A statistically efficient method for ellipse detection

Author

Qiang Ji ; Haralick, Robert M.

Author_Institution

Dept. of Comput. Sci., Nevada Univ., NV, USA

Volume

2

fYear

1999

Firstpage

730

Abstract

In this paper, we introduce a statistically efficient method for detecting ellipses in an image. Given a set of digital arc segments, we introduce geometric criteria to select possible pairs of arc segments belonging to the same ellipse. The selected arc pairs are subsequently validated or rejected based on certain statistical criteria via hypothesis testing. The advantages of the technique include: 1) the proposed criteria are scale-invariant; and 2) they can automatically adapt to the noise characteristics of each image and do not need to be adjusted empirically. Performance evaluation of the technique with real images demonstrates its good performance.

Keywords

edge detection; feature extraction; image segmentation; statistical analysis; computer vision; connected edgels; digital arc segments; ellipse detection; geometric criteria; hypothesis testing; noise characteristics; real images; scale-invariant criteria; statistically efficient method; Clustering algorithms; Computer science; Computer vision; Image edge detection; Image reconstruction; Image segmentation; Joining processes; Machine vision; Parameter estimation; Testing;

fLanguage

English

Publisher

ieee

Conference_Titel

Image Processing, 1999. ICIP 99. Proceedings. 1999 International Conference on

Conference_Location

Kobe

Print_ISBN

0-7803-5467-2

Type

conf

DOI

10.1109/ICIP.1999.822992

Filename

822992