Bayesian pot-assembly from fragments as problems in perceptual-grouping and geometric-learning


Research output: Contribution to journalArticlepeer-review

36 Scopus citations


A heretofore unsolved problem of great archaeological importance is the automatic assembly of pots made on a wheel from the hundreds (or thousands) of sherds found at an excavation site. An approach is presented to the automatic estimation of mathematical models of such pots from 3D measurements of sherds. A Bayesian approach is formulated beginning with a description of the complete set of geometric parameters that determine the distribution of the sherd measurement data. Matching of fragments and aligning them geometrically into configurations is based on matching break-curves (curves on a pot surface separating fragments), estimated axis and profile curve pairs for individual fragments and configurations of fragments, and a number of features of groups of break-curves. Pot assembly is a bottom-up maximum likelihood performance-based search. Experiments are illustrated on pots which were broken for the purpose, and on sherds from an archaeological dig located in Petra, Jordan. The performance measure can also be an aposteriori probability, and many other types of information can be included, e.g., pot wall thickness, surface color, patterns on the surface, etc. This can also be viewed as the problem of learning a geometric object from an unorganized set of free-form fragments of the object and of clutter, or as a problem of perceptual grouping.

Original languageEnglish (US)
Pages (from-to)297-302
Number of pages6
JournalProceedings - International Conference on Pattern Recognition
Issue number3
StatePublished - 2002


  • Automatic pot assembly
  • Geometric learning
  • Perceptual grouping
  • Structure from unorganized 3D data

ASJC Scopus subject areas

  • Computer Vision and Pattern Recognition


Dive into the research topics of 'Bayesian pot-assembly from fragments as problems in perceptual-grouping and geometric-learning'. Together they form a unique fingerprint.

Cite this