Zambanini, S., Kampel, M.: Coarse-to-fine correspondence search for classifying ancient coins. Reverse: FELICIT PVBL, Felicitas standing left, legs crossed, holding caduceus and leaning on column S below. Obverse: IMP MAXIMIANVS AVG, Radiate and cuirassed bust right. RIC Vb 360 Bust Type F, Cohen 95 - 2,8 grams - 21 mm - C. In: Daniilidis, K., Maragos, P., Paragios, N. Ae Antoninianus - Maximianus ( 290 to 291 ) - Lugdunum. Wang, K., Belongie, S.: Word spotting in the wild. Computer Vision and Image Understanding 112(1), 91–99 (2008) Shekhovtsov, A., Kovtun, I., Hlaváca, V.: Efficient mrf deformation model for non-rigid image matching. In: Muscle CIS Coin Competition Workshop, pp. Reisert, M., Ronneberger, O., Burkhardt, H.: An efficient gradient based registration technique for coin recognition. In: 7th International Conference on Digital Image Computing - Techniques and Applications, pp. Nölle, M., Penz, H., Rubik, M., Mayer, K., Holländer, I., Granec, R.: Dagobert - a new coin recognition and sorting system. From around 290 BC cast bronze Aes Signatum appeared. Van der Maaten, L.J., Poon, P.: Coin-o-matic: A fast system for reliable coin classification. Coinage in Rome first appeared before circa 290 BC using lumps of refined bronze called Aes Rude. International Journal of Computer Vision 60(2), 91–110 (2004) Lowe, D.G.: Distinctive image features from scale-invariant keypoints. IEEE Pattern Analysis and Machine Intelligence 33(5), 978–994 (2011) Liu, C., Yuen, J., Torralba, A.: Sift flow: Dense correspondence across scenes and its applications. In: International Conference on Virtual Systems and Multimedia, pp. Kavelar, A., Zambanini, S., Kampel, M.: Word detection applied to images of ancient roman coins. Kampel, M., Zaharieva, M.: Recognizing ancient coins based on local features. Huber-Mörk, R., Zambanini, S., Zaharieva, M., Kampel, M.: Identification of ancient coins based on fusion of shape and local features. In: Proceedings of the International Joint Conference on Neural Networks, vol. 2, pp. International Journal of Computer Vision 61, 55–79 (2005)įukumi, M., Omatu, S., Takeda, F., Kosaka, T.: Rotation-invariant neural pattern recognition system with application to coin recognition. 886–893 (2005)įelzenszwalb, P.F., Huttenlocher, D.P.: Pictorial structures for object recognition. In: Conference on Computer Vision and Pattern Recognition, pp. Cambridge University Press (1974)ĭalal, N., Triggs, B.: Histograms of oriented gradients for human detection. Pattern Recognition 13, 111–122 (1981)īremananth, R., Balaji, B., Sankari, M., Chitra, A.: A new approach to coin recognition using neural pattern analysis. Springer, Heidelberg (2012)īallard, D.H.: Generalizing the hough transform to detect arbitrary shapes. In: Fitzgibbon, A., Lazebnik, S., Perona, P., Sato, Y., Schmid, C. 1728–1734 (2010)Īrandjelović, O.: Reading ancient coins: Automatically identifying denarii using obverse legend seeded retrieval. ![]() Arandjelovic, O.: Automatic attribution of ancient roman imperial coins.
0 Comments
Leave a Reply. |
AuthorWrite something about yourself. No need to be fancy, just an overview. ArchivesCategories |