Improving RANSAC for Efficient and Precise Model Fitting with Statistical Analysis

Zhang, Y; Tian, H; Deng, Y; Wang, J

Improving RANSAC for Efficient and Precise Model Fitting with Statistical Analysis

Zhang, Y Tian, H Deng, Y Wang, J

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Publisher:: European Open Access Publishing (Europa Publishing)
Publication Type:: Journal Article
Citation:: European Journal of Electrical Engineering and Computer Science, 3 (5)

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Field	Value	Language
dc.contributor.author	Zhang, Y	en_US
dc.contributor.author	Tian, H	en_US
dc.contributor.author	Deng, Y	en_US
dc.contributor.author	Wang, J https://orcid.org/0000-0002-6282-0456	en_US
dc.date.accessioned	2020-03-19T07:46:41Z
dc.date.available	2020-03-19T07:46:41Z
dc.identifier.citation	European Journal of Electrical Engineering and Computer Science, 3 (5)	en_US
dc.identifier.uri	http://hdl.handle.net/10453/139418
dc.description.abstract	<jats:p>RANSAC (random sample consensus) has been widely used as a benchmark algorithm for model fitting in the presence of outliers for more than thirty years. It is robust for outlier removal and rough model fitting, but neither reliable nor efficient enough for many applications where precision and time is critical. Many other algorithms have been proposed for the improvement of RANSAC. However, no much effort has been done to systematically tackle its limitations on model fitting repeatability, quality indication, iteration termination, and multi-model fitting.A new paradigm, named as SASAC (statistical analysis for sample consensus), is introduced in this paper to relinquish the limitations of RANSAC above. Unlike RANSAC that does not consider sampling noise, which is true in most sampling cases, a term named as ? rate is defined in SASAC. It is used both as an indicator for the quality of model fitting and as a criterion for terminating iterative model searching. Iterative least square is advisably integrated in SASAC for optimal model estimation, and a strategy is proposed to handle a multi-model situation.Experiment results for linear and quadratic function model fitting demonstrate that SASAC can significantly improve the quality and reliability of model fitting and largely reduce the number of iterations for model searching. Using the ? rate as an indicator for the quality of model fitting can effectively avoid wrongly estimated model. In addition, SASAC works very well to a multi-model dataset and can provide reliable estimations to all the models. SASAC can be combined with RANSAC and its variants to dramatically improve their performance.</jats:p>	en_US
dc.publisher	European Open Access Publishing (Europa Publishing)	en_US
dc.relation.ispartof	European Journal of Electrical Engineering and Computer Science	en_US
dc.relation.isbasedon	10.24018/ejece.2019.3.5.111	en_US
dc.title	Improving RANSAC for Efficient and Precise Model Fitting with Statistical Analysis	en_US
dc.type	Journal Article
utslib.citation.volume	5	en_US
utslib.citation.volume	3	en_US
pubs.embargo.period	Not known	en_US
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Civil and Environmental Engineering
pubs.organisational-group	/University of Technology Sydney/Strength - CBI - Centre for Built Infrastructure
utslib.copyright.status	open_access
pubs.issue	5	en_US
pubs.publication-status	Published online	en_US
pubs.volume	3	en_US

Abstract:

RANSAC (random sample consensus) has been widely used as a benchmark algorithm for model fitting in the presence of outliers for more than thirty years. It is robust for outlier removal and rough model fitting, but neither reliable nor efficient enough for many applications where precision and time is critical. Many other algorithms have been proposed for the improvement of RANSAC. However, no much effort has been done to systematically tackle its limitations on model fitting repeatability, quality indication, iteration termination, and multi-model fitting.A new paradigm, named as SASAC (statistical analysis for sample consensus), is introduced in this paper to relinquish the limitations of RANSAC above. Unlike RANSAC that does not consider sampling noise, which is true in most sampling cases, a term named as ? rate is defined in SASAC. It is used both as an indicator for the quality of model fitting and as a criterion for terminating iterative model searching. Iterative least square is advisably integrated in SASAC for optimal model estimation, and a strategy is proposed to handle a multi-model situation.Experiment results for linear and quadratic function model fitting demonstrate that SASAC can significantly improve the quality and reliability of model fitting and largely reduce the number of iterations for model searching. Using the ? rate as an indicator for the quality of model fitting can effectively avoid wrongly estimated model. In addition, SASAC works very well to a multi-model dataset and can provide reliable estimations to all the models. SASAC can be combined with RANSAC and its variants to dramatically improve their performance.

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http://hdl.handle.net/10453/139418