A survey of Hough Transform

Mukhopadhyay, Priyanka; Chaudhuri, B. B.

doi:10.1016/j.patcog.2014.08.027

Cited by 513 publications

(247 citation statements)

References 151 publications

Supporting

Mentioning

243

Contrasting

Unclassified

Order By: Relevance

“…A Hough transform [14][15][16] detects shape in binary images by using an array named parameter space. Each point in binary images votes for the parameters space.…”

Section: Related Workmentioning

confidence: 99%

See 1 more Smart Citation

Road Detection by Using a Generalized Hough Transform

Liu

Zhang

et al. 2017

Remote Sensing

View full text Add to dashboard Cite

Road detection plays key roles for remote sensing image analytics. Hough transform (HT) is one very typical method for road detection, especially for straight line road detection. Although many variants of Hough transform have been reported, it is still a great challenge to develop a low computational complexity and time-saving Hough transform algorithm. In this paper, we propose a generalized Hough transform (i.e., Radon transform) implementation for road detection in remote sensing images. Specifically, we present a dictionary learning method to approximate the Radon transform. The proposed approximation method treats a Radon transform as a linear transform, which then facilitates parallel implementation of the Radon transform for multiple images. To evaluate the proposed algorithm, we conduct extensive experiments on the popular RSSCN7 database for straight road detection. The experimental results demonstrate that our method is superior to the traditional algorithms in terms of accuracy and computing complexity.

show abstract

“…A Hough transform [14][15][16] detects shape in binary images by using an array named parameter space. Each point in binary images votes for the parameters space.…”

Section: Related Workmentioning

confidence: 99%

“…A Hough transform (HT) [14][15][16] is one of the very typical methods and has been widely applied to computer processing, image processing, and digital image processing. It transforms the problem of a global detection in a binary image into peaks detection in a Hough parameter space.…”

Section: Introductionmentioning

confidence: 99%

Road Detection by Using a Generalized Hough Transform

Liu

Zhang

et al. 2017

Remote Sensing

View full text Add to dashboard Cite

show abstract

“…Because of its good anti-noise performance and insensitivity to the phenomenon of partial cover, it is widely used in the fields of pattern recognition, machine version and artificial intelligence [1] . Although SHT has strong robustness in anti-noise and anti-distortion, the method has the following disadvantages in practical application [2] : (1) The SHT can only detect geometric graphic element with strict mathematical expressions, such as lines, circles and ellipses; (2) With the increase of parameters in the curve expression, both the complexity and the memory cost of the SHT are exponentially growing;(3) If we want to improve the accuracy of the recognition of the characteristic circle, more detailed parameter quantization is needed, which will lead to much more processing time and memory consumption; (4) There is a great error in the recognition if the radius is not properly controlled.…”

Section: Introductionmentioning

confidence: 99%

An Improved Method of Hough Transform for Circle Recognition Based on Optimized Gradient

He¹,

Yp²

2017

Proceedings of the 2017 5th International Conference on Frontiers of Manufacturing Science and Measuring Technology (FMSMT 2017

View full text Add to dashboard Cite

Abstract. For the situation in which the low accuracy of circular hole recognition in hole workpiece based on image processing technology, a method of Optimized Gradient Hough Transform(OGHT) for circle recognition is proposed. Based on the Standard Hough Transform (SHT), the OGHT is used to extract the edge feature firstly, then select a certain number of edge pixels to evaluate the gradient to determine the center of circle. Next, calculating and sorting the coincidence degree between the rounds recognized by Standard Hough Transform and the edge in source image, and finally the highest rank is identified as the circle we needed. The proposed method is verified by comparative experiments, which improves the accuracy of circle recognition with little effect on efficiency.

show abstract

“…The main idea of the method is to transform a spatially-extended pattern into a concentrated region in a feature space, that is a problem of detecting a global feature in the image space is then converted into a local problem in the feature space. A more detailed introduction to the Hough transform can be found in [1] [2], [3] and [4] who place a particular group of Hough transforms in the framework of importance sampling. Variations on the original Hough transform have been proposed for different situations.…”

Section: Introductionmentioning

confidence: 99%

A clutter-calibrated Hough transform for the estimation of directional structure and dominant directions in grey-level images

Hamed¹,

Aykroyd

2017

Stat., optim. inf. comput.

View full text Add to dashboard Cite

Increasing amounts of image data are being routinely collected as part of the big-data revolution, with applications as diverse as automated security surveillance and dynamic medical imaging. To make best use of the data, the analyses must be automatic and rapid. Simple image properties can be used to highlight specific features in an initial screening or form input to elaborate classification techniques. A key stage in any image analysis is the identification of structure amongst the noise. It is important to realise that noise can be localized, independent and random, or it could contain small-scale structure which, in some ways, resembles the important features-this is called clutter. This paper uses the concept of the Hough transform to convert grey-level images into a more useful feature space representation. This space is searched for high density regions to identify dominant structure whilst taking into account micro-line clutter. Further, a directional distribution is introduced and a resulting dominant direct is proposed as a single structural summary. Many examples of simulated and real data images are used to illustrate the proposed techniques.

show abstract

A survey of Hough Transform

Cited by 513 publications

References 151 publications

Road Detection by Using a Generalized Hough Transform

Road Detection by Using a Generalized Hough Transform

An Improved Method of Hough Transform for Circle Recognition Based on Optimized Gradient

A clutter-calibrated Hough transform for the estimation of directional structure and dominant directions in grey-level images

Contact Info

Product

Resources

About