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Fast line and object segmentation in noisy and cluttered environments using relative connectivity

: Teutsch, Michael; Schamm, Thomas

Volltext urn:nbn:de:0011-n-1927489 (1.8 MByte PDF)
MD5 Fingerprint: a460b0a4c76f3c47fbf304cbeb6c9b06
Erstellt am: 26.1.2012

Arabnia, H.R.:
International Conference on Image Processing, Computer Vision, & Pattern Recognition, IPCV 2011. Proceedings. Vol.2 : Affiliated with WORLDCOMP '11; July 18 - 21, 2011, Las Vegas, Nevada, USA
Las Vegas: CSREA Press, 2011
ISBN: 1-60132-189-9
ISBN: 1-60132-190-2
ISBN: 1-60132-191-0
International Conference on Image Processing, Computer Vision, and Pattern Recognition (IPCV) <15, 2011, Las Vegas/Nev.>
World Congress in Computer Science, Computer Engineering, and Applied Computing (WORLDCOMP) <2011, Las Vegas/Nev.>
Konferenzbeitrag, Elektronische Publikation
Fraunhofer IOSB ()
line patterns hough transform; LPHT; line detection; line extraction; foreground background segmentation

In applications such as 3D plane segmentation of road traffic environments using u/v-disparity-histograms, line extraction is a key component and has to be as fast and precise as possible. Hough Transform is a good way to detect straight lines but specific line segments limited by start and end points are still to be determined. The Line Patterns Hough Transform (LPHT) introduced by Yip[1] directly delivers potential start and end points using the principle of relative connectivity. But this approach poses some challenges, too. We modified his idea to use Standard Hough Transform (SHT) together with relative connectivity for a fast and robust line segment extraction even in environments strongly affected by noise and clutter. Furthermore, we demonstrate the benefit of modified LPHT and relative connectivity for object segmentation in noisy Synthetic Aperture Radar (SAR) or infrared (IR) data.