A survey on ellipse detection methods

Wong, Chin Yeow; Lin, Stephen; Ren, Tian-Ao; Kwok, Ngaiming

doi:10.1109/isie.2012.6237243

Cited by 27 publications

(13 citation statements)

References 49 publications

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“…Once the marker lines have been detected a approach is used to locate the outline of the frame: the Canny filter edges that were n marker lines are used to perform a robust ell Numerous ellipse detection algorith proposed in the literature [12]. All have difficulty that a general ellipse has 5 deg which makes the search space quite large ( fitting a circle, where there are only 3 degr Typically extra constraints are imposed to r space and make the computation more feasi…”

Section: Ball Outline Detectionmentioning

confidence: 99%

Calibration of an industrial vision system using an ellipsoid

Heather¹

2014

2014 IEEE International Conference on Image Processing (ICIP)

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A robust multi-camera calibration algorithm developed for an industrial vision system is described. An ellipsoid with a simple surface pattern and accurately known geometry is used as a calibration target. Our algorithm automatically detects the presence of the ball on the conveyor and accurately determines the position of its outline and marker lines in each image frame using efficient image processing techniques. A fast least-squares minimization is then performed to determine the optimal camera and motion parameters. The method is fully automatic and requires no human interaction or guidance, helping to minimize machine setup and maintenance times. The calibration algorithm has been demonstrated on real image captures and performance is quantified using simulated image sequences.

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Section: Ball Outline Detectionmentioning

confidence: 99%

Calibration of an industrial vision system using an ellipsoid

Heather¹

2014

2014 IEEE International Conference on Image Processing (ICIP)

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“…A large number of methods (e.g. Hough transforms [9,10]) has already been proposed to detect ellipses in images [11]. However their extension to 3D, though possible, are usually computationally expensive mainly because of the number of parameters to estimate (9 for a 3D ellipsoid).…”

Section: Kidney Detection Via Robust Ellipsoid Estimationmentioning

confidence: 99%

Kidney Detection and Segmentation in Contrast-Enhanced Ultrasound 3D Images

Prevost

Mory

Cuingnet

et al. 2013

Abdomen and Thoracic Imaging

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Contrast-enhanced ultrasound (CEUS) imaging has lately benefited of an increasing interest for diagnosis and intervention planning, as it allows to visualize blood flow in real-time harmlessly for the patient. It complements thus the anatomical information provided by conventional ultrasound (US). This chapter is dedicated to kidney segmentation methods in 3D CEUS images. First we present a generic and fast two-step approach to locate (via a robust ellipsoid estimation algortihm) and segment (using a template deformation framework) the kidney automatically. Then we show how user interactions can be integrated within the algorithm to guide or correct the segmentation in real time. Finally, we develop a co-segmentation framework that generalizes the aforementioned method and allows the simultaneous use of multiple images (here the CEUS and the US images) to improve the segmentation result. The different approaches are evaluated on a clinical database of 64 volumes.

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“…This method performs very well both in synthetic as well as in real-world images. However, it is computationally very expensive (Wong & Lin 2012).…”

Section: Introductionmentioning

confidence: 99%

Conics Detection Method based on Pascal’s Theorem

Salehin¹,

Zheng²,

Gao³

2015

Proceedings of the 10th International Conference on Computer Vision Theory and Applications

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This paper presents a novel conics detection method that can be applied for real images. The existing methods usually detect either circular or elliptical, or parabolic shape at one operation. Most of them need the information about center, radius, major axis, minor axis, vertex, and more. In our proposed method, the tangents on curve segments, conic parts, and conics are constructed using Pascal's theorem. The conic parts can be used to detect different types of conic sections from an image. The performance of the proposed method has been tested on the sample images selected from Caltech-256 database and various types of conic sections can be identified from the real images compared to other method. 491Salehin M., Zheng L. and Gao J.. Conics Detection Method based on Pascal's Theorem.

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A survey on ellipse detection methods

Cited by 27 publications

References 49 publications

Calibration of an industrial vision system using an ellipsoid

Calibration of an industrial vision system using an ellipsoid

Kidney Detection and Segmentation in Contrast-Enhanced Ultrasound 3D Images

Conics Detection Method based on Pascal’s Theorem

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