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Title: A novel window based method for approximating the Hausdorff in 3D range imagery.

Abstract

Matching a set of 3D points to another set of 3D points is an important part of any 3D object recognition system. The Hausdorff distance is known for it robustness in the face of obscuration, clutter, and noise. We show how to approximate the 3D Hausdorff fraction with linear time complexity and quadratic space complexity. We empirically demonstrate that the approximation is very good when compared to actual Hausdorff distances.

Authors:
Publication Date:
Research Org.:
Sandia National Laboratories
Sponsoring Org.:
USDOE
OSTI Identifier:
919145
Report Number(s):
SAND2004-4447
TRN: US200825%%60
DOE Contract Number:
AC04-94AL85000
Resource Type:
Technical Report
Country of Publication:
United States
Language:
English
Subject:
97; 99 GENERAL AND MISCELLANEOUS//MATHEMATICS, COMPUTING, AND INFORMATION SCIENCE; HAUSDORFF SPACE; THREE-DIMENSIONAL CALCULATIONS; APPROXIMATIONS; IMAGES; COMPUTER CALCULATIONS; Human face recognition (Computer science); Multispectral Imagery; Hausdorff measures.; IMAGERY

Citation Formats

Koch, Mark William. A novel window based method for approximating the Hausdorff in 3D range imagery.. United States: N. p., 2004. Web. doi:10.2172/919145.
Koch, Mark William. A novel window based method for approximating the Hausdorff in 3D range imagery.. United States. doi:10.2172/919145.
Koch, Mark William. Fri . "A novel window based method for approximating the Hausdorff in 3D range imagery.". United States. doi:10.2172/919145. https://www.osti.gov/servlets/purl/919145.
@article{osti_919145,
title = {A novel window based method for approximating the Hausdorff in 3D range imagery.},
author = {Koch, Mark William},
abstractNote = {Matching a set of 3D points to another set of 3D points is an important part of any 3D object recognition system. The Hausdorff distance is known for it robustness in the face of obscuration, clutter, and noise. We show how to approximate the 3D Hausdorff fraction with linear time complexity and quadratic space complexity. We empirically demonstrate that the approximation is very good when compared to actual Hausdorff distances.},
doi = {10.2172/919145},
journal = {},
number = ,
volume = ,
place = {United States},
year = {Fri Oct 01 00:00:00 EDT 2004},
month = {Fri Oct 01 00:00:00 EDT 2004}
}

Technical Report:

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