A straight line detection using principal component analysis

Yun Seok Lee, Han Suh Koo, Chang Sung Jeong

Research output: Contribution to journalArticlepeer-review

85 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 languageEnglish
Pages (from-to)1744-1754
Number of pages11
JournalPattern Recognition Letters
Volume27
Issue number14
DOIs
Publication statusPublished - 2006 Oct 15

Bibliographical note

Funding Information:
This work was supported by the Brain Korea 21 and KIPA Information Technology Research Center. The Pentagon image was obtained from Carnegie Mellon Image Database and all the rest of the source images in experiment section were obtained from Visual Geometry Group at Oxford University.

Keywords

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

ASJC Scopus subject areas

  • Software
  • Signal Processing
  • Computer Vision and Pattern Recognition
  • Artificial Intelligence

Fingerprint

Dive into the research topics of 'A straight line detection using principal component analysis'. Together they form a unique fingerprint.

Cite this