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A New Edge Detector Based on SMOTE and Logistic Regression

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Book cover Advances in Fuzzy Logic and Technology 2017 (EUSFLAT 2017, IWIFSGN 2017)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 642))

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Abstract

Edge detection remains a hot topic due to its importance as a low level operation for high level operations in computer vision and the fact that there is no edge detector that is optimal for all kinds of images. In this paper, a new edge detector is proposed. The algorithm relies on the concept of edge detection as an imbalanced binary classification problem. In particular, each pixel is characterized by a gradients feature vector and classified as edge or non-edge pixel by means of logistic regression and hysteresis. This algorithm outperforms other state-of-the-art edge detectors both from the visual and quantitative points of view.

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Notes

  1. 1.

    This image dataset can be downloaded from ftp://figment.csee.usf.edu/pub/ROC/edge_comparison_dataset.tar.gz.

  2. 2.

    The details of the competition can be seen in http://irafm.osu.cz/edge2017/main.php.

References

  1. Boaventura, I., Gonzaga, A.: Method to evaluate the performance of edge detector. In: Proceedings of SIBGRAPI 2009, pp. 1–3 (2009)

    Google Scholar 

  2. Bowyer, K., Kranenburg, C., Dougherty, S.: Edge detector evaluation using empirical ROC curves. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR 1999), vol. 1, pp. 354–359 (1999)

    Google Scholar 

  3. Canny, J.: A computational approach to edge detection. IEEE Trans. Pattern Anal. Mach. Intell. 8(6), 679–698 (1986)

    Article  Google Scholar 

  4. Chawla, N.V., Bowyer, K.W., Hall, L.O., Kegelmeyer, W.P.: SMOTE: synthetic minority over-sampling technique. J. Artif. Int. Res. 16(1), 321–357 (2002)

    MATH  Google Scholar 

  5. Cox, D.R.: The regression analysis of binary sequences (with discussion). J. Roy. Stat. Soc. B 20, 215–242 (1958)

    MATH  Google Scholar 

  6. González-Hidalgo, M., Massanet, S., Mir, A., Ruiz-Aguilera, D.: On the generalization of the fuzzy morphological operators for edge detection. In: Alonso, J.M., Bustince, H., Reformat, M. (eds.) 2015 Conference of the International Fuzzy Systems Association and the European Society for Fuzzy Logic and Technology (IFSA-EUSFLAT 2015). Atlantis Press (2015)

    Google Scholar 

  7. Kovesi, P.D.: MATLAB and Octave functions for computer vision and image processing, http://www.csse.uwa.edu.au/~pk/research/matlabfns/

  8. López, V., Fernández, A., García, S., Palade, V., Herrera, F.: An insight into classification with imbalanced data: empirical results and current trends on using data intrinsic characteristics. Inf. Sci. 250, 113–141 (2013)

    Article  Google Scholar 

  9. Lopez-Molina, C., Bustince, H., Fernandez, J., Couto, P., De Baets, B.: A gravitational approach to edge detection based on triangular norms. Pattern Recogn. 43(11), 3730–3741 (2010)

    Article  MATH  Google Scholar 

  10. Lopez-Molina, C., De Baets, B., Bustince, H.: Quantitative error measures for edge detection. Pattern Recogn. 46(4), 1125–1139 (2013)

    Article  Google Scholar 

  11. Lopez-Molina, C., De Baets, B., Bustince, H., Sanz, J., Barrenechea, E.: Multiscale edge detection based on Gaussian smoothing and edge tracking. Knowl.-Based Syst. 44, 101–111 (2013)

    Article  Google Scholar 

  12. Martin, D.R., Fowlkes, C.C., Malik, J.: Learning to detect natural image boundaries using local brightness, color, and texture cues. IEEE Trans. Pattern Anal. Mach. Intell. 26(5), 530–549 (2004)

    Article  Google Scholar 

  13. Medina-Carnicer, R., Muñoz-Salinas, R., Yeguas-Bolivar, E., Diaz-Mas, L.: A novel method to look for the hysteresis thresholds for the Canny edge detector. Pattern Recogn. 44(6), 1201–1211 (2011)

    Article  Google Scholar 

  14. Papari, G., Petkov, N.: Edge and line oriented contour detection: state of the art. Image Vis. Comput. 29(2–3), 79–103 (2011)

    Article  Google Scholar 

  15. Perfilieva, I., Hodáková, P., Hurtík, P.: F1-transform edge detector inspired by Canny’s algorithm. In: Greco, S., et al. (eds.) Advances on Computational Intelligence: 14th International Conference on Information Processing and Management of Uncertainty in Knowledge-Based Systems, IPMU 2012. Proceedings, Part I, pp. 230–239. Springer, Heidelberg (2012)

    Chapter  Google Scholar 

  16. Pratt, W.K.: Digital Image Processing, 4th edn. Wiley-Interscience (2007)

    Google Scholar 

  17. R Core Team. R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna, Austria (2012)

    Google Scholar 

  18. Wang, R.: Edge detection using convolutional neural network. In: Cheng, L., et al. (ed.) Advances in Neural Networks, pp. 12–20. Springer (2016)

    Google Scholar 

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Acknowledgments

This paper has been partially supported by the Spanish Grant TIN2016-75404-P AEI/FEDER, UE.

Author information

Authors and Affiliations

  1. Department of Mathematics and Computer Science, University of the Balearic Islands, Crta. Valldemossa km. 7.5, E-07122, Palma, Spain

    Raquel Fernandez-Peralta, Sebastia Massanet & Arnau Mir

Authors
  1. Raquel Fernandez-Peralta
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  2. Sebastia Massanet
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  3. Arnau Mir
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Corresponding author

Correspondence to Sebastia Massanet .

Editor information

Editors and Affiliations

  1. Systems Research Institute, Polish Academy of Sciences, Warsaw, Poland

    Janusz Kacprzyk

  2. Systems Research Institute, Polish Academy of Sciences, Warsaw, Poland

    Eulalia Szmidt

  3. Systems Research Institute, Polish Academy of Sciences, Warsaw, Poland

    Slawomir Zadrożny

  4. Department of Telecommunications and Information Processing, Bulgarian Academy of Sciences, Sofia, Bulgaria

    K. T. Atanassov

  5. WIT - Warsaw School of Information Technology, Warsaw, Poland

    Maciej Krawczak

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Fernandez-Peralta, R., Massanet, S., Mir, A. (2018). A New Edge Detector Based on SMOTE and Logistic Regression. In: Kacprzyk, J., Szmidt, E., Zadrożny, S., Atanassov, K., Krawczak, M. (eds) Advances in Fuzzy Logic and Technology 2017. EUSFLAT IWIFSGN 2017 2017. Advances in Intelligent Systems and Computing, vol 642. Springer, Cham. https://doi.org/10.1007/978-3-319-66824-6_5

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  • DOI: https://doi.org/10.1007/978-3-319-66824-6_5

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  • Online ISBN: 978-3-319-66824-6

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