Computer algorithm for archaeological projectile points automatic classification

Fernando Castillo Flores, Francisco García Ugalde, Jose Luis Punzo Díaz, Jesus Zarco Navarro, Alfonso Gastelum-Strozzi, María Del Pilar Angeles, Mariko Nakano Miyatake

Research output: Contribution to journalArticlepeer-review

5 Scopus citations

Abstract

The manual archaeological projectile point morphological classification is an extensive and complex process since it involves a large number of categories. This article presents an algorithm that automatically makes this process, based on the projectile point digital image and using a classification scheme according to global archaeological approaches. The algorithm supports different conditions such as changes in scale and quality of the image. Moreover, it requires only a uniform background and an approximate north-south projectile point orientation. The principal computer methods that compose the algorithm are the curvature scale space map (CSS-map), the gradient contour on the projectile point, and the support vector machines (SVM) algorithm. Finally, the classifier was trained and tested on a dataset of approximately 800 projectile points images, and the results have shown a better performance than other shape descriptors such as Pyramid of Histograms of Orientation Gradients (PHOG), Histogram of Orientation Shape Context (HOOSC) (both used in a bag-of-words context), and geometric moment invariants (Hu moments).

Original languageEnglish
Article number3300972
JournalJournal on Computing and Cultural Heritage
Volume12
Issue number3
DOIs
StatePublished - Jun 2019

Keywords

  • Automatic classification
  • CSS-map
  • Computer vision
  • Image analysis
  • Lithic technology
  • Pattern recognition
  • Projectile points

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