Ellipse detection with hard c-regression models and random initializations

Author

Ichihashi, Hidetomo ; Lam, Li Chieu ; Honda, Katsuhiro ; Notsu, Akira

Author_Institution

Dept. of Comput. Sci. & Intell. Syst., Osaka Prefecture Univ., Sakai, Japan

fYear

2011

fDate

27-30 June 2011

Firstpage

394

Lastpage

400

Abstract

Shell clustering methods partition data sets into several shell-shape clusters by extracting local circles or ellipses as prototypes of clusters. This paper proposes hard c regression models (HCRMs) for shell clustering. The procedure is a defuzzified switching regression models. HCRMs successfully detect ellipses by using random initializations. We report the performance using 20 data sets each of which consists of two ellipses. The detection time on average is 14 milliseconds on DELL PRECISION T5400 3.16GHz.

Keywords

edge detection; pattern clustering; random processes; regression analysis; DELL PRECISION T5400; HCRM; cluster prototypes; defuzzified switching regression models; ellipse detection; ellipses; hard c-regression models; local circles; random initializations; shell clustering methods partition data sets; shell-shape clusters; Computational modeling; Feature extraction; Image edge detection; Mathematical model; Prototypes; Switches; Transforms; clustering; ellipse detection; random initializations; switching regression;

fLanguage

English

Publisher

ieee

Conference_Titel

Fuzzy Systems (FUZZ), 2011 IEEE International Conference on

Conference_Location

Taipei

ISSN

1098-7584

Print_ISBN

978-1-4244-7315-1

Electronic_ISBN

1098-7584

Type

conf

DOI

10.1109/FUZZY.2011.6007377

Filename

6007377