Efficient search and verification for function based classification from real range images

Guy Froimovich Ehud Rivlin, Ilan Shimshoni, Octavian Soldea

Research output: Contribution to journalArticlepeer-review


In this work we propose a probabilistic model for generic object classification from raw range images. Our approach supports a validation process in which classes are verified using a functional class graph in which functional parts and their realization hypotheses are explored. The validation tree is efficiently searched. Some functional requirements are validated in a final procedure for more efficient separation of objects from non-objects. The search employs a knowledge repository mechanism that monotonically adds knowledge during the search and speeds up the classification process. Finally, we describe our implementation and present results of experiments on a database that comprises about 150 real raw range images of object instances from 10 classes.

Original languageEnglish
Pages (from-to)200-217
Number of pages18
JournalComputer Vision and Image Understanding
Issue number3
StatePublished - Mar 2007


  • 3D segmentation
  • Classification
  • Computer vision
  • Function based reasoning
  • Raw range images
  • Recognition

ASJC Scopus subject areas

  • Software
  • Signal Processing
  • Computer Vision and Pattern Recognition


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