Representation and classification of 3-D objects

Péter Csákány, Andrew M. Wallace

Research output: Contribution to journalArticlepeer-review

17 Citations (Scopus)


This paper addresses the problem of generic object classification from three-dimensional depth or meshed data. First, surface patches are segmented on the basis of differential geometry and quadratic surface fitting. These are represented by a modified Gaussian image that includes the well-known shape index. Learning is an interactive process in which a human teacher indicates corresponding patches, but the formation of generic classes is unaided. Classification of unknown objects is based on the measurement of similarities between feature sets of the objects and the generic classes. The process is demonstrated on a group of three-dimensional (3-D) objects built from both CAD and laser-scanned depth data.

Original languageEnglish
Pages (from-to)638-647
Number of pages10
JournalIEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Issue number4
Publication statusPublished - Aug 2003


  • 3-D vision
  • Classification
  • Object recognition


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