ONE-DIMENSIONAL DIGITAL PROCESSING OF IMAGES FOR STRAIGHT-LINE DETECTION

doi:10.1016/S0031-3203(98)00046-6

Pattern Recognition

Volume 31, Issue 11, November 1998, Pages 1687-1690

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Abstract

The detection of straight lines in images is a common requirement in the recognition of some patterns. Several approaches have been in the use for many years but are computationally intensive. This paper presents a simple algorithm which can make use of the fast fourier transformer (FFT) for rapid computation and has the added feature of being able to detect straight lines of a specified length. The method herein suggests that simply unlacing the image raster before matched filtering can reduce the search range because of the restricted periodicities in the one-dimensional unlaced signal. These matched filters can be applied in the frequency domain through the use of the FFT. This note discusses the algorithm and presents example results.

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Cited by (3)

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Citation Excerpt :
The line detection algorithms based on HTLD are typically characterized by high computational costs. Hemdal has put forward an algorithm based on measuring the periodicity of lines in a single dimension by unwrapping the binary image and applying Discrete Fourier Transform but the results are applicable in a limited set of problems (Hemdal, 1998). Lagunovsky and Ablameyko have used cluster analysis methods to extract straight lines but the method is sensitive to straightness of lines (Lagunovsky and Ablameyko, 1999).
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One of the most fundamental tasks in pattern recognition involves fitting a curve such as a line segment to a given set of data points. Using the conventional ordinary least-squares (OLS) method of fitting a line to a set of data points is notoriously unreliable when the data contain points coming from two different populations: (i) randomly distributed points (“random noise”), (ii) points correlated with the line itself (e.g., obtained by perturbing the line with zero-mean Gaussian noise). Points which lie far away from the line (i.e., “outliers”) usually belong to the random noise population; since they contribute the most to the squared distances, they skew the line estimate from its correct position. In this paper we present an analytic method of separating the components of the mixture. Unlike previous methods, we derive a closed-form solution. Applying a variant of the method of moments (MoM) to the assumed mixture model yields an analytic estimate of the desired line. Finally, we provide experimental results obtained by our method.

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