New parallel Hough transform for circles

Chan, Roger W.

doi:10.1049/ip-e.1991.0046

Cited by 16 publications

(6 citation statements)

References 7 publications

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“…3b), it was necessary to increase the original resolution from 128 by 128 pixels to 512 by 512 pixels by interpolation. The next step was to locate the centre of each well; this was achieved by converting the edge map data into Hough space by using a modified Hough transform algorithm for circles ( Chan & Siu, 1991). The Hough transform converts a single point in the original edge map into a line in Hough space ( Fig.…”

Section: Methodsmentioning

confidence: 99%

The effect of freezing and thawing on the magnetic resonance imaging parameters of water in beef, lamb and pork meat

Evans¹,

Nott²,

Kshirsagar³

et al. 1998

Int J of Food Sci Tech

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A new multisample magnetic resonance imaging (MRI) technique has been used to measure the values of T1, T2, magnetization transfer (MT) rate and liquid proton density ratio (M0%) for water in fresh and frozen–thawed beef, lamb and pork; four samples from the longissimus dorsi muscle of 25 different individuals per species were studied. The effect of two different chilling regimes, standard and tenderloin, were also studied. Standard chilled pork and lamb had a significantly higher M0% than tenderloin chilled samples, but there was no correlation between M0% and fresh weight; the differences in M0% are thought to be due to differences in MR (magnetic resonance)‐visible water. There was a significant decrease in water content, T1 relaxation time, and a significant increase in MT rate in the frozen–thawed samples when all the data were pooled for each species. However, individual animals differed in the magnitude and direction of the change; this means that because of interanimal variation, measurements of MR parameters from a single sample of meat cannot be used at present to authenticate it.

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Section: Methodsmentioning

confidence: 99%

The effect of freezing and thawing on the magnetic resonance imaging parameters of water in beef, lamb and pork meat

Evans¹,

Nott²,

Kshirsagar³

et al. 1998

Int J of Food Sci Tech

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show abstract

“…Detecting circles are usually on the basis of circles being special cases of ellipses. For example, the two-staged parameter space decomposition of finding the center first and then other parameters is shown in Chan (1991), using two sets of 2D accumulators. The geometric symmetry is also used in Rad et al (2003), applied on gradient pair vectors, and in Ioannou et al (1999) using the geometric constraint that any chord passes through the circle center.…”

Section: Hough Transformmentioning

confidence: 99%

Hough Transform Implementation For Event-Based Systems: Concepts and Challenges

Seifozzakerini

Yau

Mao

et al. 2018

Front. Comput. Neurosci.

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Hough transform (HT) is one of the most well-known techniques in computer vision that has been the basis of many practical image processing algorithms. HT however is designed to work for frame-based systems such as conventional digital cameras. Recently, event-based systems such as Dynamic Vision Sensor (DVS) cameras, has become popular among researchers. Event-based cameras have a significantly high temporal resolution (1 μs), but each pixel can only detect change and not color. As such, the conventional image processing algorithms cannot be readily applied to event-based output streams. Therefore, it is necessary to adapt the conventional image processing algorithms for event-based cameras. This paper provides a systematic explanation, starting from extending conventional HT to 3D HT, adaptation to event-based systems, and the implementation of the 3D HT using Spiking Neural Networks (SNNs). Using SNN enables the proposed solution to be easily realized on hardware using FPGA, without requiring CPU or additional memory. In addition, we also discuss techniques for optimal SNN-based implementation using efficient number of neurons for the required accuracy and resolution along each dimension, without increasing the overall computational complexity. We hope that this will help to reduce the gap between event-based and frame-based systems.

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“…Two-pass algorithms are now classical in Hough-based circle detection [41][42][43], but their first pass is a one-tomany 2d HT, where the votes are made along straight lines, either defined by the gradient direction [41,42], or by the bissector of a point pair chord [43]. In our V2 version, the first pass is a one-to-one DHT based on the isophote curvature.…”

Section: O R E N D F O R Endmentioning

confidence: 99%

Line and circle detection using dense one-to-one Hough transforms on greyscale images

Manzanera

Nguyễn

2016

J Image Video Proc.

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International audienceBy estimating the first-order (direction) and second-order (curvature) derivatives in an image, the parameters of a line or circle passing through a point may be uniquely defined in most cases. This allows to compute a one-to-one Hough transform, every point in the image space voting for one unique point in the parameter space. Moreover, those parameters can be directly estimated on the greyscale image without the need to calculate the contour and without reducing the spatial support of the Hough transform, i.e. densely on the whole image. The general framework using multiscale derivatives is presented, and the one-to-one Hough dense transforms for detecting lines and circles are evaluated and compared with other variants of Hough transforms, from qualitative and computational points of view

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New parallel Hough transform for circles

Cited by 16 publications

References 7 publications

The effect of freezing and thawing on the magnetic resonance imaging parameters of water in beef, lamb and pork meat

The effect of freezing and thawing on the magnetic resonance imaging parameters of water in beef, lamb and pork meat

Hough Transform Implementation For Event-Based Systems: Concepts and Challenges

Line and circle detection using dense one-to-one Hough transforms on greyscale images

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