A straight line detection using principal component analysis

Yun Seok Lee; Han S. Koo; Chang-Sung Jeong

doi:10.1016/j.patrec.2006.04.016

A straight line detection using principal component analysis

Yun Seok Lee, Han S. Koo, Chang-Sung Jeong

School of Electrical Engineering

Research output: Contribution to journal › Article

73 Citations (Scopus)

Abstract

A straight line detection algorithm is presented. The algorithm separates row and column edges from edge image using their primitive shapes. The edges are labeled, and the principal component analysis (PCA) is performed for each labeled edges. With the principal components, the algorithm detects straight lines and their orientations, which is useful for various intensive applications. Our algorithm overcomes the disadvantages of Hough transform (HT) and other algorithms, i.e. unknown grouping of collinear lines, complexity and local ambiguities. The experimental results show the efficiency of our algorithm.

Original language	English
Pages (from-to)	1744-1754
Number of pages	11
Journal	Pattern Recognition Letters
Volume	27
Issue number	14
DOIs	https://doi.org/10.1016/j.patrec.2006.04.016
Publication status	Published - 2006 Oct 15

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Keywords

Edge image
Line descriptor
Principal component analysis (PCA)
Straight line detection

ASJC Scopus subject areas

Computer Vision and Pattern Recognition
Signal Processing
Electrical and Electronic Engineering

Cite this

Lee, Y. S., Koo, H. S., & Jeong, C-S. (2006). A straight line detection using principal component analysis. Pattern Recognition Letters, 27(14), 1744-1754. https://doi.org/10.1016/j.patrec.2006.04.016

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AB - A straight line detection algorithm is presented. The algorithm separates row and column edges from edge image using their primitive shapes. The edges are labeled, and the principal component analysis (PCA) is performed for each labeled edges. With the principal components, the algorithm detects straight lines and their orientations, which is useful for various intensive applications. Our algorithm overcomes the disadvantages of Hough transform (HT) and other algorithms, i.e. unknown grouping of collinear lines, complexity and local ambiguities. The experimental results show the efficiency of our algorithm.

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Access to Document

10.1016/j.patrec.2006.04.016