Line segment detection using weighted mean shift procedures on a 2D slice sampling strategy

Authors: Marcos Nieto and Carlos Cuevas and Luis Salgado and Narciso García

Date: 09.04.2011

Pattern Analysis & Applications


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Abstract

A new line segment detection approach is introduced in this paper for its application in real-time computer vision systems. It has been designed to work unsupervised without any prior knowledge of the imaged scene; hence, it does not require tuning of input parameters. Although many works have been presented on this topic, as far as we know, none of them achieves a trade-off between accuracy and speed as our strategy does. The reduction of the computational cost compared to other fast methods is based on a very efficient sampling strategy that sequentially proposes points on the image that likely belong to line segments. Then, a fast line growing algorithm is applied based on the Bresenham algorithm, which is combined with a modified version of the mean shift algorithm to provide accurate line segments while being robust against noise. The performance of this strategy is tested for a wide variety of images, comparing its results with popular state-of-the-art line segment detection methods. The results show that our proposal outperforms these works considering simultaneously accuracy in the results and processing speed.

BIB_text

@Article {
author = {Marcos Nieto and Carlos Cuevas and Luis Salgado and Narciso García},
title = {Line segment detection using weighted mean shift procedures on a 2D slice sampling strategy},
journal = {Pattern Analysis & Applications},
pages = {149-163},
volume = {14},
keywds = {
Line segment – Eigenvalues – Real time – Slice sampling – Mean shift – Bresenham algorithm
}
abstract = {
A new line segment detection approach is introduced in this paper for its application in real-time computer vision systems. It has been designed to work unsupervised without any prior knowledge of the imaged scene; hence, it does not require tuning of input parameters. Although many works have been presented on this topic, as far as we know, none of them achieves a trade-off between accuracy and speed as our strategy does. The reduction of the computational cost compared to other fast methods is based on a very efficient sampling strategy that sequentially proposes points on the image that likely belong to line segments. Then, a fast line growing algorithm is applied based on the Bresenham algorithm, which is combined with a modified version of the mean shift algorithm to provide accurate line segments while being robust against noise. The performance of this strategy is tested for a wide variety of images, comparing its results with popular state-of-the-art line segment detection methods. The results show that our proposal outperforms these works considering simultaneously accuracy in the results and processing speed.
}
date = {2011-04-09},
year = {2011},
}
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