Probabilistic indexing for object recognition
Olson, C.F.
Dept. of Comput. Sci., Cornell Univ., Ithaca, NY ;
This paper appears in: Pattern Analysis and Machine Intelligence, IEEE Transactions on
Publication Date: May 1995
Volume: 17,
Issue: 5
On page(s): 518-522
ISSN: 0162-8828
References Cited: 17
CODEN: ITPIDJ
INSPEC Accession Number: 4988890
Digital Object Identifier: 10.1109/34.391391
Current Version Published: 2002-08-06
Abstract
Recent papers have indicated that indexing is a promising approach
to fast model-based object recognition because it allows most of the
possible matches between sets of image features and sets of model
features to be quickly eliminated from consideration. This
correspondence describes a system that is capable of indexing using sets
of three points undergoing 3D transformations and projection by taking
advantage of the probabilistic peaking effect. To be able to index using
sets of three points, we must allow false negatives. These are overcome
by ensuring that we examine several correct hypotheses. The use of these
techniques to speed up the alignment method is described. Results are
given on real and synthetic data
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