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Abstract
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| 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. | |
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International
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Si |
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JCR
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Si |
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Title
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PATTERN ANALYSIS AND APPLICATIONS |
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ISBN
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1433-7541 |
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Impact factor JCR
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1,293 |
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Impact info
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Volume
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14 |
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10.1007/s10044-011-0211-4 |
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Journal number
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2 |
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From page
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149 |
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To page
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163 |
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Month
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MAYO |
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Ranking
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