Probabilistic 3D object recognition

Ilan Shimshoni, Jean Ponce

Research output: Contribution to conferencePaperpeer-review

Abstract

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 subsets. 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
Pages488-493
Number of pages6
StatePublished - 1995
Externally publishedYes
EventProceedings of the 5th International Conference on Computer Vision - Cambridge, MA, USA
Duration: 20 Jun 199523 Jun 1995

Conference

ConferenceProceedings of the 5th International Conference on Computer Vision
CityCambridge, MA, USA
Period20/06/9523/06/95

ASJC Scopus subject areas

  • Engineering (all)

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