Edgar Roman-Rangel

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Archaeologists often spend significant time looking at traditional printed catalogs to identify and classify historical images. Our collaborative efforts between archaeologists and multimedia researchers seek to develop a tool to retrieve two specific types of ancient Maya visual information: hieroglyphs and iconographic elements. Towards that goal we(More)
This paper presents an original approach for shape-based analysis of ancient Maya hieroglyphs based on an interdisciplinary collaboration between computer vision and archeology. 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. Our paper has(More)
We introduce an interdisciplinary project for archaeological and computer vision research teams on the analysis of the ancient Maya writing system. Our first task is the automatic retrieval of Maya syllabic glyphs using the Shape Context descriptor. We investigated the effect of several parameters to adapt the shape descriptor given the high complexity of(More)
Abstract— Bag-of-visual-words or bag-of-visterms (bov) is a common technique used to index Multimedia information with the purposes of retrieval and classification. In this work we address the problem of constructing efficient bov representations of complex shapes as are the Maya syllabic hieroglyphs. Based on retrieval experiments, we assess and evaluate(More)
We present an overview of the CODICES project, an interdisciplinary approach for analysis of pre-Columbian collections of pictorial materials – more specifically, of Maya hieroglyphics. We discuss some of the main scientific and technical challenges that we have found in our work, and present a summary of our current technical achievements. This overview(More)
We analyze the performance of deep neural architectures for extracting shape representations of binary images, and for generating low-dimensional representations of them. In particular, we focus on indexing binary images exhibiting compounds of Maya hieroglyphic signs, referred to as glyph-blocks, which constitute a very challenging dataset of arts given(More)
We introduce the Tepalcatl project, an ongoing bi-disciplinary effort conducted by archaeologists and computer vision researchers, which focuses on developing statistical methods for the automatic categorization of potsherds; more precisely, potsherds from ancient Mexico including the Teotihuacan and Aztec civilizations. We captured 3D models of several(More)
In this work we address the problem of detecting instances of complex shapes in binary images. We investigated the effects of combining DoG and Harris-Laplace interest points with SIFT and HOOSC descriptors. Also, we propose the use of a retrieval-based detection framework suitable to deal with images that are sparsely annotated, and where the objects of(More)