We show that, in images of man-made environments, the horizon line can usually be hypothesized based on a-contrario detections of second-order grouping events. This allows constraining the extraction of the horizontal vanishing points on that line, thus reducing false detections. Experiments made on three datasets show that our method, not only achieves state-of-the-art performance w.r.t. horizon line detection on two datasets, but also yields much less spurious vanishing points than the previous top-ranked methods.
CITATION STYLE
Simon, G., Fond, A., & Berger, M. O. (2018). A-contrario horizon-first vanishing point detection using second-order grouping laws. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 11214 LNCS, pp. 323–338). Springer Verlag. https://doi.org/10.1007/978-3-030-01249-6_20
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