Share Email Print
cover

Optical Engineering

Consistency-based ellipse detection method for complicated images
Author(s): Lijun Zhang; Xuexiang Huang; Weichun Feng; Shuli Liang; Tianjian Hu
Format Member Price Non-Member Price
PDF $20.00 $25.00
cover GOOD NEWS! Your organization subscribes to the SPIE Digital Library. You may be able to download this paper for free. Check Access

Paper Abstract

Accurate ellipse detection in complicated images is a challenging problem due to corruptions from image clutter, noise, or occlusion of other objects. To cope with this problem, an edge-following-based ellipse detection method is proposed which promotes the performances of the subprocesses based on consistency. The ellipse detector models edge connectivity by line segments and exploits inconsistent endpoints of the line segments to split the edge contours into smooth arcs. The smooth arcs are further refined with a novel arc refinement method which iteratively improves the consistency degree of the smooth arc. A two-phase arc integration method is developed to group disconnected elliptical arcs belonging to the same ellipse, and two constraints based on consistency are defined to increase the effectiveness and speed of the merging process. Finally, an efficient ellipse validation method is proposed to evaluate the saliency of the elliptic hypotheses. Detailed evaluation on synthetic images shows that our method outperforms other state-of-the-art ellipse detection methods in terms of effectiveness and speed. Additionally, we test our detector on three challenging real-world datasets. The F-measure score and execution time of results demonstrate that our method is effective and fast in complicated images. Therefore, the proposed method is suitable for practical applications.

Paper Details

Date Published: 12 May 2016
PDF: 15 pages
Opt. Eng. 55(5) 053105 doi: 10.1117/1.OE.55.5.053105
Published in: Optical Engineering Volume 55, Issue 5
Show Author Affiliations
Lijun Zhang, Beijing Institute of Tracking and Telecommunication Technology (China)
Xuexiang Huang, Beijing Institute of Tracking and Telecommunication Technology (China)
Weichun Feng, Beijing Institute of Tracking and Telecommunication Technology (China)
Shuli Liang, Beijing Institute of Tracking and Telecommunication Technology (China)
Tianjian Hu, Beijing Institute of Tracking and Telecommunication Technology (China)


© SPIE. Terms of Use
Back to Top