Morphological scale-space in image processing
The purpose of this paper is twofold. First, we provide an extensive review of the state-of-the-art in scale-space generation techniques for image processing, including linear methods, diffusion-based methods, and emphasizing morphological methods. Then, we introduce a new morphological approach to scale-space, called the lomo scale-space. The technique introduces a novel two-dimensional generalization of the concept of locally monotonic (lomo) signals. The lomo scale-space is a sequence of locally monotonic image representations where the scale is specified by the spatial extent or degree of local monotonicity. The morphological process used to generate the lomo scale-space retains many desirable properties of other morphological methods, such as edge localization and smoothing of extrema. In contrast to previous morphological scale-space methods, the filters employed here are self-dual, and thus do not induce a gray-level bias into the scaled signal representations. The scale-space methods reviewed and introduced in this paper are applicable to several multiscale image processing tasks such as segmentation, object-based image compression, content-based retrieval, and video tracking.
morphology scale-space non-linear filters