Accurate and Robust Line Segment Extraction Using Minimum Entropy With Hough Transform

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 distributi...

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Bibliographic Details
Published in:IEEE transactions on image processing Vol. 24; no. 3; pp. 813 - 822
Main Authors: Zezhong Xu, Bok-Suk Shin, Klette, Reinhard
Format: Journal Article
Language:English
Published: United States IEEE 01.03.2015
Subjects:
Accuracy
Entropy
fitting and interpolation
Hough transform
Image segmentation
Interpolation
line segment detection
Polynomials
Quantization (signal)
Transforms
line segment detection
entropy
Hough transform
fitting and interpolation
ISSN:1057-7149, 1941-0042, 1941-0042
Online Access:Get full text
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Summary: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.
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ISSN:1057-7149
1941-0042
1941-0042
DOI:10.1109/TIP.2014.2387020