A. Krzyżak, H. Walk, A Distribution-Free Theory of Nonparametric Regression. Springer, 2002.10.1007/b97848 Search in Google Scholar

[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 Search in Google Scholar

[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 Search in Google Scholar

[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 Search in Google Scholar

[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 Search in Google Scholar

[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 Search in Google Scholar

[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 Search in Google Scholar

[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 Search in Google Scholar

[36] D. Marr, E. Hildreth, Theory of edge detection, Proc. R. Soc. London, B-207, 1980, pp. 187-217.10.1098/rspb.1980.00206102765 Search in Google Scholar

[37] W.K. Pratt, Digital Image Processing, 4th Edition, John Wiley Inc., New York, 2007.10.1002/0470097434 Search in Google Scholar

[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 Search in Google Scholar

[39] P. Qiu, Nonparametric estimation of jump surface, The Indian Journal of Statistics, Series A, Vol. 59, No. 2, 1997, pp. 268-294. Search in Google Scholar

[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 Search in Google Scholar

[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 Search in Google Scholar

[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 Search in Google Scholar

[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 Search in Google Scholar

[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 Search in Google Scholar

[45] L. Rutkowski, M. Jaworski, P. Duda, Stream Data Mining: Algorithms and Their Probabilistic Properties, Springer, 2019.10.1007/978-3-030-13962-9 Search in Google Scholar

[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. Search in Google Scholar

[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. Search in Google Scholar

[48] M.P. Wand, M.C. Jones, Kernel Smoothing. CRC Press, 1994.10.1201/b14876 Search in Google Scholar

[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 Search in Google Scholar

[50] D. Ruppert, M.P. Wand, Multivariate locally weighted least squares regression. The Annals of Statistics, 1994, pp. 1346-1370.10.1214/aos/1176325632 Search in Google Scholar

[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 Search in Google Scholar

[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 Search in Google Scholar

[53] D. Ziou, S. Tabbone, Edge detection techniques -An overview, Pattern Recognition and Image Analysis, Vol. 8, No. 4, 1998, pp. 537-559. Search in Google Scholar

eISSN:
2449-6499
Idioma:
Inglés
Calendario de la edición:
4 veces al año
Temas de la revista:
Computer Sciences, Databases and Data Mining, Artificial Intelligence