An occlusion-resistant ellipse detection method by joining coelliptic arcs

Halil Ibrahim Cakir; Cihan Topal; Cuneyt Akinlar

doi:10.1007/978-3-319-46475-6_31

An occlusion-resistant ellipse detection method by joining coelliptic arcs

Halil Ibrahim Cakir^*, Cihan Topal, Cuneyt Akinlar

^*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

7 Citations (Scopus)

Abstract

In this study, we propose an ellipse detection method which gives prospering results on occlusive cases. The method starts with detection of edge segments. Then we extract elliptical arcs by computing corners and fitting ellipse to the pixels between two consecutive corners. Once the elliptical arcs are extracted, we aim to test all possible arc subsets. However, this requires exponential complexity and runtime diverges as the number of arcs increases. To accelerate the process, arc pairing strategy is deployed by using conic properties of arcs. If any pair found to be non-coelliptic, then arc combinations including that pair are eliminated. Therefore the number of possible arcs subsets is reduced and computation time is improved. In the end, ellipse fitting is applied to remaining arc combinations to decide on final ellipses. Performance of the proposed algorithm is tested on real datasets, and better results have been obtained compare to state-of-the-art algorithms.

Original language	English
Title of host publication	Computer Vision - 14th European Conference, ECCV 2016, Proceedings
Editors	Bastian Leibe, Nicu Sebe, Max Welling, Jiri Matas
Publisher	Springer Verlag
Pages	492-507
Number of pages	16
ISBN (Print)	9783319464749
DOIs	https://doi.org/10.1007/978-3-319-46475-6_31
Publication status	Published - 2016
Externally published	Yes
Event	14th European Conference on Computer Vision, ECCV 2016 - Amsterdam, Netherlands Duration: 8 Oct 2016 → 16 Oct 2016

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	9906 LNCS
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	14th European Conference on Computer Vision, ECCV 2016
Country/Territory	Netherlands
City	Amsterdam
Period	8/10/16 → 16/10/16

Bibliographical note

Publisher Copyright:
© Springer International Publishing AG 2016.

Keywords

Arc detection
Ellipse detection
Feature extraction
Hough transform

Access to Document

10.1007/978-3-319-46475-6_31

Cite this

Cakir, H. I., Topal, C., & Akinlar, C. (2016). An occlusion-resistant ellipse detection method by joining coelliptic arcs. In B. Leibe, N. Sebe, M. Welling, & J. Matas (Eds.), Computer Vision - 14th European Conference, ECCV 2016, Proceedings (pp. 492-507). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 9906 LNCS). Springer Verlag. https://doi.org/10.1007/978-3-319-46475-6_31

Cakir, Halil Ibrahim ; Topal, Cihan ; Akinlar, Cuneyt. / An occlusion-resistant ellipse detection method by joining coelliptic arcs. Computer Vision - 14th European Conference, ECCV 2016, Proceedings. editor / Bastian Leibe ; Nicu Sebe ; Max Welling ; Jiri Matas. Springer Verlag, 2016. pp. 492-507 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

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title = "An occlusion-resistant ellipse detection method by joining coelliptic arcs",

abstract = "In this study, we propose an ellipse detection method which gives prospering results on occlusive cases. The method starts with detection of edge segments. Then we extract elliptical arcs by computing corners and fitting ellipse to the pixels between two consecutive corners. Once the elliptical arcs are extracted, we aim to test all possible arc subsets. However, this requires exponential complexity and runtime diverges as the number of arcs increases. To accelerate the process, arc pairing strategy is deployed by using conic properties of arcs. If any pair found to be non-coelliptic, then arc combinations including that pair are eliminated. Therefore the number of possible arcs subsets is reduced and computation time is improved. In the end, ellipse fitting is applied to remaining arc combinations to decide on final ellipses. Performance of the proposed algorithm is tested on real datasets, and better results have been obtained compare to state-of-the-art algorithms.",

keywords = "Arc detection, Ellipse detection, Feature extraction, Hough transform",

author = "Cakir, {Halil Ibrahim} and Cihan Topal and Cuneyt Akinlar",

note = "Publisher Copyright: {\textcopyright} Springer International Publishing AG 2016.; 14th European Conference on Computer Vision, ECCV 2016 ; Conference date: 08-10-2016 Through 16-10-2016",

year = "2016",

doi = "10.1007/978-3-319-46475-6_31",

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series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",

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Cakir, HI, Topal, C & Akinlar, C 2016, An occlusion-resistant ellipse detection method by joining coelliptic arcs. in B Leibe, N Sebe, M Welling & J Matas (eds), Computer Vision - 14th European Conference, ECCV 2016, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 9906 LNCS, Springer Verlag, pp. 492-507, 14th European Conference on Computer Vision, ECCV 2016, Amsterdam, Netherlands, 8/10/16. https://doi.org/10.1007/978-3-319-46475-6_31

An occlusion-resistant ellipse detection method by joining coelliptic arcs. / Cakir, Halil Ibrahim; Topal, Cihan; Akinlar, Cuneyt.
Computer Vision - 14th European Conference, ECCV 2016, Proceedings. ed. / Bastian Leibe; Nicu Sebe; Max Welling; Jiri Matas. Springer Verlag, 2016. p. 492-507 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 9906 LNCS).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - An occlusion-resistant ellipse detection method by joining coelliptic arcs

AU - Cakir, Halil Ibrahim

AU - Topal, Cihan

AU - Akinlar, Cuneyt

N1 - Publisher Copyright: © Springer International Publishing AG 2016.

PY - 2016

Y1 - 2016

N2 - In this study, we propose an ellipse detection method which gives prospering results on occlusive cases. The method starts with detection of edge segments. Then we extract elliptical arcs by computing corners and fitting ellipse to the pixels between two consecutive corners. Once the elliptical arcs are extracted, we aim to test all possible arc subsets. However, this requires exponential complexity and runtime diverges as the number of arcs increases. To accelerate the process, arc pairing strategy is deployed by using conic properties of arcs. If any pair found to be non-coelliptic, then arc combinations including that pair are eliminated. Therefore the number of possible arcs subsets is reduced and computation time is improved. In the end, ellipse fitting is applied to remaining arc combinations to decide on final ellipses. Performance of the proposed algorithm is tested on real datasets, and better results have been obtained compare to state-of-the-art algorithms.

AB - In this study, we propose an ellipse detection method which gives prospering results on occlusive cases. The method starts with detection of edge segments. Then we extract elliptical arcs by computing corners and fitting ellipse to the pixels between two consecutive corners. Once the elliptical arcs are extracted, we aim to test all possible arc subsets. However, this requires exponential complexity and runtime diverges as the number of arcs increases. To accelerate the process, arc pairing strategy is deployed by using conic properties of arcs. If any pair found to be non-coelliptic, then arc combinations including that pair are eliminated. Therefore the number of possible arcs subsets is reduced and computation time is improved. In the end, ellipse fitting is applied to remaining arc combinations to decide on final ellipses. Performance of the proposed algorithm is tested on real datasets, and better results have been obtained compare to state-of-the-art algorithms.

KW - Arc detection

KW - Ellipse detection

KW - Feature extraction

KW - Hough transform

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M3 - Conference contribution

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SN - 9783319464749

T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

SP - 492

EP - 507

BT - Computer Vision - 14th European Conference, ECCV 2016, Proceedings

A2 - Leibe, Bastian

A2 - Sebe, Nicu

A2 - Welling, Max

A2 - Matas, Jiri

PB - Springer Verlag

T2 - 14th European Conference on Computer Vision, ECCV 2016

Y2 - 8 October 2016 through 16 October 2016

ER -

Cakir HI, Topal C, Akinlar C. An occlusion-resistant ellipse detection method by joining coelliptic arcs. In Leibe B, Sebe N, Welling M, Matas J, editors, Computer Vision - 14th European Conference, ECCV 2016, Proceedings. Springer Verlag. 2016. p. 492-507. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-319-46475-6_31

An occlusion-resistant ellipse detection method by joining coelliptic arcs

Abstract

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Bibliographical note

Keywords

Access to Document

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