Direct Inverse Randomized Hough Transform for Incomplete Ellipse Detection in Noisy Images

2014 ◽  
pp. 13-24 ◽  
Author(s):  
Wei Lu ◽  
Jinhua Yu ◽  
Jinglu Tan
Keyword(s):  
Hough Transform ◽  
Ellipse Detection ◽  
Noisy Images ◽  
Randomized Hough Transform

The Application of Randomized Hough Transform in Ellipse Image Detection

2010 ◽  
Vol 159 ◽  
pp. 388-392
Author(s):  
Xin Yu Hu ◽  
Zuo Bing Chen ◽  
Dao De Zhang ◽  
Guang You Yang
Keyword(s):  
Hough Transform ◽  
Computing Time ◽  
Least Square ◽  
Storage Space ◽  
Fast Detection ◽  
Ellipse Detection ◽  
Good Ability ◽  
Randomized Hough Transform ◽  
Edge Points ◽  
Least Square Approach

As the traditional Hough transform has such defects as large storage space and long computing time in ellipse detection, an improved randomized ellipses detection method based on least squares was presented, which utilizes the least square approach to fit the ellipse and combines both of the advantages of the random Hough transform and the least square. By setting appropriate distance threshold of the candidate ellipse and the threshold of edge points, the method of ellipse detection decreases the number of random sampling and the invalid calculation of cumulation in the process of Hough transform. The results show that the method doesn’t require large storage space, has good ability to overcome the noise and realizes the fast detection for the single ellipse and defective ellipse.

ELLIPSOIDAL DROPLET DETECTION USING RANDOMIZED HOUGH TRANSFORM

2006 ◽  
Vol 16 (4) ◽  
pp. 365-378
Author(s):  
Y. J. Choo ◽  
B. S. Kang
Keyword(s):  
Hough Transform ◽  
Randomized Hough Transform

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.

scholarly journals Faster and optimal detection of parametric shapes

2021 ◽  
Author(s):  
Shynimol E. Thayilchira
Keyword(s):  
Probability Theory ◽  
Image Analysis ◽  
Computational Complexity ◽  
Hough Transform ◽  
Cross Entropy ◽  
Optimal Detection ◽  
Time Efficiency ◽  
Generalized Hough Transform ◽  
Randomized Hough Transform ◽  
Complex Image

In this project, an analysis of the faster detection of shapes using Randomized Hough Transform (RHT) was investigated. Since reduced computational complexity and time efficiency are the major concerns for complex image analysis, the focus of the research was to investigate RHT for these specific tasks. Also, a detailed analysis of probability theory associated with RHT theory was investigated as well. Thus effectiveness of RHT was proven mathematically in this project. In this project, RHT technique combined with Generalized Hough Transform (GHT) using Newton's curve fitting technique was proposed for faster detection of shapes in the Hough Domain. Finally, the image under question was enhanced using Minimum Cross-Entropy Optimization to further enhance the image and then RGHT process was carried out. This helped the RGHT process to obtain the required time efficiency.

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