By Rafael Grompone von Gioi
The trustworthy detection of low-level photograph constructions is an previous and nonetheless hard challenge in laptop imaginative and prescient. This e-book leads a close travel during the LSD set of rules, a line phase detector designed to be absolutely automated. in response 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 genuine and artificial photos. the problems concerned, in addition to the suggestions used, are universal to many geometrical constitution detection difficulties and a few attainable extensions are discussed.
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Extra resources for A Contrario Line Segment Detection
5 shows two discrete edges at different angles, both presenting the staircase effect. Next to each image is the result of LSD without using the initial scaling (center). In the first case the edge is detected as four horizontal line segments instead of one; in the second case, no line segment is detected. In both cases the result is reasonable, but it does not correspond to what we would expect. 5 (right) shows the result of LSD using the 80 % scaling. Both edges are now detected and with the right orientation (even if the first one is still fragmented).
One is to count in Ntests all the tests for different precisions. But the simpler one is to divide ε among the γ different detectors applied. Each detector would have an allowance of ε /γ false detections; summing all together, the multi-p method will produce, on average, ε false detections. 5 Continuous Angular Measurement The last section made clear the need to use different values for the precision p to have a good compromise between accuracy and robustness to noise. It is natural to attempt to handle all precisions at the same time, using continuous statistics instead of the binomial ones .
The main rectangle’s angle is set to the angle of the eigenvector associated with the smallest eigenvalue of the matrix M= muu muv muv mvv 48 3 The LSD Algorithm with muu = ∑ j∈region |∇x( j)| · (u( j) − cu )2 , ∑ j∈region |∇x( j)| mvv = ∑ j∈region |∇x( j)| · (v( j) − cv )2 , ∑ j∈region |∇x( j)| muv = ∑ j∈region |∇x( j)| · (u( j) − cu )(v( j) − cv ) . 9 NFA Computation The a contrario validation depends on the precision p, whose value is initially set to τ /π , where τ is the angular tolerance used in the region growing algorithm.