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Novel Methods for Image Description

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Computer Vision Methods for Fast Image Classification and Retrieval

Part of the book series: Studies in Computational Intelligence ((SCI,volume 821))

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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.

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References

  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)

    Article  Google Scholar 

  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. 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. Guberman, S., Maximov, V.V., Pashintsev, A.: Gestalt and image understanding. Gestalt Theor. 34(2), 143 (2012)

    Google Scholar 

  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)

    Article  Google Scholar 

  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. Marr, D., Hildreth, E.: Theory of edge detection. Proc. R. Soc. Lond. B 207(1167), 187–217 (1980)

    Article  Google Scholar 

  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)

    Article  Google Scholar 

  9. Milborrow, S., Nicolls, F.: Locating facial features with an extended active shape model. Comput. Vis. ECCV 2008, 504–513 (2008)

    Google Scholar 

  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)

    Chapter  Google Scholar 

  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. 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. 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)

    Chapter  Google Scholar 

  14. Papari, G., Petkov, N.: Adaptive pseudo dilation for gestalt edge grouping and contour detection. IEEE Trans. Image Process. 17(10), 1950–1962 (2008)

    Article  MathSciNet  Google Scholar 

  15. Petermann, B.: The Gestalt Theory and the Problem of Configuration. Routledge (2013)

    Google Scholar 

  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. Rogers, M., Graham, J.: Robust active shape model search. In: European Conference on Computer Vision, pp. 517–530. Springer (2002)

    Google Scholar 

  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)

    Article  Google Scholar 

  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. Tek, F.B., Dempster, A.G., Kale, I.: Computer vision for microscopy diagnosis of malaria. Malar. J. 8(1), 153 (2009)

    Article  Google Scholar 

  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)

    Article  Google Scholar 

  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)

    Article  Google Scholar 

  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. 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)

    Article  Google Scholar 

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Authors and Affiliations

  1. Institute of Computational Intelligence, Częstochowa University of Technology, Częstochowa, Poland

    Rafał Scherer

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  1. Rafał Scherer
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Correspondence to Rafał Scherer .

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Scherer, R. (2020). Novel Methods for Image Description. In: Computer Vision Methods for Fast Image Classification and Retrieval. Studies in Computational Intelligence, vol 821. Springer, Cham. https://doi.org/10.1007/978-3-030-12195-2_4

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