Multiscale morphological image simplification

Abstract

Image simplification reduces the information content of an image, being frequently used as a preprocessing stage in several algorithms to suppress undesired details such as noise. Morphological filters, commonly used for this purpose, have as main drawbacks the asymmetric treatment of peaks and valleys and the difficulty to choose an appropriate structuring element size. Here, we propose a self-dual multiscale image simplification operator with sound edge preservation properties. This enables us to represent the inherent multiscale nature of real-world images by embedding the original signal into a family of derived signals, which represent simplified versions of the image obtained by successively removing its structures across scales. Thus, it is possible to analyze the different representation levels to extract the interest features, and the definition of a structure element size does not constitute a problem anymore. Based on these notions, we present some experiments on image segmentation, a basic step of various pattern recognition approaches. © 2008 Springer-Verlag Berlin Heidelberg.