The morphological lomo filter for multiscale image processing

Locally monotonic (lomo) images are defined as root signals of a morphological lomo filter. The morphological approach allows a multidimensional generalization of local monotonicity. This generalization is well motivated in that it retains the essential properties of one-dimensional (1D) local monotonicity. Repeated application of the lomo filter produces a lomo root signal of a specified scale. By filtering at multiple scales, a locally monotonic scale-space can be created and used in multiscale image applications such as segmentation, tracking, content based retrieval, and image coding. In contrast to existing linear and nonlinear scale-generating filters, the lomo filter has no spatial or graylevel bias and preserves edge localization through scale-space.