NDLI: A test framework for the accuracy of line detection by Hough Transforms

Content Provider	IEEE Xplore Digital Library
Author	Thuy Tuong Nguyen Xuan Dai Pham Dongkyun Kim Jae Wook Jeon
Copyright Year	2008
Description	Author affiliation: Sch. of Inf. & Commun. Eng., Sungkyunkwan Univ., Suwon (Thuy Tuong Nguyen; Xuan Dai Pham; Dongkyun Kim; Jae Wook Jeon)
Abstract	The detection of lines in an image is an important task. In the fact that many works have been done on line extraction, there is a lack of a comprehensive comparison of the so far proposed algorithms. The design and implementation of a framework to test line detection algorithms on intensity images is described in this paper. Our test framework is applied to compare the correctness and precision of lines extracted by Standard Hough Transform, Progressive Probabilistic Hough Transform and a proposed method based on Standard Hough Transform. The correctness of the extracted lines relates to global accuracy whereas the precision concerns accuracy at a local level. The well-known Standard Hough Transform (SHT) and Progressive Probabilistic Hough Transform (PPHT) are two of the most efficient algorithms for line detection. SHT can detect almost straight lines in the image, and it is highly resistant to noise. Line segments are effectively found by PPHT. However, this algorithm has lower accuracy than SHT. The proposed method based on SHT overcomes this. It contains three extensions: the technique of accumulation, the application of a local maxima rule, and the detection of line segments. The test framework enables us to evaluate the advantages and disadvantages of the three Hough Transform algorithms by analyzing the results of line extraction.
Starting Page	1528
Ending Page	1533
File Size	353604
Page Count	6
File Format	PDF
ISBN	9781424421701
ISSN	19354576
DOI	10.1109/INDIN.2008.4618347
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2008-07-13
Publisher Place	South Korea
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Content Type	Text
Resource Type	Article

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