Computer Aided Recognition and Classification of Coats of Arms

Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 736)

Abstract

This paper describes the design and development of a system for detection and recognition of coat of arms and its heraldic parts (components). It introduces the methods by which individual features can be implemented. Most of the heraldic parts are segmented using a convolution neural networks and the rest of them are segmented using active contour model. The Histogram of the gradient method was chosen for coats of arms detection in an image. For training and functionality verification we used our own data that was created as a part of our research. The resulting system can serve as an auxiliary tool used in heraldry and other sciences related to history.

Keywords

Image segmentation Neural networks Heraldry 

Notes

Acknowledgements

This work was supported by the BUT project FIT-S-17-4014 and the IT4IXS: IT4Innovations Excellence in Science project (LQ1602).

References

  1. 1.
    Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2005)Google Scholar
  2. 2.
    Cipolla, R., Battiatob, S., Farinella, G.M.: Computer Vision: Detection, Recognition and Reconstruction. Studies in Computational Intelligence, vol. 285. Springer, New York (2010). ISBN 978-3-642-12847-9Google Scholar
  3. 3.
    Hyeonwooh, N., Seunghoon, H., Bohyung, H.: Learning deconvolution network for semantic segmentation. arXiv preprint arXiv:1505.04366 (2015)
  4. 4.
    Long, J., Shellhamer, E. Darrell, T.: Fully convolutional networks for semantic segmentation. CoRR, volume abs/1605.06211 (2016). http://arxiv.org/abs/1605.06211
  5. 5.
    Kass, M.: Snakes: active contour models. Int. J. Comput. Vis. 1(4), 321–331 (1998).  https://doi.org/10.1007/bf00133570. ISSN 1573-1405CrossRefGoogle Scholar
  6. 6.
    Schwarzenberg, K.F.: Heraldika: heraldika, čili, Přehled její theorie se zřetelem k Čechám na vývojovém základě. In: Vyšehrad 2, vol. 3. Vyšehrad, Prague (2007). Historica (Vyšehrad). ISBN 978-80-7021-827-3Google Scholar
  7. 7.
    Janáček, J., Louda, J.: České erby. Albatros, Prague (1974). Oko (Albatros)Google Scholar
  8. 8.
    Mysliveček, M.: Erbovník, aneb, Kniha o znacích i osudech rodů žijících v Čechách a na Moravě podle starých pramenů a dávných ne vždy věrných svědectví. Horizont, Praha (1993). ISBN 80-7012-070-3Google Scholar
  9. 9.
    Introduction to Support Vector Machines. OpenCV (2016). http://docs.opencv.org/2.4/doc/tutorials/ml/introduction_to_svm/introduction_to_svm.html. Accessed 30 Dec 2016
  10. 10.
    Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. CoRR, volume abs/1409.1556 (2014). http://arxiv.org/abs/1409.1556
  11. 11.
    Rosebrock, A.: Intersection over Union (IoU) for object detection. https://www.pyimagesearch.com/2016/11/07/intersection-over-union-iou-for-object-detection/. Accessed 30 Dec 2016
  12. 12.
    Rahman, A., Wang, Y.: Optimizing intersection-over-union in deep neural networks for image segmentation. In: International Symposium on Visual Computing, pp. 234–244. Springer, Cham (2016)Google Scholar
  13. 13.
    Halada, J.: Lexikon české šlechty II: erby, fakta, osobnosti, sídla a zajímavosti. Illustrated by František Doubek. Akropolis, Prague (1993). ISBN 80-85770-04-0Google Scholar
  14. 14.
    Sedláček, A., Růžek, V.: Atlasy erbů a pečetí české a moravské středověké šlechty: vol. 2. Academia, Prague (2001). ISBN 80-200-0935-3Google Scholar
  15. 15.
    Vavřínek, K.: Almanach českých šlechtických a rytířských rodů 2027. Vavřínek Zdeněk (2016)Google Scholar

Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  1. 1.FIT, IT4Innovations Centre of ExcellenceBrno University of TechnologyBrnoCzech Republic

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