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

By Rafael Grompone von Gioi

ISBN-10: 1493905740

ISBN-13: 9781493905744

The trustworthy detection of low-level snapshot constructions is an outdated and nonetheless tough challenge in desktop imaginative and prescient. This ebook leads an in depth journey during the LSD set of rules, a line phase detector designed to be absolutely computerized. in accordance 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 solid and undesirable effects are illustrated on actual and artificial photographs. the problems concerned, in addition to the thoughts used, are universal to many geometrical constitution detection difficulties and a few attainable extensions are discussed.

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Extra info for A Contrario Line Segment Detection

Example text

An ), where a j take the values zero or one according to whether the j-th pixel is τ -aligned or not. In other words, the angle information is quantized with precision τ . Alternatively, we can keep the level-line angle information as is. Let us normalize the angles between 0 and 1, zero meaning a perfect alignment and one meaning the level-line is opposed to the line segment. Now a j take values in the full interval [0, 1]. The space of possible configurations (a1 , . . , an ) defines a unit hypercube of n dimensions with one vertex at the origin, see Fig.

10 shows an example of a line-support region (in gray) and the rectangle corresponding to it. This line-support region could be better interpreted as two thinner rectangles, one longer than the other, forming an obtuse angle. As these two thin rectangles are aligned one to the other up to a tolerance τ , all the pixels of both rectangles are grouped into one line-support region. In LSD this problem is handled by detecting problematic line-support regions and cutting them into two smaller regions, hoping to cut the region at the right place to solve the problem.

In some cases, some simple variations of the initial rectangle can improve the detectability, step 12. 11 describes the variations tried. 9 discusses the computation of the NFA value and the threshold ε . Finally, Sect. 12 gives an analysis of the computational complexity of the LSD algorithm. LSD was designed as an automatic image analysis tool. As such, it must work without requiring any parameter tuning. The algorithm actually depends on several quantities that determine its behavior; but their values were carefully devised to work on all images.

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