A fast ellipse/circle detector using geometric symmetry

Ho, Chun-Ta; Chen, Ling‐Hwei

doi:10.1016/0031-3203(94)00077-y

Cited by 163 publications

(51 citation statements)

References 6 publications

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“…Some researchers have provided methods for elliptical object detection using geometrical properties [8,18] and the Hough transform [19]. A working environment for this study is electronic catalogs, which include objects like pans, pots, cups, and plates.…”

Section: The Proposed Methodsmentioning

confidence: 99%

Partial Object Recognition based on Ellipse of Objects using Symmetry in Image Databases

Cho¹

2008

The KIPS Transactions:PartB

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This paper discusses the problem of partial object recognition in image databases. We propose the method to reconstruct and estimate partially occluded shapes and regions of objects in images from overlapping and cutting. We present the robust method for recognizing partially occluded objects based on symmetry properties, which is based on an ellipse of objects. Our method provides simple techniques to reconstruct occluded regions via a region copy using the symmetry axis within an object. Since our method relies on reconstruction of the object based on the symmetry rather than statistical estimates, it has proven to be remarkably robust in recognizing partially occluded objects in the presence of scale changes, rotation, and viewpoint changes.

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Section: The Proposed Methodsmentioning

confidence: 99%

Partial Object Recognition based on Ellipse of Objects using Symmetry in Image Databases

Cho¹

2008

The KIPS Transactions:PartB

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“…Besides the use of edge orientation as that in GHT, these major modifications include: the simultaneous consideration of a range of circle radii [12], [14], [15], use of a complex accumulator array with radius modulated phase [15]- [17], incorporation of different sampling strategies [18]- [21], exploration of the symmetry [3], [22], [23], adoption of the hierarchical structure [24], and the implementation of the circle detection as filtering operations [17]. It is worth noting that these modifications are not isolated, in fact, they usually work in cooperation.…”

Section: Circle Detectionmentioning

confidence: 99%

“…To explore the symmetry avoids the voting stage required by the Hough transform and improves the efficiency. It can be directly generalized to ellipse detection [3], [22], [23].…”

Section: Exploring the Symmetrymentioning

confidence: 99%

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Circle Detection Based on Voting for Maximum Compatibility

Liu

Huang

2012

IEICE Trans. Inf. & Syst.

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SUMMARYWe present a fast voting scheme for localizing circular objects among clutter and occlusion. Typical solutions for the problem are based on Hough transform that evaluates an instance of circle by counting the number of edge points along its boundary. The evaluated value is proportional to radius, making the normalization with respect to the factor necessary for detecting circles with different radii. By representing circle with a number of sampled points, we get rid of the step. To evaluate an instance then involves obtaining the same number of edge points, each close to a sampled point in both spatial position and orientation. The closeness is measured by compatibility function, where a truncating operation is used to suppress noise and deal with occlusion. To evaluate all instances of circle is fulfilled by letting edge point vote in a maximizing way such that any instance possesses a set of maximally compatible edge points. The voting process is further separated into the radius-independent anddependent parts. The time-consuming independent part can be shared by different radii and outputs the sparse matrices. The radius-dependent part shifts these sparse matrices according to the radius. We present precisionrecall curves showing that the proposed approach outperforms the solutions based on Hough transform, at the same time, achieves the comparable time complexity as algorithm of Hough transform using 2D accumulator array.

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“…An ellipse has five parameters, so it requires a five-dimensional parameter space, but in our case, we reduce the parameter space to compute only the center-two parameters-of the ellipses. Some techniques (like those in [1,8,9,16,13]) have appeared to overcome the excessive computational cost and space requirements needed. They normally decompose the five-dimensional parameter space into several subspaces of fewer dimensions.…”

Section: Introductionmentioning

confidence: 99%

Robust detection and ordering of ellipses on a calibration pattern

Álvarez

Salgado

Sánchez

2007

Pattern Recognit. Image Anal.

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-The aim of this work is to accurately estimate from an image the parameters of some ellipses and their relative positions with respect to a given pattern. The process is characterized because it is fully automated and is robust against image noise and occlusions. We have built a calibrator pattern with two planes each containing several ordered circles in known 3D positions. Our method is able to estimate the position of every ellipse and to put them into correspondence with the original calibrator circles.

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A fast ellipse/circle detector using geometric symmetry

Cited by 163 publications

References 6 publications

Partial Object Recognition based on Ellipse of Objects using Symmetry in Image Databases

Partial Object Recognition based on Ellipse of Objects using Symmetry in Image Databases

Circle Detection Based on Voting for Maximum Compatibility

Robust detection and ordering of ellipses on a calibration pattern

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