Accurate and robust line segment extraction using minimum entropy with Hough transform


by Z Xu, B-S Shin, R Klette
Abstract:
The Hough transform is a popular technique used in the field of image processing and computer vision. With a Hough transform technique, not only the normal angle and distance of a line but also the line-segment’s length and midpoint (centroid) can be extracted by analysing the voting distribution around a peak in the Hough space. In this paper, a method based on minimum-entropy analysis is proposed to extract the set of parameters of a line segment. In each column around a peak in Hough space, the voting values specify probabilistic distributions. The corresponding entropies and statistical means are computed. The line-segment’s normal angle and length are simultaneously computed by fitting a quadratic polynomial curve to the voting entropies. The line-segment’s midpoint and normal distance are computed by fitting and interpolating a linear curve to the voting means. The proposed method is tested on simulated images for detection accuracy by providing comparative results. Experimental results on real-world images verify the method as well. The proposed method for line-segment detection is both accurate and robust in the presence of quantization error, background noise, or pixel disturbances.
Reference:
Accurate and robust line segment extraction using minimum entropy with Hough transform (Z Xu, B-S Shin, R Klette), In IEEE Transactions on Image Processing, Institute of Electrical and Electronics Engineers Inc., volume 24, 2015.
Bibtex Entry:
@article{xu2015accuratetransform,
author = "Xu, Z and Shin, B-S and Klette, R",
journal = "IEEE Transactions on Image Processing",
month = "Mar",
pages = "813--822",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
title = "Accurate and robust line segment extraction using minimum entropy with Hough transform",
volume = "24",
year = "2015",
abstract = "The Hough transform is a popular technique used in the field of image processing and computer vision. With a Hough transform technique, not only the normal angle and distance of a line but also the line-segment's length and midpoint (centroid) can be extracted by analysing the voting distribution around a peak in the Hough space. In this paper, a method based on minimum-entropy analysis is proposed to extract the set of parameters of a line segment. In each column around a peak in Hough space, the voting values specify probabilistic distributions. The corresponding entropies and statistical means are computed. The line-segment's normal angle and length are simultaneously computed by fitting a quadratic polynomial curve to the voting entropies. The line-segment's midpoint and normal distance are computed by fitting and interpolating a linear curve to the voting means. The proposed method is tested on simulated images for detection accuracy by providing comparative results. Experimental results on real-world images verify the method as well. The proposed method for line-segment detection is both accurate and robust in the presence of quantization error, background noise, or pixel disturbances.",
doi = "10.1109/TIP.2014.2387020",
issn = "1057-7149",
issue = "3",
keyword = "entropy",
keyword = "fitting and interpolation",
keyword = "Hough transform",
keyword = "line segment detection",
language = "eng",
day = "1",
}