An analysis of feature detectability from curvature estimation.

A common approach to finding features in digitized lines is to estimate the curvature along the lines and determine the features from the curvature plot. We compare two approaches to curvature estimation by analyzing performance with respect to signal-to-noise ratio and signal localization of corner features. One approach, curvature estimation by difference of slope estimates which yields optimum signal-to-noise ratio. The other approach, Gaussian smoothing of the second line derivative, is compared with the difference of slopes method and found to yield poorer signal localization for low signal-to-noise ratio. Besides analytical comparisons, the methods are tested and compared for lines containing chosen corner angles and random noise. These empirical comparisons corroborate the analytical results.