Evolution strategy sampling consensus for robust estimator

Yuichiro Toda, Naoyuki Kubota

Research output: Contribution to journalArticle

1 Citation (Scopus)

Abstract

RANdom SAmple Consensus (RANSAC) has been applied to many 3D image processing problems such as homography matrix estimation problems and shape detection from 3D point clouds, and is one of the most popular robust estimator methods. However, RANSAC has a problem related to the trade-off between computational cost and stability of search because RANSAC is based on random sampling. Genetic Algorithm SAmple Consensus (GASAC) based on a population-based multi-point search was proposed in order to improve RANSAC. GASAC can im-prove the performance of search. However, it is sometimes difficult to maintain the genetic diversity in the search if the large size of outliers is included in a data set. Furthermore, a computational time of GASAC sometimes is slower than that of RANSAC because of calculation of the genetic operators. This paper proposes Evolution Strategy SAmple Consensus (ESSAC) as a new robust estimator. ESSAC is based 011 Evolution Strategy in order to maintain the genetic diversity. In ESSAC, we apply two heuristic searches to ESSAC. One is a search range control, the other is adaptive/self-adaptive mutation. By applying these heuristic searches, the trade-off between computational speed and search stability can be improved. Finally, this paper shows several experimental results in order to evaluate the effectiveness of the proposed method.

Original languageEnglish
Pages (from-to)788-802
Number of pages15
JournalJournal of Advanced Computational Intelligence and Intelligent Informatics
Volume20
Issue number5
DOIs
Publication statusPublished - Sep 1 2016
Externally publishedYes

Fingerprint

Genetic algorithms
Sampling
Mathematical operators
Image processing
Costs

Keywords

  • Evolution strategy
  • Homography estimation
  • Random sampling consensus

ASJC Scopus subject areas

  • Human-Computer Interaction
  • Computer Vision and Pattern Recognition
  • Artificial Intelligence

Cite this

Evolution strategy sampling consensus for robust estimator. / Toda, Yuichiro; Kubota, Naoyuki.

In: Journal of Advanced Computational Intelligence and Intelligent Informatics, Vol. 20, No. 5, 01.09.2016, p. 788-802.

Research output: Contribution to journalArticle

@article{bca14631ca9f4054b84af8f516939875,
title = "Evolution strategy sampling consensus for robust estimator",
abstract = "RANdom SAmple Consensus (RANSAC) has been applied to many 3D image processing problems such as homography matrix estimation problems and shape detection from 3D point clouds, and is one of the most popular robust estimator methods. However, RANSAC has a problem related to the trade-off between computational cost and stability of search because RANSAC is based on random sampling. Genetic Algorithm SAmple Consensus (GASAC) based on a population-based multi-point search was proposed in order to improve RANSAC. GASAC can im-prove the performance of search. However, it is sometimes difficult to maintain the genetic diversity in the search if the large size of outliers is included in a data set. Furthermore, a computational time of GASAC sometimes is slower than that of RANSAC because of calculation of the genetic operators. This paper proposes Evolution Strategy SAmple Consensus (ESSAC) as a new robust estimator. ESSAC is based 011 Evolution Strategy in order to maintain the genetic diversity. In ESSAC, we apply two heuristic searches to ESSAC. One is a search range control, the other is adaptive/self-adaptive mutation. By applying these heuristic searches, the trade-off between computational speed and search stability can be improved. Finally, this paper shows several experimental results in order to evaluate the effectiveness of the proposed method.",
keywords = "Evolution strategy, Homography estimation, Random sampling consensus",
author = "Yuichiro Toda and Naoyuki Kubota",
year = "2016",
month = "9",
day = "1",
doi = "10.20965/jaciii.2016.p0788",
language = "English",
volume = "20",
pages = "788--802",
journal = "Journal of Advanced Computational Intelligence and Intelligent Informatics",
issn = "1343-0130",
publisher = "Fuji Technology Press",
number = "5",

}

TY - JOUR

T1 - Evolution strategy sampling consensus for robust estimator

AU - Toda, Yuichiro

AU - Kubota, Naoyuki

PY - 2016/9/1

Y1 - 2016/9/1

N2 - RANdom SAmple Consensus (RANSAC) has been applied to many 3D image processing problems such as homography matrix estimation problems and shape detection from 3D point clouds, and is one of the most popular robust estimator methods. However, RANSAC has a problem related to the trade-off between computational cost and stability of search because RANSAC is based on random sampling. Genetic Algorithm SAmple Consensus (GASAC) based on a population-based multi-point search was proposed in order to improve RANSAC. GASAC can im-prove the performance of search. However, it is sometimes difficult to maintain the genetic diversity in the search if the large size of outliers is included in a data set. Furthermore, a computational time of GASAC sometimes is slower than that of RANSAC because of calculation of the genetic operators. This paper proposes Evolution Strategy SAmple Consensus (ESSAC) as a new robust estimator. ESSAC is based 011 Evolution Strategy in order to maintain the genetic diversity. In ESSAC, we apply two heuristic searches to ESSAC. One is a search range control, the other is adaptive/self-adaptive mutation. By applying these heuristic searches, the trade-off between computational speed and search stability can be improved. Finally, this paper shows several experimental results in order to evaluate the effectiveness of the proposed method.

AB - RANdom SAmple Consensus (RANSAC) has been applied to many 3D image processing problems such as homography matrix estimation problems and shape detection from 3D point clouds, and is one of the most popular robust estimator methods. However, RANSAC has a problem related to the trade-off between computational cost and stability of search because RANSAC is based on random sampling. Genetic Algorithm SAmple Consensus (GASAC) based on a population-based multi-point search was proposed in order to improve RANSAC. GASAC can im-prove the performance of search. However, it is sometimes difficult to maintain the genetic diversity in the search if the large size of outliers is included in a data set. Furthermore, a computational time of GASAC sometimes is slower than that of RANSAC because of calculation of the genetic operators. This paper proposes Evolution Strategy SAmple Consensus (ESSAC) as a new robust estimator. ESSAC is based 011 Evolution Strategy in order to maintain the genetic diversity. In ESSAC, we apply two heuristic searches to ESSAC. One is a search range control, the other is adaptive/self-adaptive mutation. By applying these heuristic searches, the trade-off between computational speed and search stability can be improved. Finally, this paper shows several experimental results in order to evaluate the effectiveness of the proposed method.

KW - Evolution strategy

KW - Homography estimation

KW - Random sampling consensus

UR - http://www.scopus.com/inward/record.url?scp=84990841865&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84990841865&partnerID=8YFLogxK

U2 - 10.20965/jaciii.2016.p0788

DO - 10.20965/jaciii.2016.p0788

M3 - Article

AN - SCOPUS:84990841865

VL - 20

SP - 788

EP - 802

JO - Journal of Advanced Computational Intelligence and Intelligent Informatics

JF - Journal of Advanced Computational Intelligence and Intelligent Informatics

SN - 1343-0130

IS - 5

ER -