Straight line extraction via multi-scale Hough transform based on pre-storage weight matrix

2011 ◽  
Vol 32 (23) ◽  
pp. 8315-8330 ◽  
Author(s):  
Shenghua Xu ◽  
Jiping Liu ◽  
Yong Wang ◽  
Litao Han ◽  
Yunsheng Zhang
Keyword(s):  
Hough Transform ◽  
Weight Matrix ◽  
Line Extraction ◽  
Multi Scale ◽  
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.

A multi-scale plane-detection method based on the Hough transform and region growing

2016 ◽  
Vol 31 (154) ◽  
pp. 166-192 ◽  
Author(s):  
Xiaoxu Leng ◽  
Jun Xiao ◽  
Ying Wang
Keyword(s):  
Hough Transform ◽  
Detection Method ◽  
Region Growing ◽  
Multi Scale

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.

Improved Hough Transform Applying in Mobile Robot Vision Navigation

2012 ◽  
Vol 232 ◽  
pp. 408-413
Author(s):  
Yin Ping Jiang ◽  
Xian Xian Zhang ◽  
Xiao Peng Fu
Keyword(s):  
Mobile Robot ◽  
Hough Transform ◽  
Robot Vision ◽  
Geometric Figure ◽  
Vision Navigation ◽  
Line Extraction ◽  
Local Threshold ◽  
Global Threshold ◽  
Image Quality Reduction ◽  
Mobile Robot Vision

This paper mainly discusses that in mobile robot vision navigation system, by using the improved Hough transform, we can improve the accuracy of line extraction and therefore avoid the image quality reduction caused by noise points. Considering the limitations of the standard Hough transform, we come up with a method with which we will accumulates the H (ρ, θ) through distributing the increment value, set a global threshold to shun the pointless measurements, eliminate the false lines by comparing θ difference between tow arbitrary lines, find the peaks by using rectangle window, and set a local threshold to eliminate false peaks. In this way, we can gain a method superior to the standard Hough transform which works better in extracting lines in application. The experiments show that this method can not only extract line features of geometric figure effectively in brief background, but also eliminate the iterative lines efficiently.

scholarly journals Parallel Hough Transform-Based Straight Line Detection and Its FPGA Implementation in Embedded Vision

Sensors ◽  
2013 ◽  
Vol 13 (7) ◽  
pp. 9223-9247 ◽  
Author(s):  
Xiaofeng Lu ◽  
Li Song ◽  
Sumin Shen ◽  
Kang He ◽  
Songyu Yu ◽  
...  
Keyword(s):  
Hough Transform ◽  
Fpga Implementation ◽  
Line Detection ◽  
Embedded Vision ◽  
Straight Line ◽  
Straight Line Detection

Fast Circular Object Detection Using Multiscale Grayscale Hough Transform

2014 ◽  
Vol 490-491 ◽  
pp. 1542-1547 ◽  
Author(s):  
Wen Xia Yang ◽  
Zhang Can Huang
Keyword(s):  
Object Detection ◽  
Hough Transform ◽  
Dramatic Improvement ◽  
Gray Level ◽  
3 Dimensional ◽  
Radial Gradient ◽  
Multi Scale ◽  
Second Stage ◽  
Circular Object ◽  
Edge Detecting

A fast Hough transform for circular object detection is proposed in this paper which can be directly applied to gray level images. This method consists of three major stages. In the first stage, the center positions of circular objects are detected using the gray level Hough transform, which requires no conventional preprocessing such as edge detecting and binarization. The second stage determines the radius of the detected objects by analyzing the radial gradient profile. In order to detect objects with different radius in the same scene, a multi-scale strategy is integrated in the proposed method. Compared with traditional Hough transform, the gray level Hough transform uses a 2-dimensional accumulation map rather than the 3-dimensional one, which results in a dramatic improvement on the computational efficiency. Experiments have been carried out on more than 2000 real-world images and the result shows that 90.3% of the circular objects have been accurately detected, which demonstrate the applicability of the proposed method.

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