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@ {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 },

year = {2011-04-09 },

keys = {Line segment – Eigenvalues – Real time – Slice sampling – Mean shift – Bresenham algorithm },

pages = {149- 163 },

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. },

issn = {1433-7541 },

in = {Pattern Analysis & Applications },

}