EDCircles: A real-time circle detector with a false detection control

Akınlar, Cüneyt; Topal, Cihan

doi:10.1016/j.patcog.2012.09.020

Cited by 151 publications

(95 citation statements)

References 42 publications

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“…Akinlar and Topal have proposed a robust circle detection method (Akinlar and Topal, 2013), named EDCircles. The ellipses features in camera images are obtained after postprocessing of EDCircles results ( Fig.…”

Section: Feature Extraction 21 Ellipses Extractionmentioning

confidence: 99%

Matching CAD Model and Image Features for Robot Navigation and Inspection of an Aircraft

Jovanźeviź

Viana

Orteu

et al. 2016

Proceedings of the 5th International Conference on Pattern Recognition Applications and Methods

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Abstract:This paper focuses on the navigation of a moving robot equipped with cameras, moving around an aircraft to perform inspection of different types of items (probes, doors, etc.). Matching CAD model and image features is useful to provide meaningful features for localization and inspection tasks. In our approach two primitive sets are matched using a similarity function. The similarity scores are injected in the edges of a bipartite graph. A best-match search procedure in bipartite graph guarantees the uniqueness of the match solution. The method provides good matching results even when the location of the robot with respect to the aircraft is badly estimated. Inspection approaches on static ports and air inlet vent are presented.

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Section: Feature Extraction 21 Ellipses Extractionmentioning

confidence: 99%

Matching CAD Model and Image Features for Robot Navigation and Inspection of an Aircraft

Jovanźeviź

Viana

Orteu

et al. 2016

Proceedings of the 5th International Conference on Pattern Recognition Applications and Methods

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show abstract

“…Akinlar et al [17] give a definition: under the condition that the premise is p, if the angle is less than ), the direction of the two straight lines is the same. In the experiments, in order to increase the robustness, we set this value to 25 , if the  of one point is more than nearby (taken the four points before the point and four points behind the point) average 25 , then it suggests that the edge direction has a great changes at that point, the both sides of the edge certainly does not belong to the same arc, then delete these point, make the edge off, to improve the occlusion resistance of the algorithm.…”

Section: Single Pixel Edge Trackingmentioning

confidence: 99%

Multiple Circles Detection in Complex Scenes

Chu¹,

Wang²,

Sun³

et al. 2016

IJMUE

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“…The same approach is used in the EDLines straight line calculator [1] but with the difference that the latter algorithm starts from an edge drawing output while the former is based on image level lines. Similar ideas were also proposed to detect circles and ellipses [20,2]. Level lines are employed to detect good continuations and image corners in [6].…”

Section: Introductionmentioning

confidence: 99%

Gestaltic Grouping of Line Segments

Rajaei¹,

Gioi²

2018

Image Processing on Line

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Using simple grouping rules in Gestalt theory, one may detect higher level features (geometric structures) in an image from elementary features. By recursive grouping of already detected geometric structures a bottom-up pyramid could be built that extracts increasingly complex geometric features from the input image. Taking advantage of the (recent) advances in reliable line segment detectors, in this paper, we propose three feature detectors along with their corresponding detailed algorithms that constitute one step up in this pyramid. For any digital image, our unsupervised algorithm computes three classic Gestalts from the set of predetected line segments: good continuations, non-local alignments, and bars. The methodology is based on a common stochastic a contrario model yielding three simple detection formulas, characterized by their number of false alarms. This detection algorithm is illustrated on several digital images. Source CodeThe Matlab source code implementing the proposed algorithms, which is part of this publication, is accessible at corresponding IPOL web page. 1

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EDCircles: A real-time circle detector with a false detection control

Cited by 151 publications

References 42 publications

Matching CAD Model and Image Features for Robot Navigation and Inspection of an Aircraft

Matching CAD Model and Image Features for Robot Navigation and Inspection of an Aircraft

Multiple Circles Detection in Complex Scenes

Gestaltic Grouping of Line Segments

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