Algorithm for extracting ellipses using weighted center points map

Yamato, Junji; Ishii, Ikuo; Makino, Hideo; Irisawa, Kazuyoshi

doi:10.1002/scj.4690211010

Cited by 6 publications

(3 citation statements)

References 3 publications

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“…This feature has been utilized to recognize ellipses in many methods, and the validity of the feature has been proved [5,11,12]. The authors also proposed a convenient method, the Segments Pair Matching Method, to detect candidate ellipses using diameters, and have verified its effectiveness on real images [13,14].…”

Section: Introductionmentioning

confidence: 99%

Elliptic arc discrimination using conjugate diameter of chord

Mori

Watanabe

Katagiri

2001

Systems & Computers in Japan

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SUMMARYThere are many solids of revolution in our surroundings. Since they have elliptic outlines, discriminating elliptic arcs is an important technique for recognizing objects. The presence of diameters is a significant property of ellipses and has been effectively used in many methods of recognizing ellipses. However, the property is not valuable in discriminating short elliptic arcs because diameters are meaningless for short arcs. This paper proposes a method of discriminating elliptic arcs using two parallel scanning lines to a conjugate diameter of a chord. The method requires accurate determination of the conjugate diameter. First, the procedure of binary searching for the conjugate diameter will be established. Next, the method is tested on a real image to prove its validity. Finally, tolerance to noise and sensitivity to deformations of the method will be evaluated. © 2001 Scripta Technica, Syst Comp Jpn, 32(13): 113, 2001

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Section: Introductionmentioning

confidence: 99%

Elliptic arc discrimination using conjugate diameter of chord

Mori

Watanabe

Katagiri

2001

Systems & Computers in Japan

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“…To deal with this particular difficulty, Scholars have proposed many efficient implementations of the HT. One kind of these methods is the parameter space decomposition technique [3,4,5]. It decomposes the computation into separate stages, each stage passing results to the next, in this way, a high dimensional parameter problem is reduced to a series of lower dimensional problem.…”

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confidence: 99%

Moving point target trajectory detection based on dual-Hough transform

Hu¹

2007

SPIE Proceedings

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This paper deals with research on detecting moving point target trajectory in image sequence. A novel method is presents for this purpose, which combines two 2-dimension Hough transforms to suppress noise points and to detect trajectory points in time order. The first Hough transform has an accumulators array using a restricted voting process and a set of straight lines are found in the image plane. A new T-L parameter space is proposed which is derived from these straight lines. In the second transform, collinear points are mapped into T-L space and it is easy to find the direction of motion. Experimental results show that our method can effectively extract moving point target trajectory accurately in a limited observing time especially scanning images from large numbers of noise points while search region is much larger than target movability. Key words : Moving point target (MPT) trajectory detection, dual-Hough transform (DHT), X-Y-T observation space, T-L parameter space NTRODUCTIONIn long-range infrared detection and tracking application, target is close to point source, and motion trajectory becomes an important evidence for target detection and prediction for the absence of shape information. When search region is much larger than target movability and there are large number noise points with intensity over segmentation threshold, one Hough transform will give many false trajectories consist of noise points or real target trajectories mixed noise points. That will interfere with predicting trajectory in a limited observing time especially for scanning images.The Hough transform HT [1] is a powerful method for parameter extraction of any analytic pattern in images. By mapping features of an image space into a parameter space, the Hough transform converts a difficult global detection problem to a more easily solved local peak detection problem [2]. The main advantages of the Hough transform are its robustness to noise in the image and discontinuities in the pattern to be detected--both of which are frequently encountered in real-world images.The principal disadvantages of the standard Hough transform (SHT) are its demand for a tremendous amount of computing power and large storage. Both the storage and computation grow exponentially with the number of parameters. The SHT can be successfully adapted for detecting patterns with two parameters, e.g. straight lines, centers of circles with a fixed radius etc. However, it is not practical for direct application of the HT for a pattern with three of more parameters, because both the computer storage and computation grow exponentially with the number of parameters. To deal with this particular difficulty, Scholars have proposed many efficient implementations of the HT. One kind of these methods is the parameter space decomposition technique [3,4,5]. It decomposes the computation into separate stages, each stage passing results to the next, in this way, a high dimensional parameter problem is reduced to a series of lower dimensional problem. Another type of methods...

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“…To deal with this particular difficulty, two kinds of methods were proposed. One is the parameter space decomposition technique [8,9]. It decomposes the computation into separate stages, each stage passing results to the next, in this way, a high dimensional parameter problem is reduced to a series of lower dimensional problems.…”

Section: Introductionmentioning

confidence: 99%

Curve Detection Using Inverse Hough Transform as a Parameter Optimization Problem

Chang

Hashimoto

1995

IEEJ Trans.EIS

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The Hough Transform is an elegant way of extracting global features like parametric curves from binary edge images, however, its large computation and memory requirements prevent it from being used for practical computer vision tasks. This paper presents a new approach to detect parametric curves using the Inverse Hough Transform. The key idea of this method is to make the voting process on the image space instead of that on the parameter space in the conventional method, then convert the local peak detection problem into a parameter optimization problem. This leads to substantial savings, not only in storage requirements but also in the amount of calculation required. The experimental results and qualitative analysis showed that in comparison with the conventional Hough Transform methods, the new method has advantages of high speed, small storage, arbitrary parameter range and high parameter resolution.

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Algorithm for extracting ellipses using weighted center points map

Cited by 6 publications

References 3 publications

Elliptic arc discrimination using conjugate diameter of chord

Elliptic arc discrimination using conjugate diameter of chord

Moving point target trajectory detection based on dual-Hough transform

Curve Detection Using Inverse Hough Transform as a Parameter Optimization Problem

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