Concentric circle detection based on normalized distance variance and the straight line Hough transform

2014 ◽  
Author(s):  
Xing Chen ◽  
Ling Lu ◽  
Sheng Yang
Keyword(s):  
Hough Transform ◽  
Concentric Circle ◽  
Circle Detection ◽  
Straight Line

Coarse-To-Fine 3D Randomized Hough Transform for Dim Target Detection

2014 ◽  
Vol 519-520 ◽  
pp. 1040-1045
Author(s):  
Ling Fan
Keyword(s):  
Target Detection ◽  
Hough Transform ◽  
Constant Velocity ◽  
Three Dimensional ◽  
Randomized Hough Transform ◽  
Straight Line ◽  
Memory Complexity ◽  
Coarse To Fine

This paper makes some improvements on Roberts representation for straight line in space and proposes a coarse-to-fine three-dimensional (3D) Randomized Hough Transform (RHT) for the detection of dim targets. Using range, bearing and elevation information of the received echoes, 3D RHT can detect constant velocity target in space. In addition, this paper applies a coarse-to-fine strategy to the 3D RHT, which aims to solve both the computational and memory complexity problems. The validity of the coarse-to-fine 3D RHT is verified by simulations. In comparison with the 2D case, which only uses the range-bearing information, the coarse-to-fine 3D RHT has a better practical value in dim target detection.

Fast circle detection algorithm using sequenced Hough transform

2014 ◽  
Vol 22 (4) ◽  
pp. 1104-1111 ◽  
Author(s):  
叶峰 YE Feng ◽  
陈灿杰 CHEN Can-jie ◽  
赖乙宗 LAI Yi-zong ◽  
陈剑东 CHEN Jian-dong
Keyword(s):  
Hough Transform ◽  
Detection Algorithm ◽  
Circle Detection

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.

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