An Occlusion-Resistant Ellipse Detection Method by Joining Coelliptic Arcs
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
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Dumlupinar University, Kutahya, Turkey [email protected] 2 Anadolu University, Eskisehir, Turkey {cihant,cakinlar}@anadolu.edu.tr 3 Visea Innovative, Eskisehir, Turkey
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: Ellipse detection Hough transform
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Arc detection
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Feature extraction
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Introduction
Extracting ellipses from images is an important problem in computer vision and has a diverse area of applications from object detection to pose estimation [3,10, 11,15–17,26]. Since the projections of circular objects appear as ellipse on the camera image plane, ellipse detection is employed in many real life applications. However, ellipse detection is much more difficult than circle detection because an ellipse has 5 degrees of freedom (the center coordinates x & y, semi-major and semi-minor axes a & b, and rotation angle α) whereas a circle has 3. Due to the same reason, many different shapes (i.e., a rectangular or a line) can be represented by an ellipse with a reasonable amount of accuracy. This work is supported by The Scientific and Technological Research Council of Turkey (TUBITAK) and Anadolu University Commission of Scientific Research Projects (BAP) under the grant numbers 115E928 and 1505F319, respectively. c Springer International Publishing AG 2016 B. Leibe et al. (Eds.): ECCV 2016, Part II, LNCS 9906, pp. 492–507, 2016. DOI: 10.1007/978-3-319-46475-6 31
An Occlusion-Resistant Ellipse Detection Method by Joining Coelliptic Arcs
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There are many studies on ellipse detection found in the literature and they are categorized in two groups, i.e. model-based and feature-based methods. Although both approaches have pros and cons, many state-of-the-art algorithms are feature-based methods and gives better results in terms of accuracy and speed. Model-based methods fits a mathematical model to plain pixel information. McLaughlin uses the famous Hough Transform (HT) for accurate ellipse detection [17]. A model-based search is a very slow operation for ellipse shape since it needs to be performed in 5-dimensional parameter space. Zhang and Liu utilize HT with
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