Range image segmentation based on randomized Hough transform

2005 ◽  
Vol 26 (13) ◽  
pp. 2033-2041 ◽  
Author(s):  
Yihong Ding ◽  
Xijian Ping ◽  
Min Hu ◽  
Dan Wang
Keyword(s):  
Image Segmentation ◽  
Hough Transform ◽  
Range Image ◽  
Randomized Hough Transform

Two-phase flow void fraction estimation based on bubble image segmentation using Randomized Hough Transform with Neural Network (RHTN)

2020 ◽  
Vol 118 ◽  
pp. 103133 ◽  
Author(s):  
Pedro L.S. Serra ◽  
Paulo H.F. Masotti ◽  
Marcelo S. Rocha ◽  
Delvonei A. de Andrade ◽  
Walmir M. Torres ◽  
...  
Keyword(s):  
Neural Network ◽  
Image Segmentation ◽  
Void Fraction ◽  
Hough Transform ◽  
Two Phase Flow ◽  
Phase Flow ◽  
Two Phase ◽  
Randomized Hough Transform ◽  
Flow Void

Range Image Segmentation Via Distance Slicing

1988 ◽  
Author(s):  
Jia-Guu Leu ◽  
Ishwar K. Sethi
Keyword(s):  
Image Segmentation ◽  
Range Image

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.

Sign in / Sign up

Export Citation Format

Share Document