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Title: Line detection with adaptive random samples
Authors: Gürbüz, Ali Cafer
Keywords: Line detection
Hough Transform
Tunnel Detection
Random sampling
Subsurface shape detection
Issue Date: 2011
Publisher: Tubitak Scientific & Technical Research Council Turkey
Abstract: This paper examines the detection of parameterized shapes in multidimensional noisy grayscale images. A novel shape detection algorithm utilizing random sample theory is presented. Although the method can be generalized, line detection is detailed. Each line in the image corresponds to a point in the line parameter space. The method creates hypothesis lines by randomly selecting parameter space points and tests the surrounding regions for acceptable linear features. The information obtained from each randomly selected line is used to update the parameter distribution, which reduces the required number of random trials. The selected lines are re-estimated within a smaller search space with a more accurate algorithm like the Hough transform (HT). Faster results are obtained compared to HT, without losing performance as in other faster HT variants. The method is robust and suitable for binary or grayscale images. Results are given from both simulated and experimental subsurface seismic and ground penetrating radar (GPR) images when searching for features like pipes or tunnels.
ISSN: 1300-0632
Appears in Collections:Elektrik ve Elektronik Mühendisliği Bölümü / Department of Electrical & Electronics Engineering
Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collection
TR Dizin İndeksli Yayınlar / TR Dizin Indexed Publications Collection
WoS İndeksli Yayınlar Koleksiyonu / WoS Indexed Publications Collection

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