Progressive-X: Efficient, Anytime, Multi-Model Fitting Algorithm
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21230%2F19%3A00337388" target="_blank" >RIV/68407700:21230/19:00337388 - isvavai.cz</a>
Result on the web
<a href="https://doi.org/10.1109/ICCV.2019.00388" target="_blank" >https://doi.org/10.1109/ICCV.2019.00388</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1109/ICCV.2019.00388" target="_blank" >10.1109/ICCV.2019.00388</a>
Alternative languages
Result language
angličtina
Original language name
Progressive-X: Efficient, Anytime, Multi-Model Fitting Algorithm
Original language description
The Progressive-X algorithm, Prog-X in short, is proposed for geometric multi-model fitting. The method interleaves sampling and consolidation of the current data interpretation via repetitive hypothesis proposal, fast rejection, and integration of the new hypothesis into the kept instance set by labeling energy minimization. Due to exploring the data progressively, the method has several beneficial properties compared with the state-of-the-art. First, a clear criterion, adopted from RANSAC, controls the termination and stops the algorithm when the probability of finding a new model with a reasonable number of inliers falls below a threshold. Second, Prog-X is an any-time algorithm. Thus, whenever is interrupted, e.g. due to a time limit, the returned instances cover real and, likely, the most dominant ones. The method is superior to the state-of-the-art in terms of accuracy in both synthetic experiments and on publicly available real-world datasets for homography, two-view motion, and motion segmentation.
Czech name
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Czech description
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Classification
Type
D - Article in proceedings
CEP classification
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OECD FORD branch
10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)
Result continuities
Project
Result was created during the realization of more than one project. More information in the Projects tab.
Continuities
P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)
Others
Publication year
2019
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Article name in the collection
2019 IEEE International Conference on Computer Vision (ICCV 2019)
ISBN
978-1-7281-4804-5
ISSN
1550-5499
e-ISSN
2380-7504
Number of pages
9
Pages from-to
3779-3787
Publisher name
IEEE Computer Society Press
Place of publication
Los Alamitos
Event location
Seoul
Event date
Oct 27, 2019
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
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