![]() ![]() M., Gatica-Perez, D., Tuytelaars, T., & Gool, L. V. Historical implications of a pattern of dates at Piedras Negras, Guatemala. A boundary-fragment model for object detection. IEEE Transactions on Pattern Analysis and Machine Intelligence, 27(11), 1832–1837. Efficient shape matching using shape contexts. International Journal of Computer Vision, 60(1), 63–86. Scale and affine interest point detectors. Chronicle of the Maya kings and queens: deciphering the dynasties of the ancient Maya. The new catalog of Maya hieroglyphs, vol. 1: the classic period inscriptions. conference on computer vision (ICCV), Kyoto. Shape guided contour grouping with particle filters. IEEE Transactions on Image Processing, 13, 340–353. Studying digital imagery of ancient paintings by mixtures of stochastic models. ![]() IEEE Transactions on Image Processing, 13(3), 302–313. An integrated content and metadata based retrieval system for art. J., Grimwood, P., Stevenson, A., Lahanier, C., & Stevenson, J. computer vision and pattern recognition (CVPR) conference, Miami. Shape discovery from unlabeled image collections. thesis, Universidad Complutense de Madrid. Evolución formal de las grafías escriturarias Mayas: implicaciones históricas y culturales. The foreign impact on lowland Maya language and script, vol. Justeson, J., Norman, W., Campbell, L., & Kaufman, T. Learning shape prior models for object matching. In European conference on computer vision, LNCS, Vol. I, pp. 304–317. Hamming embedding and weak geometric consistency for large scale image search. Die Entwicklung der Mayaschrift: Grundlagen zur Erforschung des Wandels der Mayaschrift von der protoklassik Bus zur spanischen Eroberung. Cambridge: Peabody Museum of Archeology and Ethnology. Introduction to the corpus, corpus of Maya hieroglyphic inscriptions. conference on computer vision (ICCV), Rio de Janeiro. Learning globally-consistent local distance functions for shape-based image retrieval and classification. IEEE Transactions on Pattern Analysis and Machine Intelligence, 30(1), 36–51.įrome, A., Singer, Y., Sha, F., & Malik, J. Groups of adjacent contours for object detection. ACM MM-MIR, Paris.įerrari, V., Fevrier, L., Jurie, F., & Schmid, C. precise search by visual content in cultural heritage image databases. Journal de la Societé des Américanistes, 47, 111–119.īoujemaa, N., Gouet, V., & Ferecatua, M. El glifo emblema en las inscripciones Mayas. IEEE Transactions on Pattern Analysis and Machine Intelligence, 24(4), 509–522.īerlin, H. Shape matching and object recognition using shape contexts. neural information processing systems, Denver, pp. 831–837.īelongie, S., Malik, J., & Puzicha, J. Shape context: a new descriptor for shape matching and object recognition. Reading: Addison Wesley.īelongie, S., Malik, J., & Puzicha, J. Overall, our approach is promising, as it improves performance on the retrieval task, has been successfully validated under an epigraphic viewpoint, and has the potential of offering both novel insights in archeology and practical solutions for real daily scholar needs.īaeza-Yates, R., & Ribeiro-Neto, B. Third, we present what to our knowledge constitutes the first automatic analysis of visual variability of syllabic glyphs along historical periods and across geographic regions of the ancient Maya world via the HOOSC descriptor. Based on the identification of their limitations, we propose a new shape descriptor named Histogram of Orientation Shape Context (HOOSC), which is more robust and suitable for description of Maya hieroglyphs. Second, we present an objective evaluation of the performance of two state-of-the-art shape-based contextual descriptors (Shape Context and Generalized Shape Context) in retrieval tasks, using two datasets of syllabic Maya glyphs. First, we introduce an overview of our interdisciplinary approach towards the improvement of the documentation, analysis, and preservation of Maya pictographic data. Our work is guided by realistic needs of archaeologists and scholars who critically need support for search and retrieval tasks in large Maya imagery collections. This paper presents an original approach for shape-based analysis of ancient Maya hieroglyphs based on an interdisciplinary collaboration between computer vision and archeology. ![]()
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