A Contrario Line Segment Detection by Rafael Grompone von Gioi

By Rafael Grompone von Gioi

The trustworthy detection of low-level picture constructions is an previous and nonetheless difficult challenge in machine imaginative and prescient. This booklet leads an in depth travel in the course of the LSD set of rules, a line phase detector designed to be totally computerized. according to the a contrario framework, the set of rules works successfully with no the necessity of any parameter tuning. The layout standards are completely defined and the algorithm's strong and undesirable effects are illustrated on actual and artificial photographs. the problems concerned, in addition to the concepts used, are universal to many geometrical constitution detection difficulties and a few attainable extensions are discussed.

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2 is not valid as adjacent pixels necessarily have dependent gradient angles. It is still possible to build an a contrario method, but a theoretical bound for the number of false detections under the dependency model is much harder to get. However, numerical experiments on white noise show that the number of false detections does not blow up when all pixels in the discretization are kept, and we assume independence. This is an empirical verification that the nonaccidentalness principle is still valid without any down-sampling.

If the pixels are well aligned, using a finer precision will keep the same number of aligned points, but a smaller p yields a smaller (and therefore better) NFA value. In a similar way, it only makes sense to try to reduce the rectangle’s width because the initial width was chosen to cover the whole line-support region. Often, reducing the width by one pixel may reduce the number of aligned pixels by only a few units while reducing the total number of pixels by a number equal to the length of the rectangle, see Fig.

5. LSD is a greedy algorithm. A Status variable is assigned to each pixel to keep track of the ones still “Available” to form line-support regions and the ones already used or discarded, thus “Unavailable”. Step 3 discards all the pixels whose gradient magnitude is less than or equal to ρ , rejecting image flat zones and pixels highly affected by quantization noise, see Sect. 6. Line-support regions are obtained by a region growing algorithm, step 6. Starting from a seed pixel P, neighbor pixels are iteratively added if their level-line angles correspond to the one of the region up to a tolerance τ .

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