Imaging Components, Systems, and Processing

Consistency-based ellipse detection method for complicated images

[+] Author Affiliations
Lijun Zhang, Xuexiang Huang, Weichun Feng, Shuli Liang, Tianjian Hu

Beijing Institute of Tracking and Telecommunications Technology, No. 26, Beiqing Road, Haidian District, Beijing 100094, China

Opt. Eng. 55(5), 053105 (May 12, 2016). doi:10.1117/1.OE.55.5.053105
History: Received November 21, 2015; Accepted April 4, 2016
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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.

© 2016 Society of Photo-Optical Instrumentation Engineers

Citation

Lijun Zhang ; Xuexiang Huang ; Weichun Feng ; Shuli Liang and Tianjian Hu
"Consistency-based ellipse detection method for complicated images", Opt. Eng. 55(5), 053105 (May 12, 2016). ; http://dx.doi.org/10.1117/1.OE.55.5.053105


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