Probabilistic 3D object recognition

Ilan Shimshoni, Jean Ponce

Research output: Contribution to journalArticlepeer-review


A probabilistic 3D object recognition algorithm is presented. In order to guide the recognition process the probability that match hypotheses between image features and model features are correct is computed. A model is developed which uses the probabilistic peaking effect of measured angles and ratios of lengths by tracing iso-angle and iso-ratio curves on the viewing sphere. The model also accounts for various types of uncertainty in the input such as incomplete and inexact edge detection. For each match hypothesis the pose of the object and the pose uncertainty which is due to the uncertainty in vertex position are recovered. This is used to find sets of hypotheses which reinforce each other by matching features of the same object with compatible uncertainty regions. A probabilistic expression is used to rank these hypothesis sets. The hypothesis sets with the highest rank are output. The algorithm has been fully implemented, and tested on real images.

Original languageEnglish
Pages (from-to)51-70
Number of pages20
JournalInternational Journal of Computer Vision
Issue number1
StatePublished - 2000
Externally publishedYes

Bibliographical note

Funding Information:
This work was supported in part by the Beckman Institute and the Center for Advanced Study of the University of Illinois at Urbana-Champaign, by the National Science Foundation under grant IRI-9224815, and by the National Aeronautics and Space Administration under grant NAG 1-613.

ASJC Scopus subject areas

  • Software
  • Computer Vision and Pattern Recognition
  • Artificial Intelligence


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