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A Self-Organizing Map Based Hough Transform for Detecting Straight Lines  

Lee, Moon-Kyu (Faculty of Mechanical and Automotive Engineering, Keimyung University)
Publication Information
Journal of Korean Institute of Industrial Engineers / v.28, no.2, 2002 , pp. 162-170 More about this Journal
Abstract
Detecting straight lines in an image is frequently required for various machine vision applications such as restoring CAD drawings from scanned images and object recognition. The standard Hough transform has been dominantly used to that purpose. However, massive storage requirement and low precision in estimating line parameters due to the quantization of parameter space are the major drawbacks of the Hough transform technique. In this paper, to overcome the drawbacks, an iterative algorithm based on a self-organizing map is presented. The self-organizing map can be adaptively learned such that image points are clustered by prominent lines. Through the procedure of the algorithm, a set of lines are sequentially detected one at a time. The algorithm can produce highly precised estimates of line parameters using very small amount of storage memory. Computational results for synthetically generated images are given. The promise of the algorithm is also demonstrated with its application to two natural images of inserts.
Keywords
line detection; hough transform; self-organizing map;
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