期刊
PATTERN ANALYSIS AND APPLICATIONS
卷 14, 期 2, 页码 149-163出版社
SPRINGER
DOI: 10.1007/s10044-011-0211-4
关键词
Line segment; Eigenvalues; Real time; Slice sampling; Mean shift; Bresenham algorithm
资金
- Ministerio de Ciencia e Innovacion of the Spanish Government [TEC2007-67764]
- Comunidad de Madrid [S-0505/TIC-0223]
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.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据