Full text: Recording, documentation and cooperation for cultural heritage

International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Volume XL-5/W2, 2013 
XXIV International CIPA Symposium, 2 — 6 September 2013, Strasbourg, France 
Ballard, D. H., 1981. Generalizing the hough transform to detect 
arbitrary shapes. Pattern Recognition 13, pp. 111-122. 
Bremananth, R., Balaji, B., Sankari, M. and Chitra, A., 2005. A 
new approach to coin recognition using neural pattern analysis. 
In: Proceedings of IEEE INDICON 2005, pp. 366-370. 
Crawford, M. H., 1974. Roman republican coinage. Cambridge 
University Press. 
Dalal, N. and Triggs, B., 2005. Histograms of oriented gradi- 
ents for human detection. In: Proceedings of the Conference on 
Computer Vision and Pattern Recognition, pp. 886-893. 
Felzenszwalb, P. F. and Huttenlocher, D. P., 2005. Pictorial struc- 
tures for object recognition. International Journal of Computer 
Vision 61, pp. 55-79. 
Fukumi, M., Omatu, S., Takeda, F. and Kosaka, T., 1991. 
Rotation-invariant neural pattern recognition system with appli- 
cation to coin recognition. In: Proceedings of the International 
Joint Conference on Neural Networks, Vol. 2, pp. 1027-1032. 
Huber-Mórk, R., Zambanini, S., Zaharieva, M. and Kampel, M., 
2011. Identification of ancient coins based on fusion of shape and 
local features. Machine Vision Applications 22, pp. 983—994. 
Kampel, M. and Zaharieva, M., 2008. Recognizing ancient coins 
based on local features. In: Advances in Visual Computing, Vol. 
5358, Springer, pp. 11-22. 
Kavelar, A., Zambanini, S. and Kampel, M., 2012. Word detec- 
tion applied to images of ancient roman coins. In: International 
Conference on Virtual Systems and Multimedia, pp. 577-580. 
Liu, C., Yuen, J. and Torralba, A., 2011. Sift flow: Dense cor- 
respondence across scenes and its applications. Pattern Analysis 
and Machine Intelligence 33, pp. 978-994. 
Lowe, D. G., 2004. Distinctive image features from scale- 
invariant keypoints. International Journal of Computer Vision 
60(2), pp. 91-110. 
Nôlle, M., Penz, H., Rubik, M., Mayer, K., Hollander, I. and 
Granec, R., 2003. Dagobert - a new coin recognition and sort- 
ing system. In: Proceedings of the 7th International Confer- 
ence on Digital Image Computing - Techniques and Applications, 
pp. 329-338. 
Reisert, M., Ronneberger, O. and Burkhardt, H., 2006. An effi- 
cient gradient based registration technique for coin recognition. 
In: Proceedings of the Muscle CIS Coin Competition Workshop, 
pp. 19-31. 
van der Maaten, L. J. and Poon, P., 2006. Coin-o-matic: A fast 
system for reliable coin classification. In: Proceedings of the 
Muscle CIS Coin Competition Workshop, pp. 7-18. 
Zambanini, S. and Kampel, M., 2009. Segmentation of ancient 
coins based on local entropy and gray value range. In: Pro- 
ceedings of the 4th International Conference on Computer Vision 
Theory and Applications, Vol. 1, pp. 273-276. 
Zambanini, S. and Kampel, M., 2013a. Coarse-to-fine correspon- 
dence search for classifying ancient coins. In: J.-L Park and 
J. Kim (eds), Computer Vision - ACCV 2012 Workshops, Lec- 
ture Notes in Computer Science, Vol. 7729, Springer Berlin Hei- 
delberg, pp. 25-36. 
Zambanini, S. and Kampel, M., 2013b. A local image de- 
scriptor robust to illumination changes. In: J.-K. Kmrinen and 
M. Koskela (eds), Scandinavian Conference on Image Analysis, 
Lecture Notes in Computer Science, Vol. 7944, Springer Berlin 
Heidelberg, pp. 11-21. 
Zambanini, S., Kavelar, A. and Kampel, M., 2013. Improving 
ancient roman coin classification by fusing exemplar-based clas- 
sification and legend recognition. In: International Workshop on 
Multimedia for Cultural Heritage. 
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