Model-based object recognition by geometric hashing

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

The Geometric Hashing paradigm for model-based recognition of objects in cluttered scenes is discussed. This paradigm enables a unified approach to rigid object recognition under different viewing transformation assumptions both for 2-D and 3-D objects obtained by different sensors, e.g. vision, range, tactile. It is based on an intensive off-line model preprocessing (learning) stage, where model information is indexed into a hash-table using minimal, transformation invariant features. This enables the on-line recognition algorithm to be particularly efficient. The algorithm is straightforwardly parallelizable. Initial experimentation of the technique has led to successful recognition of both 2-D and 3-D objects in cluttered scenes from an arbitrary viewpoint. We, also, compare the Geometric Hashing with the Hough Transform and the alignment techniques. Extensions of the basic paradigm which reduce its worst case recognition complexity are discussed.

Original languageEnglish
Title of host publicationComputer Vision – ECCV 1990 - 1st European Conference on Computer Vision, Proceedings
EditorsOlivier Faugeras
PublisherSpringer Verlag
Pages526-536
Number of pages11
ISBN (Print)9783540525226
DOIs
StatePublished - 1990
Event1st European Conference on Computer Vision, ECCV 1990 - Antibes, France
Duration: 23 Apr 199027 Apr 1990

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume427 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference1st European Conference on Computer Vision, ECCV 1990
Country/TerritoryFrance
CityAntibes
Period23/04/9027/04/90

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