A transform for multiscale image segmentation by integrated edge and region detection

Describes a transform to extract image regions at all geometric and photometric scales. It is argued that linear approaches have the shortcoming that they require a priori models of region shape. The proposed transform avoids this by letting the structure emerge, bottom-up, from interactions among pixels. The transform involves global computations on pairs of pixels followed by vector integration of the results. An attraction force field is computed over the image in which pixels belonging to the same region are mutually attracted and the region is characterized by a convergent flow. It is shown that the transform possesses properties that allow multiscale segmentation, or extraction of original, unblurred structure at all different geometric and photometric scales present. This is in contrast with much previous work wherein multiscale structure is viewed as the smoothed structure in a multiscale signal decimation. Scale is an integral parameter of the force computation, and the number and values of scale parameters associated with the image can be estimated automatically. Regions are detected at all a priori unknown scales resulting in automatic construction of a segmentation tree, in which each pixel is annotated with descriptions of all the regions it belongs to. Transform properties are presented for piecewise-constant images but hold for more general ones. Thus the proposed method is intended as a solution to the problem of multiscale, integrated edge and region detection, or low-level image segmentation. Experimental results are given.