Download A Contrario Line Segment Detection by Rafael Grompone von Gioi PDF

By Rafael Grompone von Gioi

The trustworthy detection of low-level snapshot constructions is an outdated and nonetheless difficult challenge in laptop imaginative and prescient. This e-book leads a close journey throughout the LSD set of rules, a line section detector designed to be absolutely computerized. in keeping with the a contrario framework, the set of rules works successfully with out the necessity of any parameter tuning. The layout standards are completely defined and the algorithm's strong and undesirable effects are illustrated on genuine and artificial photos. the problems concerned, in addition to the recommendations used, are universal to many geometrical constitution detection difficulties and a few attainable extensions are discussed.

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For angles larger than 45 degrees, the meaning of being aligned to the edge dissolves and orthogonal to the edge becomes a better description. Due to image noise induced by acquisition and quantization, and due to imperfection of edges, it is rare to observe precision finer than p = 1/64. In practice, the range of 1/4 to 1/32 is good enough: coarser precision improves little the robustness to noise and finer precision seldom occurs [22, p. 66]. The selection of p also implies a compromise between robustness to noise and the ability to detect short line segments.

To sum up, we do not know the exact detection thresholds to take into consideration the dependency induced by the gradient computation mask, but the empirical evidence is strong enough to support the use of the same thresholds as for the independent case. 2 A measure of the sensitivity of Desolneux, Moisan, and Morel’s algorithm to the dependency of pixels. The table shows the mean number of detections in 100 white noise images of size 50 × 50 for different values of ε . ’s algorithm depends on the choice for the angular precision p, related to the angular tolerance τ by p = πτ .

2 gives some hints on extensions to handle curves more appropriately. As before, very small structures, as the central black circle, are not detected. R. 1007/978-1-4939-0575-1 4, © The Author(s) 2014 53 54 4 Experiments Fig. 1 Left: a synthetic image representing the molecule of lysergic acid diethylamide, 305×274 pixels. Right: the line segments detected by the LSD algorithm Fig. 2 Left: a synthetic image, 249×249 pixels. 3 shows three experiments on natural images. The first scene contains highly geometrical content, essentially composed of flat structures with straight borders.

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