All

What are you looking for?

All
Projects
Results
Organizations

Quick search

  • Projects supported by TA ČR
  • Excellent projects
  • Projects with the highest public support
  • Current projects

Smart search

  • That is how I find a specific +word
  • That is how I leave the -word out of the results
  • “That is how I can find the whole phrase”

Low-rank matrix approximations for Coherent point drift

The result's identifiers

  • Result code in 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>

  • Alternative codes found

    RIV/00216208:11310/15:10281328

  • Result on the web

    <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>

Alternative languages

  • Result language

    angličtina

  • Original language name

    Low-rank matrix approximations for Coherent point drift

  • Original language description

    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

  • Czech name

  • Czech description

Classification

  • Type

    J<sub>x</sub> - Unclassified - Peer-reviewed scientific article (Jimp, Jsc and Jost)

  • CEP classification

    IN - Informatics

  • OECD FORD branch

Result continuities

  • Project

  • Continuities

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

Others

  • Publication year

    2015

  • Confidentiality

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

Data specific for result type

  • Name of the periodical

    Pattern Recognition Letters

  • ISSN

    0167-8655

  • e-ISSN

  • Volume of the periodical

    2014

  • Issue of the periodical within the volume

    52

  • Country of publishing house

    NL - THE KINGDOM OF THE NETHERLANDS

  • Number of pages

    6

  • Pages from-to

    53-58

  • UT code for WoS article

    000345697400008

  • EID of the result in the Scopus database

    2-s2.0-84909957934