An extension to the randomized hough transform exploiting connectivity |
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| Affiliation: | 1. Shanghai Key Laboratory of Intelligent Manufacturing and Robotics, HC206A, Shanghai University, 99 Shangda Road, BaoShan District, Shanghai, China;2. Department of Aerospace Engineering, Ryerson University, 350 Victoria Street, Toronto, ON M5B 2K3, Canada;1. MOE Key Laboratory of Laser Life Science & Institute of Laser Life Science, College of Biophotonics, South China Normal University, Guangzhou 510631, PR China;2. Department of Pain Management, The First Affiliated Hospital of Jinan University, Guangzhou 510630, PR China;1. MOE Key Laboratory of Laser Life Science & Institute of Laser Life Science, College of Biophotonics, South China Normal University, Guangzhou 510631, China;2. Key Laboratory of Optoelectronic Devices and Systems of Ministry of Guangdong Province, and Shenzhen Key Laboratory of Micro-Nano Biomedical Optical Detection and Imaging, Shenzhen University, Shenzhen 518060, China;1. Department of Automation, University of Science and Technology of China, Hefei 230027, China;2. The State Key Laboratory for Management and Control of Complex Systems, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China;3. College of Information Science and Technology, Beijing University of Chemical Technology, Beijing 100029, China;4. University of Science and Technology Beijing, Beijing 100083, China;5. North China University of Technology, Beijing 100144, China |
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| Abstract: | Finding global curve segments in an image is an important task. For such a task, a new branch of Hough Transform algorithms, called probabilistic Hough Transforms, has been actively developed in recent years. One of the first was a new and efficient probabilistic version of the Hough Transform for curve detection, the Randomized Hough Transform (RHT). In this paper, a novel extension of the RHT, called the Connective Randomized Hough Transform (CRHT), is suggested to improve the RHT for line detection in complex and noisy pictures. The CRHT method combines the ability of the Hough Transform for global feature extraction with curve fitting techniques by exploiting the connectivity of local edge image points. Tests demonstrate the high speed and low memory usage of the CRHT, as compared both to the Standard Hough Transform and the basic RHT. |
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