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.
AN ADAPTIVE APPROACH TO SCALE SELECTION FOR LINE AND EDGE-DETECTION
MORRONE, MARIA CONCETTA;
1995-01-01
Abstract
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.File in questo prodotto:
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