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Precision Range Image Registration Using a Robust Surface Interpenetration Measure and Enhanced Genetic Algorithms
May 2005 (vol. 27 no. 5)
pp. 762-776
ASCII Text
x 
 
Luciano Silva, Olga R.P. Bellon, Kim L. Boyer, "Precision Range Image Registration Using a Robust Surface Interpenetration Measure and Enhanced Genetic Algorithms," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 27, no. 5, pp. 762-776, May, 2005.
 
 
BibTex
x
 
@article{ 10.1109/TPAMI.2005.108,
author = {Luciano Silva and Olga R.P. Bellon and Kim L. Boyer},
title = {Precision Range Image Registration Using a Robust Surface Interpenetration Measure and Enhanced Genetic Algorithms},
journal ={IEEE Transactions on Pattern Analysis and Machine Intelligence},
volume = {27},
number = {5},
issn = {0162-8828},
year = {2005},
pages = {762-776},
doi = {http://doi.ieeecomputersociety.org/10.1109/TPAMI.2005.108},
publisher = {IEEE Computer Society},
address = {Los Alamitos, CA, USA},
}
 
 
RefWorks Procite/RefMan/Endnote
x 
 
TY - JOUR
JO - IEEE Transactions on Pattern Analysis and Machine Intelligence
TI - Precision Range Image Registration Using a Robust Surface Interpenetration Measure and Enhanced Genetic Algorithms
IS - 5
SN - 0162-8828
SP762
EP776
EPD - 762-776
A1 - Luciano Silva,
A1 - Olga R.P. Bellon,
A1 - Kim L. Boyer,
PY - 2005
KW - Range image registration
KW - genetic algorithms
KW - robust methods
KW - stochastic search.
VL - 27
JA - IEEE Transactions on Pattern Analysis and Machine Intelligence
ER -
 
This paper addresses the range image registration problem for views having low overlap and which may include substantial noise. The current state of the art in range image registration is best represented by the well-known iterative closest point (ICP) algorithm and numerous variations on it. Although this method is effective in many domains, it nevertheless suffers from two key limitations: It requires prealignment of the range surfaces to a reasonable starting point and it is not robust to outliers arising either from noise or low surface overlap. This paper proposes a new approach that avoids these problems. To that end, there are two key, novel contributions in this work: a new, hybrid genetic algorithm (GA) technique, including hillclimbing and parallel-migration, combined with a new, robust evaluation metric based on surface interpenetration. Up to now, interpenetration has been evaluated only qualitatively; we define the first quantitative measure for it. Because they search in a space of transformations, GAs are capable of registering surfaces even when there is low overlap between them and without need for prealignment. The novel GA search algorithm we present offers much faster convergence than prior GA methods, while the new robust evaluation metric ensures more precise alignments, even in the presence of significant noise, than mean squared error or other well-known robust cost functions. The paper presents thorough experimental results to show the improvements realized by these two contributions.

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Index Terms:
Range image registration, genetic algorithms, robust methods, stochastic search.
Citation:
Luciano Silva, Olga R.P. Bellon, Kim L. Boyer, "Precision Range Image Registration Using a Robust Surface Interpenetration Measure and Enhanced Genetic Algorithms," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 27, no. 5, pp. 762-776, May 2005, doi:10.1109/TPAMI.2005.108
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