Abstract
A statistical signal detection theory approach can be used for curve detection in digital images corrupted by random noise. This approach is shown to be a refinement of the frequently used Hough transform, resulting in improved performance, both in deciding the presence or absence of a curve in the image and in determining the location in the image of an existing curve. Detection performance is evaluated using receiver operating characteristics (ROC) curves. Location estimation performance is measured by deriving equations for both the Hough transform and the signal detection theory approach for the probability of correctly estimating the location of a curve in noise. The performances of these two approaches are compared for various signal-to-noise ratios and found to be significantly different for some values of signal-to-noise ratio.
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