Low-rank matrix approximations for Coherent point drift

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216208%3A11320%2F15%3A10281328" target="_blank" >RIV/00216208:11320/15:10281328 - isvavai.cz</a>

Nalezeny alternativní kódy

RIV/00216208:11310/15:10281328

Výsledek na webu

<a href="http://www.sciencedirect.com/science/article/pii/S0167865514003122" target="_blank" >http://www.sciencedirect.com/science/article/pii/S0167865514003122</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1016/j.patrec.2014.10.005" target="_blank" >10.1016/j.patrec.2014.10.005</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Low-rank matrix approximations for Coherent point drift

Popis výsledku v původním jazyce

Coherent point drift (CPD) is a powerful non-rigid point cloud registration algorithm. A speed-up technique that allows it to operate on large sets in reasonable time, however depends on efficient low-rank decomposition of a large affinity matrix. The originally used algorithm for finding eigenvectors in this case is based on Arnoldi's iteration which, though very precise, requires the calculation of numerous large matrix-vector products, which even with further speed-up techniques is computationally intensive. We use a different method of finding that approximation, based on Nyström sampling and design a modification that significantly accelerates the preprocessing stage of CPD. We test our modifications on a variety of situations, including differentpoint counts, added Gaussian noise, outliers and deformation of the registered clouds. The results indicate that using our proposed approximation technique the desirable qualities of CPD such as robustness and precision are only minimall

Název v anglickém jazyce

Low-rank matrix approximations for Coherent point drift

Popis výsledku anglicky

Coherent point drift (CPD) is a powerful non-rigid point cloud registration algorithm. A speed-up technique that allows it to operate on large sets in reasonable time, however depends on efficient low-rank decomposition of a large affinity matrix. The originally used algorithm for finding eigenvectors in this case is based on Arnoldi's iteration which, though very precise, requires the calculation of numerous large matrix-vector products, which even with further speed-up techniques is computationally intensive. We use a different method of finding that approximation, based on Nyström sampling and design a modification that significantly accelerates the preprocessing stage of CPD. We test our modifications on a variety of situations, including differentpoint counts, added Gaussian noise, outliers and deformation of the registered clouds. The results indicate that using our proposed approximation technique the desirable qualities of CPD such as robustness and precision are only minimall

Druh

J_x - Nezařazeno - Článek v odborném periodiku (Jimp, Jsc a Jost)

CEP obor

IN - Informatika

OECD FORD obor

—

Projekt

—

Návaznosti

S - Specificky vyzkum na vysokych skolach<br>I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Rok uplatnění

2015

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název periodika

Pattern Recognition Letters

ISSN

0167-8655

e-ISSN

—

Svazek periodika

2014

Číslo periodika v rámci svazku

52

Stát vydavatele periodika

NL - Nizozemsko

Počet stran výsledku

6

Strana od-do

53-58

Kód UT WoS článku

000345697400008

EID výsledku v databázi Scopus

2-s2.0-84909957934