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.

Randomized Hough Transform

2011 ◽  
pp. 1343-1350 ◽  
Author(s):  
Lei Xu ◽  
Erkki Oja
Keyword(s):  
Image Processing ◽  
Computer Vision ◽  
Hough Transform ◽  
Random Sampling ◽  
Computational Cost ◽  
Detection Accuracy ◽  
The Past ◽  
Randomized Hough Transform ◽  
Dynamic Storage ◽  
High Computational Cost

Proposed in 1962, the Hough transform (HT) has been widely applied and investigated for detecting curves, shapes, and motions in the fields of image processing and computer vision. However, the HT has several shortcomings, including high computational cost, low detection accuracy, vulnerability to noise, and possibility of missing objects. Many efforts target at solving some of the problems for decades, while the key idea remains more or less the same. Proposed in 1989 and further developed thereafter, the Randomized Hough Transform (RHT) manages to considerably overcome these shortcomings via innovations on the fundamental mechanisms, with random sampling in place of pixel scanning, converging mapping in place of diverging mapping, and dynamic storage in place of accumulation array. This article will provides an overview on advances and applications of RHT in the past one and half decades.

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