![]() ![]() al., 2013) which has similarities to other descriptors of the recognition literature (Belongie et. For the first component, we use the histogram of orientation shape context (HOOSC) (Roman-Rangel et. We introduce (1) an approach to analyse Maya glyphs combining a state-of-the-art visual shape descriptor, and (2) a non-linear method to visualize high-dimensional data. In this paper, we built a prototype for visualization of glyphs based on visual features. This motivates the study of data visualization. visitors in museums), and offer promising perspectives for scholars. They are also more engaging for users (i.e. The tools we envision are different from existing almanac-by-almanac visualization systems (Vail and Hernandez, 2013). Adding functionalities that take context (i.e., co-occurrence statistics, characteristics of the data) and part-whole relations (i.e., highlighting diagnostic parts) into account would bring guidance during decipherment tasks. 2 illustrates the challenges to analyse Maya glyphs visually. ![]() Maya glyph samples from several categories (according to Thompson's catalog) that illustrate the within-class variety and between-class similarityįig. ![]()
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