Evaluation of the Radon Transform for Line Detection Applications
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Date
2020-01-01
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Department
Computer Science and Electrical Engineering
Program
Computer Science
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This item may be protected under Title 17 of the U.S. Copyright Law. It is made available by UMBC for non-commercial research and education. For permission to publish or reproduce, please see http://aok.lib.umbc.edu/specoll/repro.php or contact Special Collections at speccoll(at)umbc.edu
This item may be protected under Title 17 of the U.S. Copyright Law. It is made available by UMBC for non-commercial research and education. For permission to publish or reproduce, please see http://aok.lib.umbc.edu/specoll/repro.php or contact Special Collections at speccoll(at)umbc.edu
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Abstract
We evaluate a modified Approximate Discrete Radon Transform (ADRT) for line detection applications. A traditional method for straight line detection is available in the Hough Transform and its variants such as Probabilistic Hough Transform. However, to achieve acceptable performance, the Hough Transform typically applies a binary threshold which decimates the strength of the gradient magnitude, an informative quantity for precise determination of edge-line intensity. In many practical images, it is difficult or impossible to obtain an acceptable threshold for edge detection such that all prominent lines are detected without introducing major artifacts. The Radon Transform overcomes these limitations by performing line detection efficiently in the original Sobel transform thereby preserving gradient magnitude intensity. The Radon Transform has been rarely applied to the detection of straight lines in images because it is often erroneously misunderstood that the forward Radon Transform is inefficient to calculate for these purposes. However, the ADRT is highly efficient to calculate over images due to dynamic programming, which yields O(N^2lgN) speed in computation for an NxN image (of N^2 pixels). Parallelizing this method can reduce computational steps to O(lgN) on O(N^2) processors. We apply and evaluate the ADRT algorithm for detection of straight lines in images over modern datasets, and additionally introduce a novel filtering scheme for detecting local maxima which correspond to line theta detections. Furthermore, we show that application of blurring and non-maximal suppression to the resulting images strengthens peak intensity thereby improving the ability to detect faint lines. The performance of our method is evaluated against traditional line detection algorithms such as the Hough Transform for which we consider the Radon Transform to be a direct improvement, as well as more recent methods such as the Line Segment Detector (LSD). Experimental results suggest that ADRT achieves better detection of faint lines and processing time as compared to the Hough Transform and is more comparable to state-of-the-art techniques in accuracy. We conclude that the ADRT is a mathematically sound method and improvement over the traditional Hough Transform for straight-line detection in images and eliminates the need to decimate the gradient with binary thresholds.