Wavelet-based multiresolution edge detection utilizing gray level edge maps

Edge detection is an important low-level vision problem. Most edge detection methods operate on an image at a single resolution and output a binary edge map. Edges within an image, however, generally occur at various resolutions, or scales, and represent transitions of different degrees, or gradient levels. Thus, single resolution edge detection methods that output binary edge maps do not always yield satisfactory results. This paper develops a multiresolution edge detection method that utilizes a multirate wavelet decomposition to generate a series of images with progressively lower edge resolution. Edges are then recursively extracted to form series of edge maps where the output is not restricted to be binary and is set to reflect the gradient level at each edge point. The series of edge maps is restricted to form a stacking edge map pyramid. In this formulation, the base (lowest level) edge map contains edges at all scales while edge pruning, based on edge scale, is performed at subsequent levels. This approach is shown to have advantages over previously defined multiresolution edge detection methods. Results are presented using natural seen images. These results are motivated by tactile imaging, an important problem in making visual information accessible to blind individuals.