Behavior analysis of Evolution Strategy Sample Consensus

Yuichiro Toda; Naoyuki Kubota

doi:10.1109/MECATRONICS.2014.7018611

Behavior analysis of Evolution Strategy Sample Consensus

Yuichiro Toda, Naoyuki Kubota

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

1 Citation (Scopus)

Abstract

Recently, robust estimators are expected in various fields such as signal processing and machine learning. In our previous work, we proposed Evolution Strategy Sample Consensus (ESSAC) as a new robust estimator method and improved a trade off between a calculation time and stability of SAmple Consensus (SAC) algorithms. In this paper, we show several experiments for behavior analysis of ESSAC in order to discuss why ESSAC enable to search stably in the dataset including the huge number of noises. and analyze several experiments related with the fitness function of SAC and the behavior of ESSAC.

Original language	English
Title of host publication	10th France-Japan Congress, 8th Europe-Asia Congress on Mecatronics, MECATRONICS 2014
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	250-255
Number of pages	6
ISBN (Electronic)	9781479957170
DOIs	https://doi.org/10.1109/MECATRONICS.2014.7018611
Publication status	Published - Jan 22 2014
Externally published	Yes
Event	10th France-Japan/8th Europe-Asia Congress on Mecatronics, MECATRONICS 2014 - Tokyo, Japan Duration: Nov 27 2014 → Nov 29 2014

Publication series

Name	10th France-Japan Congress, 8th Europe-Asia Congress on Mecatronics, MECATRONICS 2014

Other

Other	10th France-Japan/8th Europe-Asia Congress on Mecatronics, MECATRONICS 2014
Country/Territory	Japan
City	Tokyo
Period	11/27/14 → 11/29/14

Keywords

Evolutionary Computation
Homography Estimation Problem
Robust Estimator

ASJC Scopus subject areas

Electrical and Electronic Engineering
Mechanical Engineering

Access to Document

10.1109/MECATRONICS.2014.7018611

Cite this

Toda, Y., & Kubota, N. (2014). Behavior analysis of Evolution Strategy Sample Consensus. In 10th France-Japan Congress, 8th Europe-Asia Congress on Mecatronics, MECATRONICS 2014 (pp. 250-255). Article 7018611 (10th France-Japan Congress, 8th Europe-Asia Congress on Mecatronics, MECATRONICS 2014). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/MECATRONICS.2014.7018611

Behavior analysis of Evolution Strategy Sample Consensus. / Toda, Yuichiro; Kubota, Naoyuki.
10th France-Japan Congress, 8th Europe-Asia Congress on Mecatronics, MECATRONICS 2014. Institute of Electrical and Electronics Engineers Inc., 2014. p. 250-255 7018611 (10th France-Japan Congress, 8th Europe-Asia Congress on Mecatronics, MECATRONICS 2014).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Toda, Y & Kubota, N 2014, Behavior analysis of Evolution Strategy Sample Consensus. in 10th France-Japan Congress, 8th Europe-Asia Congress on Mecatronics, MECATRONICS 2014., 7018611, 10th France-Japan Congress, 8th Europe-Asia Congress on Mecatronics, MECATRONICS 2014, Institute of Electrical and Electronics Engineers Inc., pp. 250-255, 10th France-Japan/8th Europe-Asia Congress on Mecatronics, MECATRONICS 2014, Tokyo, Japan, 11/27/14. https://doi.org/10.1109/MECATRONICS.2014.7018611

Toda Y, Kubota N. Behavior analysis of Evolution Strategy Sample Consensus. In 10th France-Japan Congress, 8th Europe-Asia Congress on Mecatronics, MECATRONICS 2014. Institute of Electrical and Electronics Engineers Inc. 2014. p. 250-255. 7018611. (10th France-Japan Congress, 8th Europe-Asia Congress on Mecatronics, MECATRONICS 2014). doi: 10.1109/MECATRONICS.2014.7018611

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abstract = "Recently, robust estimators are expected in various fields such as signal processing and machine learning. In our previous work, we proposed Evolution Strategy Sample Consensus (ESSAC) as a new robust estimator method and improved a trade off between a calculation time and stability of SAmple Consensus (SAC) algorithms. In this paper, we show several experiments for behavior analysis of ESSAC in order to discuss why ESSAC enable to search stably in the dataset including the huge number of noises. and analyze several experiments related with the fitness function of SAC and the behavior of ESSAC.",

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Behavior analysis of Evolution Strategy Sample Consensus

Abstract

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Keywords

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