An adaptive approach to scale selection for line and edge detection

One of the standard problems of edge- and line-detecting algorithms is to determine the most appropriate size of the convolution-operator for the particular task, maximising the conflicting goals of resolution and sensitivity. Here we suggest a novel approach to scale selection, where the scale size varies dynamically with the convolution output: the stronger the output, the smaller the spatial scale. This principle has been applied to two types of feature-detection algorithms, and shown to perform well for both one- and two-dimensional images.