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A Contrario Line Segment Detection [electronic resource] / by Rafael Grompone von Gioi.

By: Grompone von Gioi, Rafael [author.].
Contributor(s): SpringerLink (Online service).
Material type: materialTypeLabelBookSeries: SpringerBriefs in Computer Science: Publisher: New York, NY : Springer New York : Imprint: Springer, 2014Description: VIII, 90 p. 60 illus. online resource.Content type: text Media type: computer Carrier type: online resourceISBN: 9781493905751.Subject(s): Computer science | Artificial intelligence | Computer graphics | Computer Science | Computer Imaging, Vision, Pattern Recognition and Graphics | Artificial Intelligence (incl. Robotics)Additional physical formats: Printed edition:: No titleDDC classification: 006.6 Online resources: Click here to access online
Contents:
Introduction -- A Contrario Detection -- The LSD Algorithm -- Experiments -- Extensions.
In: Springer eBooksSummary: The reliable detection of low-level image structures is an old and still challenging problem in computer vision. This book leads a detailed tour through the LSD algorithm, a line segment detector designed to be fully automatic. Based on the a contrario framework, the algorithm works efficiently without the need of any parameter tuning. The design criteria are thoroughly explained and the algorithm's good and bad results are illustrated on real and synthetic images. The issues involved, as well as the strategies used, are common to many geometrical structure detection problems and some possible extensions are discussed.
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Introduction -- A Contrario Detection -- The LSD Algorithm -- Experiments -- Extensions.

The reliable detection of low-level image structures is an old and still challenging problem in computer vision. This book leads a detailed tour through the LSD algorithm, a line segment detector designed to be fully automatic. Based on the a contrario framework, the algorithm works efficiently without the need of any parameter tuning. The design criteria are thoroughly explained and the algorithm's good and bad results are illustrated on real and synthetic images. The issues involved, as well as the strategies used, are common to many geometrical structure detection problems and some possible extensions are discussed.

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