Multiple circle detection based on center-based clustering

Scitovski, Rudolf; Marošević, Tomislav

doi:10.1016/j.patrec.2014.09.010

Cited by 29 publications

(12 citation statements)

References 25 publications

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“…A centroid is an average of all points in cluster or a medoids. It is the most representative point in a cluster and often the centre of a cluster [14].…”

Section: Centre Based Clusteringmentioning

confidence: 99%

Clustering Techniques in Data Mining For Improving Software Architecture: A Review

Kaur¹,

Kaur²

2016

IJCA

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Data mining is a set of problem solving skills, instructions and methods applied upon variety of domains to discover and create useful systems that are used to solve practical problems. Clustering technique defines classes and put objects which are related to them in one class on the other hand in classification objects are placed in predefined classes. There are many clustering techniques for the improvement of architecture which are discussed in this paper. This paper also gives comparative study of clustering techniques and addresses benefits and limitations of clustering techniques.

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“…A centroid is an average of all points in cluster or a medoids. It is the most representative point in a cluster and often the centre of a cluster [14].…”

Section: Centre Based Clusteringmentioning

confidence: 99%

Clustering Techniques in Data Mining For Improving Software Architecture: A Review

Kaur¹,

Kaur²

2016

IJCA

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“…ellipses, is not known in advance, various indexes could possibly be found that point to the most appropriate number of clusters in a partition (see e.g. [10,18]). …”

Section: Numerical Examplesmentioning

confidence: 99%

“…where D(a, E) is the distance from point a to ellipse E (an analogous definition for the circle-center is given in [13,18]). If the Mahalanobis distance-like function is used (see, e.g., [2,3,21]…”

Section: Introductionmentioning

confidence: 99%

Multiple ellipse fitting by center-based clustering

Marošević

Scitovski

2015

Croat. Oper. Res. Rev.

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Abstract. This paper deals with the multiple ellipse fitting problem based on a given set of data points in a plane. The presumption is that all data points are derived from k ellipses that should be fitted. The problem is solved by means of center-based clustering, where cluster centers are ellipses. If the Mahalanobis distance-like function is introduced in each cluster, then the cluster center is represented by the corresponding Mahalanobis circle-center. The distance from a point a ∈ R 2 to the Mahalanobis circle is based on the algebraic criterion. The well-known k-means algorithm has been adapted to search for a locally optimal partition of the Mahalanobis circle-centers. Several numerical examples are used to illustrate the proposed algorithm.

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“…Over the past years, many ellipse detection algorithms sprang up and were studied broadly to solve the ellipse detection problem, which can be briefly grouped into several categories: Hough transform method [21][22][23][24], leastsquare fitting method [25][26][27][28][29], clustering method [30][31][32][33][34]. Hough transform has been widely used for detecting geometric primitives such as line segment, circle and ellipse.…”

Section: Introductionmentioning

confidence: 99%

“…The leastsquare ellipse fitting method has high fitting accuracy, but only one ellipse can be fitted at a time, that is, the image informat ion should be classified and separated before. The general idea of ellipse detection with clustering method is that the pixels are extracted fro m the arc, then filtered and clustered, and finally the ellipse is fitted by the least-square method [22,[30][31][32][33][34]. Th is method can effectively deal with complex situations such as multip le ellipses, mutual occlusion of ellipses and partial defect of ellipses, which has attracted wide attention.…”

Section: Introductionmentioning

confidence: 99%

Dynamic Granularity Matrix Space Model Based High Robust Multi-Ellipse Center Extraction Method for Camera Calibration

Xue

2020

IEEE Access

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Ellipse center extract ion is the important basis of camera calib ration with circle arrays calibrat ion board, wh ich direct ly affects the measurement accuracy of mach ine vision system. A iming at the problem of high noise sensitivity of the most ellipse center extract ion methods, a high robust multi-ellipse center extraction method is proposed. The idea is to transform the problem of mult i-ellipse center extraction into the problems of ellipse sub-pixel edge segmentation and edge based ellipse center clustering. Firstly, the flowchart of the proposed method is introduced. Then, the theory of fuzzy quotient space is introduced to establish the dynamic granularity matrix space model, which describes image segmentation problem as the jump ing and transformat ion of the image by hierarchical structure. Finally, an adaptive multi-ellipse sub-pixel edge segmentation method and an optimal ellipse center ext raction method are proposed based on the dynamic granularity matrix space model. Experimental results sh ow that the proposed method has higher accuracy, robustness and lower camera calibration error. INDEX TERMS multi-ellipse center, dynamic granularity matrix space, sub-pixel edge segmentation, hierarchical structure, camera calibration.

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Multiple circle detection based on center-based clustering

Cited by 29 publications

References 25 publications

Clustering Techniques in Data Mining For Improving Software Architecture: A Review

Clustering Techniques in Data Mining For Improving Software Architecture: A Review

Multiple ellipse fitting by center-based clustering

Dynamic Granularity Matrix Space Model Based High Robust Multi-Ellipse Center Extraction Method for Camera Calibration

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