scholarly journals gamma - omega Hough transform-elimination of quantization noise and linearization of voting curves in the rho - theta parameter space

2003 ◽  
Author(s):  
T. Wada ◽  
T. Matsuyama
Keyword(s):  
Parameter Space ◽  
Hough Transform ◽  
Quantization Noise

scholarly journals Traditional Lines and Circles detector

2020 ◽  
Author(s):  
Jimut Bahan Pal
Keyword(s):  
Parameter Space ◽  
Hough Transform ◽  
Traditional Method ◽  
Local Maxima ◽  
Simple Detection

The traditional method for extracting features from image is accomplished by Hough Transform. When an image is represented mathematically, it becomes easy to perform computation according to a certain algorithm and detect the required features from the image. The presence of such features is determined by a voting procedure in a parameter space, from which the selected features are obtained as local maxima by creating a certain threshold. This procedure for simple detection of lines was patented by Paul V.C. Hough in 1962. There has been various advancement in this field for detecting lines and circles, all of them are based on the parent Hough Transform way to vote for the corresponding features. The various ways of detecting features and the mathematics behind them is discussed in this report.

scholarly journals A radio burst detection method based on the Hough transform

2020 ◽  
Vol 494 (2) ◽  
pp. 1994-2003
Author(s):  
Shifan Zuo ◽  
Xuelei Chen
Keyword(s):  
Parameter Space ◽  
Radio Burst ◽  
Hough Transform ◽  
Single Point ◽  
Fast Method ◽  
Noise Levels ◽  
Time Frequency ◽  
Straight Line ◽  
Fast Radio Burst ◽  
Truncation Threshold

ABSTRACT We present a simple and fast method for incoherent dedispersion and fast radio burst (FRB) detection based on the Hough transform, which is widely used for feature extraction in image analysis. The Hough transform maps a point in the time–frequency data to a straight line in the parameter space and points on the same dispersed f−2 curve to a bundle of lines all crossing at the same point, thus the curve is transformed to a single point in the parameter space, enabling an easier way for the detection of radio burst. By choosing an appropriate truncation threshold, in a reasonably radio quiet environment, i.e. with radio frequency interferences present but not dominant, the computing speed of the method is very fast. Using simulation data of different noise levels, we studied how the detected peak varies with different truncation thresholds. We also tested the method with some real pulsar and FRB data.

AN IMPROVED HOUGH TRANSFORM ALGORITHM FOR RADAR DETECTION

2010 ◽  
Vol 19 (03) ◽  
pp. 549-555 ◽  
Author(s):  
J. ZENG ◽  
J. ZHANG ◽  
L. XIANG ◽  
Z. DONG ◽  
S. YUAN
Keyword(s):  
Parameter Space ◽  
Hough Transform ◽  
Radar Detection ◽  
Maximum Range ◽  
Straight Lines

In this paper, we describe a new algorithm for radar detection based on the Hough transform which employs the slope-intercept parameter space. Unlike the conventional Hough transform, we shift the parameter space cells to perform the transform. The computation burden is reduced. Another advantage is that those straight lines whose intercept are bigger than the radar maximum range can also be detected. In addition, we also investigate the performance of the algorithm we present and show its efficiency with some simulations.

scholarly journals Analysing Arbitrary Curves from the Line Hough Transform

2020 ◽  
Vol 6 (4) ◽  
pp. 26
Author(s):  
Donald Bailey ◽  
Yuan Chang ◽  
Steven Le Moan
Keyword(s):  
Convex Hull ◽  
Parameter Space ◽  
Hough Transform ◽  
Image Space ◽  
Curve Shape ◽  
Linear Features ◽  
Closed Curves ◽  
Parametric Curves ◽  
Inflection Points ◽  
One To One

The Hough transform is commonly used for detecting linear features within an image. A line is mapped to a peak within parameter space corresponding to the parameters of the line. By analysing the shape of the peak, or peak locus, within parameter space, it is possible to also use the line Hough transform to detect or analyse arbitrary (non-parametric) curves. It is shown that there is a one-to-one relationship between the curve in image space, and the peak locus in parameter space, enabling the complete curve to be reconstructed from its peak locus. In this paper, we determine the patterns of the peak locus for closed curves (including circles and ellipses), linear segments, inflection points, and corners. It is demonstrated that the curve shape can be simplified by ignoring parts of the peak locus. One such simplification is to derive the convex hull of shapes directly from the representation within the Hough transform. It is also demonstrated that the parameters of elliptical blobs can be measured directly from the Hough transform.

Computer Vision Recognition of Incomplete Symbols in Russian Symbols

2014 ◽  
Vol 651-653 ◽  
pp. 2283-2286
Author(s):  
Hui Jie Sun ◽  
Qiang Chen
Keyword(s):  
Computer Vision ◽  
Parameter Space ◽  
Hough Transform ◽  
Recognition Rate ◽  
Image Space ◽  
Discrimination Function ◽  
Recognition Method ◽  
Identification Methods ◽  
Clustering Problem ◽  
Feature Information

In the computer vision recognition of incomplete symbols in Russian symbols, the traditional identification methods can only identify a small number of complete Russian symbols, and have a low recognition rate of the incomplete Russian symbols. To this end, this paper presents a method for computer vision recognition of incomplete symbols in Russian symbols based on Hough transform algorithm. According to the mapping from the image space to the parameter space, the complex edge feature information in image space is transformed into the clustering problem in the parameter space, and the discrimination function and the rules are developed and employed to recognize the image need to be recognized. Experiments show that with Hough transform algorithm to identify incomplete symbols in Russian symbols, the incomplete symbol in Russian symbols can be identified quickly and effectively, which improves the performance of recognition method and meet the needs of many scholars.

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