We propose a multiscale extension of a well-known line segment detector, LSD. We show that its multiscale nature makes it much less susceptible to over-segmentation and more robust to low contrast and less sensitive to noise, while keeping the parameter-less advantage of LSD and still being fast. We also present here a dense gradient filter that disregards regions in which lines are likely to be irrelevant. As it reduces line mismatches, this filter improves the robustness of the application to structure-from-motion. It also yields a faster detection.
CITATION STYLE
Salaün, Y., Marlet, R., & Monasse, P. (2017). The multiscale line segment detector. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 10214 LNCS, pp. 167–178). Springer Verlag. https://doi.org/10.1007/978-3-319-56414-2_12
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