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Point probe decision trees for geometric concept classes

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

8 Scopus citations

Abstract

A fundamental problem in model-based computer vision is that of identifying to which of a given set of concept classes of geometric models an observed model belongs. Considering a “probe” to be an oracle that tells whether or not the observed model is present at a given point in an image, we study the problem of computing efficient strategies (“decision trees”) for probing an image, with the goal to minimize the number of probes necessary (in the worst case) to determine in which class the observed model belongs. We prove a hardness result and give strategies that obtain decision trees whose height is within a log factor of optimal.

Original languageEnglish
Title of host publicationAlgorithms and Data Structures - 3rd Workshop, WADS 1993, Proceedings
EditorsFrank Dehne, Jorg-Rudiger Sack, Nicola Santoro, Sue Whitesides
PublisherSpringer Verlag
Pages95-106
Number of pages12
ISBN (Print)9783540571551
DOIs
StatePublished - 1993
Event3rd Workshop on Algorithms and Data Structures, WADS 1993 - Montreal, Canada
Duration: Aug 11 1993Aug 13 1993

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume709 LNCS

Conference

Conference3rd Workshop on Algorithms and Data Structures, WADS 1993
Country/TerritoryCanada
CityMontreal
Period08/11/9308/13/93

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