Finding circles by an array of accumulators

Kimme, Carolyn; Ballard, Dana H.; Sklansky, Jack

doi:10.1145/360666.360677

Cited by 480 publications

(197 citation statements)

References 2 publications

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“…An adaptation of the circular Hough transform (CHT) algorithm [14] is then used to assess whether these candidate edges fall on circles of varying center and angular radius. If a map of the ''CHT score'' (the fraction of pixels in an annulus surrounding a given center that are candidate edges) is sharply peaked at some angular scale , then there is evidence for a circular edge of this radius centered on the peak.…”

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confidence: 99%

First Observational Tests of Eternal Inflation

et al. 2011

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The eternal inflation scenario predicts that our observable Universe resides inside a single bubble embedded in a vast inflating multiverse. We present the first observational tests of eternal inflation, performing a search for cosmological signatures of collisions with other bubble universes in cosmic microwave background data from the WMAP satellite. We conclude that the WMAP 7-year data do not warrant augmenting the cold dark matter model with a cosmological constant with bubble collisions, constraining the average number of detectable bubble collisions on the full sky " N s < 1:6 at 68% C.L. Data from the Planck satellite can be used to more definitively test the bubble-collision hypothesis.

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confidence: 99%

First Observational Tests of Eternal Inflation

et al. 2011

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“…Fig. 8 presents some others experiments on real pictures, with also a comparison with three methods based on the Hough transform: CHT [3], MHT [4] and FHT [5]. For all of these experiments the source image was thresholded by a value defined in the HSB color space.…”

Section: Experimental Evaluationmentioning

confidence: 99%

“…The first group contains the methods that detect directly arcs and circles through some well known techniques of transformation in images (Hough or Radon transform). The generalized Hough transform is widely used as a basic tool for solving this problem [3][4][5]. These methods try to transform the input image into the parameter space and the circle candidates are detected as peaks in this space.…”

Section: Introductionmentioning

confidence: 99%

Unsupervised, Fast and Precise Recognition of Digital Arcs in Noisy Images

Nguyễn

Kerautret

Debled-Rennesson

et al. 2010

Computer Vision and Graphics

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Abstract. In image processing and pattern recognition, the accuracy of most algorithms is dependent on a good parameterization, generally a computation scale or an estimation of the amount of noise, which may be global or variable within the input image. Recently, a simple and linear time algorithm for arc detection in images was proposed [1]. Its accuracy is dependent on the correct evaluation of the amount of noise, which was set by the user in this former version. In the present work we integrate a promising unsupervised noise detection method [2] in this arc recognition method, in order to process images with or without noise, uniformly distributed or variable within the picture. We evaluate the performance of this algorithm and we compare it with standard arc and circle detection methods based on extensions of the Hough transform.

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“…The output accumulator space has a peak where these contributed circles overlap at the center of the original circle. Modification to the CHT has been widely implemented to either increase the detection rate or reduce its computational complexity [6][7][8] . In this work, the edge orientation information is used to increase the CHT performance.…”

Section: Circular Hough Transform (Cht)mentioning

confidence: 99%

“…In this work, the edge orientation information is used to increase the CHT performance. This method was first proposed by Kimme et al [6] . The use of the edge orientation information limits the possible positions of the center for each edge point.…”

Section: Circular Hough Transform (Cht)mentioning

confidence: 99%

Object Detection using Circular Hough Transform

Rizon¹,

Yazid²,

Saad³

et al. 2005

American J. of Applied Sciences

120

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Abstract:In this study we propose a new system to detect the object from an input image. The proposed system first uses the separability filter proposed by Fukui and Yamaguchi (Trans. IEICE Japan J80-D-II. 8, [2170][2171][2172][2173][2174][2175][2176][2177] 1997) to obtain the best object candidates and next, the system uses the Circular Hough Transform (CHT) to detect the presence of circular shape. The main contribution of this work consists of using together two different techniques in order to take advantages from the peculiarity of each of them. As the results of the experiments, the object detection rate of the proposed system was 96% for 25 images by moving the circle template every 20 pixels to right and down.

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Finding circles by an array of accumulators

Cited by 480 publications

References 2 publications

First Observational Tests of Eternal Inflation

First Observational Tests of Eternal Inflation

Unsupervised, Fast and Precise Recognition of Digital Arcs in Noisy Images

Object Detection using Circular Hough Transform

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