A Fast Automatic Ellipse Detector

Cooke, Tristrom

doi:10.1109/dicta.2010.102

Cited by 10 publications

(9 citation statements)

References 5 publications

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“…as Q 1 (1) Q 1 (2) , and the intersection of two lines as < Q 1 j) |i = 1, 2, 3; j = 1, 2} be six distinct points on a conic (ellipse) as shown in Fig. 3, and P 1 , P 2 and P 3 be three intersection points of the lines connecting some pairs of the six points on the conic:…”

Section: Characteristic Number On Six Points Of a Conicmentioning

confidence: 99%

“…I list the picking rules for valid combinations using CNC and coordinate constraints, respectively. The symbols e 1 1 and e 1 t denote the starting and end points of arc 1 . Figure 9 illustrates two examples of invalid three-arc combinations that form one ellipse, removed by the constraints in Tab.…”

Section: Arc Selectionmentioning

confidence: 99%

“…E LLIPSES are quite common in natural or artificial scenes. The detection of ellipses in a fast and reliable manner from real world images provides a powerful analysis tool for many computer vision applications such as wheels detection [1], biological cell division [2], and object segmentation for industrial applications [3]. Ellipse detection still remains unresolved as one of the classical tasks with long history.…”

Section: Introductionmentioning

confidence: 99%

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A Fast Ellipse Detector Using Projective Invariant Pruning

Jia

Fan

Luo

et al. 2017

IEEE Trans. on Image Process.

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Detecting elliptical objects from an image is a central task in robot navigation and industrial diagnosis, where the detection time is always a critical issue. Existing methods are hardly applicable to these real-time scenarios of limited hardware resource due to the huge number of fragment candidates (edges or arcs) for fitting ellipse equations. In this paper, we present a fast algorithm detecting ellipses with high accuracy. The algorithm leverages a newly developed projective invariant to significantly prune the undesired candidates and to pick out elliptical ones. The invariant is able to reflect the intrinsic geometry of a planar curve, giving the value of -1 on any three collinear points and +1 for any six points on an ellipse. Thus, we apply the pruning and picking by simply comparing these binary values. Moreover, the calculation of the invariant only involves the determinant of a 3×3 matrix. Extensive experiments on three challenging data sets with 648 images demonstrate that our detector runs 20%-50% faster than the state-of-the-art algorithms with the comparable or higher precision.

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Section: Characteristic Number On Six Points Of a Conicmentioning

confidence: 99%

Section: Arc Selectionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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A Fast Ellipse Detector Using Projective Invariant Pruning

Jia

Fan

Luo

et al. 2017

IEEE Trans. on Image Process.

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“…Ellipse detection can be established using reduced quantization-free parameters space [4] or by analyzing of principal components [5]. Basically, three stable points are needed to fit an ellipse on the object contour [6][7][8]. Meanwhile the accuracy of ellipse detection highly depends on accurate detection of the object contour which is a difficult task in many cases dealing with embryo images.…”

Section: Computer Vision Methodsmentioning

confidence: 99%

Application of Numerical Intelligence Methods for the Automatic Quality Grading of an Embryo Development

Jonaitis¹,

Raudonis²,

Lipnickas³

2016

IJC

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In vitro fertilization – a procedure which aims to get the embryo to adapt the methods of "oocyte" fertilized sperm outside the human body. At the end of this procedure there are several embryos. This paper represents overview of tracking-free and tracking-based methods for detection of important embryo development stages. Tracking-based method represents well known classical object tracking techniques. For tracking-free method were selected statistical feature extraction techniques and classification methods: Classification with training and classification without training. For the feature extraction proposed statistical methods: entropy, invariant moments and principal components analyses. For the classification are used neural networks, support vector machine and K-nearest neighbor method. Data collected consist of 500 images for each class. 70 percent of images are dedicated for training, and 30 percent for testing. The proposed method is checked by experiment. It is expected that this method will work well in video sequences.

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“…Ellipses are one of the most frequently observed shapes in digital imagery since they are projections of circles commonly available in realworld objects. This has prompted ellipse fitting to be one of the prerequisites for several shape matching methods employed in many applications, including facial gesture analysis [30,15], medical image analysis [27,31], vehicle wheels detection [7], visual cyclists analysis [1], and traffic sign detection [22].…”

Section: Related Workmentioning

confidence: 99%

Ellipse Detection for Visual Cyclists Analysis “In the Wild”

Eldesokey

Felsberg

Khan

2017

Computer Analysis of Images and Patterns

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Autonomous driving safety is becoming a paramount issue due to the emergence of many autonomous vehicle prototypes. The safety measures ensure that autonomous vehicles are safe to operate among pedestrians, cyclists and conventional vehicles. While safety measures for pedestrians have been widely studied in literature, little attention has been paid to safety measures for cyclists. Visual cyclists analysis is a challenging problem due to the complex structure and dynamic nature of the cyclists. The dynamic model used for cyclists analysis heavily relies on the wheels. In this paper, we investigate the problem of ellipse detection for visual cyclists analysis in the wild. Our first contribution is the introduction of a new challenging annotated dataset for bicycle wheels, collected in real-world urban environment. Our second contribution is a method that combines reliable arcs selection and grouping strategies for ellipse detection. The reliable selection and grouping mechanism leads to robust ellipse detections when combined with the standard least square ellipse fitting approach. Our experiments clearly demonstrate that our method provides improved results, both in terms of accuracy and robustness in challenging urban environment settings.

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A Fast Automatic Ellipse Detector

Cited by 10 publications

References 5 publications

A Fast Ellipse Detector Using Projective Invariant Pruning

A Fast Ellipse Detector Using Projective Invariant Pruning

Application of Numerical Intelligence Methods for the Automatic Quality Grading of an Embryo Development

Ellipse Detection for Visual Cyclists Analysis “In the Wild”

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