DocumentCode :
2394693
Title :
An improved sampling strategy for randomized hough transform based line detection
Author :
Xiaolan Shen ; Jiangxin Zhang ; Shengfeng Yu ; Limin Meng ; Du, K.-L.
Author_Institution :
Enjoyor Labs., Enjoyor Inc., Hangzhou, China
fYear :
2012
fDate :
19-20 May 2012
Firstpage :
1874
Lastpage :
1877
Abstract :
Detecting lines correctly from a digital image is an important step in many real-world applications. It has been widely used in the fields of contour extraction, character recognition and medical image analysis, as well as in many other computer vision based applications. In this paper, we present a randomized Hough transform based line detection algorithm that utilizes the edge gradient direction. This method exploits edge gradient direction to determine the main direction of a line by applying a constraint on the randomized Hough transform. It substantially reduces the count of invalid samples in the random sampling process. The proposed sampling strategy is superior to some existing methods in terms of memory requirement and computation time.
Keywords :
Hough transforms; feature extraction; gradient methods; object detection; signal sampling; character recognition; computer vision based applications; contour extraction; digital image; edge gradient direction; medical image analysis; randomized Hough transform based line detection; real-world applications; sampling strategy; Algorithm design and analysis; Computational complexity; Image edge detection; Memory management; Probability; Transforms; Vectors; edge gradient direction; line detection; randomize Hough transform; sampling strategy;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Systems and Informatics (ICSAI), 2012 International Conference on
Conference_Location :
Yantai
Print_ISBN :
978-1-4673-0198-5
Type :
conf
DOI :
10.1109/ICSAI.2012.6223412
Filename :
6223412
Link To Document :
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