de.mpg.escidoc.pubman.appbase.FacesBean
Deutsch

Hilfe Wegweiser Impressum Kontakt Einloggen

# Datensatz

DATENSATZ AKTIONENEXPORT

Freigegeben

Bericht

#### On characteristic points and approximate decision algorithms for the minimum Hausdorff distance

##### MPG-Autoren
http://pubman.mpdl.mpg.de/cone/persons/resource/persons45391

Schirra,  Stefan
Algorithms and Complexity, MPI for Informatics, Max Planck Society;

##### Externe Ressourcen
Es sind keine Externen Ressourcen verfügbar
##### Volltexte (frei zugänglich)

MPI-I-94-150.pdf
(beliebiger Volltext), 168KB

##### Ergänzendes Material (frei zugänglich)
Es sind keine frei zugänglichen Ergänzenden Materialien verfügbar
##### Zitation

Chew, L. P., Kedem, K., & Schirra, S.(1994). On characteristic points and approximate decision algorithms for the minimum Hausdorff distance (MPI-I-94-150). Saarbrücken: Max-Planck-Institut für Informatik.

We investigate {\em approximate decision algorithms} for determining whether the minimum Hausdorff distance between two points sets (or between two sets of nonintersecting line segments) is at most $\varepsilon$.\def\eg{(\varepsilon/\gamma)} An approximate decision algorithm is a standard decision algorithm that answers {\sc yes} or {\sc no} except when $\varepsilon$ is in an {\em indecision interval} where the algorithm is allowed to answer {\sc don't know}. We present algorithms with indecision interval $[\delta-\gamma,\delta+\gamma]$ where $\delta$ is the minimum Hausdorff distance and $\gamma$ can be chosen by the user. In other words, we can make our algorithm as accurate as desired by choosing an appropriate $\gamma$. For two sets of points (or two sets of nonintersecting lines) with respective cardinalities $m$ and $n$ our approximate decision algorithms run in time $O(\eg^2(m+n)\log(mn))$ for Hausdorff distance under translation, and in time $O(\eg^2mn\log(mn))$ for Hausdorff distance under Euclidean motion.