Title :
A statistically efficient method for ellipse detection
Author :
Qiang Ji ; Haralick, Robert M.
Author_Institution :
Dept. of Comput. Sci., Nevada Univ., NV, USA
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;
Conference_Titel :
Image Processing, 1999. ICIP 99. Proceedings. 1999 International Conference on
Conference_Location :
Kobe
Print_ISBN :
0-7803-5467-2
DOI :
10.1109/ICIP.1999.822992
