Real-Time Computation of the Generalized Hough Transform

By in-depth analysis and summary of tower suspension insulator strings images collected, a algorithm of edge feature matching of suspension insulator strings was proposed to detect the windage yaw angle in real-time. By filter, image grayscale, interframe difference and edge feature matching which based on invariance Generalized Hough Transform (IGHT) and local feature of suspension insulator strings stored in a database, the coordinates of the ends of suspension insulator strings were determined, and then the size of windage yaw angle of suspension insulator strings was calculated. The algorithm proposed can provide translation, scaling and rotation invariance, and be better matching accuracy and robustness.

Download Full-text

Real-time pattern recognition using an optical generalized Hough transform

Applied Optics ◽

10.1364/ao.54.010586 ◽

2015 ◽

Vol 54 (36) ◽

pp. 10586 ◽

Cited By ~ 8

Author(s):

Ariel Fernández ◽

Jorge L. Flores ◽

Julia R. Alonso ◽

José A. Ferrari

Keyword(s):

Pattern Recognition ◽

Real Time ◽

Hough Transform ◽

Time Pattern ◽

Generalized Hough Transform

Download Full-text

Real-time object recognition using a modified generalized Hough transform

Pattern Recognition ◽

10.1016/s0031-3203(03)00169-9 ◽

2003 ◽

Vol 36 (11) ◽

pp. 2557-2570 ◽

Cited By ~ 46

Author(s):

Markus Ulrich ◽

Carsten Steger ◽

Albert Baumgartner

Keyword(s):

Object Recognition ◽

Real Time ◽

Hough Transform ◽

Generalized Hough Transform

Download Full-text

Intracoronary ultrasound catheter motion compensation using the generalized Hough transform

Computers in Cardiology 1994 ◽

10.1109/cic.1994.470191 ◽

2002 ◽

Cited By ~ 1

Author(s):

M.H. Van Horn ◽

W.E. Snyder ◽

G.A. Braden ◽

D.M. Herrington

Keyword(s):

Motion Compensation ◽

Hough Transform ◽

Intracoronary Ultrasound ◽

Generalized Hough Transform

Download Full-text

INVESTIGATION ON MULTISETS MIXTURE LEARNING BASED OBJECT DETECTION

International Journal of Pattern Recognition and Artificial Intelligence ◽

10.1142/s0218001407005971 ◽

2007 ◽

Vol 21 (08) ◽

pp. 1339-1351 ◽

Cited By ~ 1

Author(s):

ZHI-YONG LIU ◽

HONG QIAO ◽

LEI XU

Keyword(s):

Object Detection ◽

Hough Transform ◽

Line Segment ◽

Reconstruction Error ◽

Mean Square ◽

Noise Resistance ◽

Generalized Hough Transform ◽

The Mean ◽

Sample Reconstruction

By minimizing the mean square reconstruction error, multisets mixture learning (MML) provides a general approach for object detection in image. To calculate each sample reconstruction error, as the object template is represented by a set of contour points, the MML needs to inefficiently enumerate the distances between the sample and all the contour points. In this paper, we develop the line segment approximation (LSA) algorithm to calculate the reconstruction error, which is shown theoretically and experimentally to be more efficient than the enumeration method. It is also experimentally illustrated that the MML based algorithm has a better noise resistance ability than the generalized Hough transform (GHT) based counterpart.

Download Full-text