Advertisement

Novel Methods for Image Description

  • Rafał SchererEmail author
Chapter
Part of the Studies in Computational Intelligence book series (SCI, volume 821)

Abstract

This chapter presents new methods for continuous edge detection and description. Standard edge detection algorithms confronted with the human perception of reality are rather primitive because they are based only on the information stored in the form of pixels. Humans can see elements of the images that do not exist in them. These mechanisms allow humans to extract and track objects partially obscured.

References

  1. 1.
    Achanta, R., Shaji, A., Smith, K., Lucchi, A., Fua, P., Süsstrunk, S., et al.: Slic superpixels compared to state-of-the-art superpixel methods. IEEE Trans. Pattern Anal. Mach. Intell. 34(11), 2274–2282 (2012)CrossRefGoogle Scholar
  2. 2.
    Cireşan, D.C., Giusti, A., Gambardella, L.M., Schmidhuber, J.: Mitosis detection in breast cancer histology images with deep neural networks. In: International Conference on Medical Image Computing and Computer-assisted Intervention, pp. 411–418. Springer (2013)Google Scholar
  3. 3.
    Flores-Quispe, R., Velazco-Paredes, Y., Escarcina, R.E.P., Castañón, C.A.B.: Automatic identification of human parasite eggs based on multitexton histogram retrieving the relationships between textons. In: 2014 33rd International Conference of the Chilean Computer Science Society (SCCC), pp. 102–106. IEEE (2014)Google Scholar
  4. 4.
    Guberman, S., Maximov, V.V., Pashintsev, A.: Gestalt and image understanding. Gestalt Theor. 34(2), 143 (2012)Google Scholar
  5. 5.
    Jiang, M., Zhang, S., Huang, J., Yang, L., Metaxas, D.N.: Scalable histopathological image analysis via supervised hashing with multiple features. Med. Image Anal. 34, 3–12 (2016)CrossRefGoogle Scholar
  6. 6.
    Lim, J.J., Zitnick, C.L., Dollár, P.: Sketch tokens: A learned mid-level representation for contour and object detection. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 3158–3165 (2013)Google Scholar
  7. 7.
    Marr, D., Hildreth, E.: Theory of edge detection. Proc. R. Soc. Lond. B 207(1167), 187–217 (1980)CrossRefGoogle Scholar
  8. 8.
    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)CrossRefGoogle Scholar
  9. 9.
    Milborrow, S., Nicolls, F.: Locating facial features with an extended active shape model. Comput. Vis. ECCV 2008, 504–513 (2008)Google Scholar
  10. 10.
    Najgebauer, P., Nowak, T., Romanowski, J., Rygal, J., Korytkowski, M.: Representation of edge detection results based on graph theory. In: Rutkowski, L., Korytkowski, M., Scherer, R., Tadeusiewicz, R., Zadeh, L.A., Zurada, J.M. (eds.) Artificial Intelligence and Soft Computing, pp. 588–601. Springer, Berlin, Heidelberg (2013)CrossRefGoogle Scholar
  11. 11.
    Najgebauer, P., Rutkowski, L., Scherer, R.: Interest point localization based on edge detection according to gestalt laws. In: 2017 2nd IEEE International Conference on Computational Intelligence and Applications (ICCIA), pp. 349–353 (2017)Google Scholar
  12. 12.
    Najgebauer, P., Rutkowski, L., Scherer, R.: Novel method for joining missing line fragments for medical image analysis. In: 2017 22nd International Conference on Methods and Models in Automation and Robotics (MMAR), pp. 861–866 (2017)Google Scholar
  13. 13.
    Nowak, T., Najgebauer, P., Ryga, J., Scherer, R.: A novel graph-based descriptor for object matching. In: Rutkowski, L., Korytkowski, M., Scherer, R., Tadeusiewicz, R., Zadeh, L., Zurada, J. (eds.) Artificial Intelligence and Soft Computing. Lecture Notes in Computer Science, vol. 7894, pp. 602–612. Springer, Berlin, Heidelberg (2013)CrossRefGoogle Scholar
  14. 14.
    Papari, G., Petkov, N.: Adaptive pseudo dilation for gestalt edge grouping and contour detection. IEEE Trans. Image Process. 17(10), 1950–1962 (2008)MathSciNetCrossRefGoogle Scholar
  15. 15.
    Petermann, B.: The Gestalt Theory and the Problem of Configuration. Routledge (2013)Google Scholar
  16. 16.
    Ren, X., Fowlkes, C.C., Malik, J.: Scale-invariant contour completion using conditional random fields. In: Null, pp. 1214–1221. IEEE (2005)Google Scholar
  17. 17.
    Rogers, M., Graham, J.: Robust active shape model search. In: European Conference on Computer Vision, pp. 517–530. Springer (2002)Google Scholar
  18. 18.
    Suzuki, C.T., Gomes, J.F., Falcao, A.X., Papa, J.P., Hoshino-Shimizu, S.: Automatic segmentation and classification of human intestinal parasites from microscopy images. IEEE Trans. Biomed. Eng. 60(3), 803–812 (2013)CrossRefGoogle Scholar
  19. 19.
    Tchiotsop, D., Tchinda, R., Didier, W., NOUBOM, M.: Automatic recognition of human parasite cysts on microscopic stools images using principal component analysis and probabilistic neural network. Editor. Pref. 4(9) (2015)Google Scholar
  20. 20.
    Tek, F.B., Dempster, A.G., Kale, I.: Computer vision for microscopy diagnosis of malaria. Malar. J. 8(1), 153 (2009)CrossRefGoogle Scholar
  21. 21.
    Veta, M., Van Diest, P.J., Willems, S.M., Wang, H., Madabhushi, A., Cruz-Roa, A., Gonzalez, F., Larsen, A.B., Vestergaard, J.S., Dahl, A.B., et al.: Assessment of algorithms for mitosis detection in breast cancer histopathology images. Med. Image Anal. 20(1), 237–248 (2015)CrossRefGoogle Scholar
  22. 22.
    Wang, S., Kubota, T., Siskind, J.M., Wang, J.: Salient closed boundary extraction with ratio contour. IEEE Trans. Pattern Anal. Mach. Intell. 27(4), 546–561 (2005)CrossRefGoogle Scholar
  23. 23.
    Yang, K., Gao, S., Li, C., Li, Y.: Efficient color boundary detection with color-opponent mechanisms. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2810–2817 (2013)Google Scholar
  24. 24.
    Yang, Y.S., Park, D.K., Kim, H.C., Choi, M.H., Chai, J.Y.: Automatic identification of human helminth eggs on microscopic fecal specimens using digital image processing and an artificial neural network. IEEE Trans. Biomed. Eng. 48(6), 718–730 (2001)CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.Institute of Computational IntelligenceCzęstochowa University of TechnologyCzęstochowaPoland

Personalised recommendations