Effective ellipse detector with polygonal curve and likelihood ratio test

Lu, Tingting; Hu, Weiduo; Liu, Chang; Yang, Daguang

doi:10.1049/iet-cvi.2014.0347

Cited by 10 publications

(3 citation statements)

References 31 publications

(112 reference statements)

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“…Their method is able to handle incomplete ellipses, but fails to detect ellipses splitting into many short arcs. Some other works formulate the mergence of elliptical arcs as an assignment problem, and iteratively correct the detection results [16], [17], [18]. These methods have high detection rates, but suffer from heavy computational costs.…”

Section: Introductionmentioning

confidence: 99%

A Fast Ellipse Detector Using Projective Invariant Pruning

Jia

Fan

Luo

et al. 2017

IEEE Trans. on Image Process.

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Detecting elliptical objects from an image is a central task in robot navigation and industrial diagnosis, where the detection time is always a critical issue. Existing methods are hardly applicable to these real-time scenarios of limited hardware resource due to the huge number of fragment candidates (edges or arcs) for fitting ellipse equations. In this paper, we present a fast algorithm detecting ellipses with high accuracy. The algorithm leverages a newly developed projective invariant to significantly prune the undesired candidates and to pick out elliptical ones. The invariant is able to reflect the intrinsic geometry of a planar curve, giving the value of -1 on any three collinear points and +1 for any six points on an ellipse. Thus, we apply the pruning and picking by simply comparing these binary values. Moreover, the calculation of the invariant only involves the determinant of a 3×3 matrix. Extensive experiments on three challenging data sets with 648 images demonstrate that our detector runs 20%-50% faster than the state-of-the-art algorithms with the comparable or higher precision.

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Section: Introductionmentioning

confidence: 99%

A Fast Ellipse Detector Using Projective Invariant Pruning

Jia

Fan

Luo

et al. 2017

IEEE Trans. on Image Process.

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“…The distance information is adopted to evaluates registration of a set of arcs [69,70]. Finally, a number of methods proposed by [35,49,71,72] have attempted to improve the detection accuracy with iterative approaches.…”

Section: Review Of Ellipse Detection Approachesmentioning

confidence: 99%

Real-Time Robotic Manipulation of Cylindrical Objects in Dynamic Scenarios Through Elliptic Shape Primitives

et al. 2019

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“…Although the computational efficiency of HT based method has been improved a lot by using the above modifications, it still runs very slowly. Besides, due to the difficulty in identifying true peaks in the parametric space, the accuracy of HT based methods in detecting ellipses rapidly deteriorates as the number of ellipses in an image increases [7,26] . Thus, it is not suitable to be used to detect ellipses in an image which contains more than one ellipses.…”

Section: Introductionmentioning

confidence: 99%