Morphological scale-space preserving transforms in many dimensions

The theory of an image decomposition that we refer to as a sieve is developed for images defined in any finite number of dimensions. The decomposition has many desirable properties including the preservation of scale-space causality and the localization of sharp-edged objects in the transformation domain. The decomposition has the additional properties of manipulability, which means that it is easy to construct pattern recognition systems, and scale-calibration, which means that it may be used for accurate measurement.