Open AccessStraight line matching method based on line pairs and feature points
Author(s) -
Zeng Jiexian,
Zhan Liqin,
Fu Xiang,
Wang Binbin
Publication year - 2016
Publication title -
iet computer vision
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.38
H-Index - 37
eISSN - 1751-9640
pISSN - 1751-9632
DOI - 10.1049/iet-cvi.2014.0372
Subject(s) - artificial intelligence , feature (linguistics) , matching (statistics) , line (geometry) , pattern recognition (psychology) , line segment , similarity (geometry) , computer vision , feature extraction , computer science , feature vector , feature detection (computer vision) , image (mathematics) , mathematics , image processing , geometry , philosophy , statistics , linguistics
Straight line matching is a fundamental task in many applications such as scene matching, stereo vision and sequence images analysis. As individual line segments cannot completely present the information of image and methods based on them are difficult to achieve high matching accuracy, the authors propose a straight line matching algorithm based on line pairs and feature points. Extracted lines are clustered into line feature sets according to their spatial proximity and geometric structures. The structural relationship between line pairs, which are selected from the line feature sets, is described by feature vector consisting of length ratio, angle and average gradient. Coarse matching of line segments is achieved by using feature vector as similarity measurement. To eliminate the mismatches in the matched straight lines, the constraints of feature points are employed. The experimental results demonstrate that the authors’ algorithm can successfully match image lines with high accuracy under various image transformations, including scale, illumination changes and viewpoint variations.