Automatic selection of edge detector parameters based on spatial and statistical measures

The basic and widely used edge detection operation in an image usually requires a prior step of setting the edge detector parameters (thresholds, blurring extent etc.). Finding the best detector parameters automatically in real-world images is a difficult challenge because no absolute ground truth exists. However, the advantage of automatic processing over manual operations done by humans motivates the development of automatic detector parameter selection. In this work, we propose an automatic detector parameter selection which considers both, statistical correspondence of detection results produced from different detector parameters, and spatial correspondence between detected edge points, represented as saliency values. The method improves a recently developed technique that employs only statistical correspondence of detection results and depends on the initial parameter range by incorporating saliency values in the statistical analysis. Automatic edge detection results show considerable improvement of the purely statistical method when a wrong initial parameter range is selected.