Morphological algorithms for color images based on a generic-programming approach

The main purpose of image processing and analysis is, often, to segment the image into objects in order to analyze the geometrical properties and the structure of the objects and recognize them. The analysis of the geometric objects must be quantitative, because only such an analysis and description of the geometric objects can provide a coherent mathematical framework for describing the spatial organization. The quantitative description of geometrical structures is the objective of mathematical morphology. So far, the use of such framework has allowed the development of a class of algorithms to deal with binary and grayscale images. Multi-channel processing has been of growing interest recently, especially in color image processing. In this paper, the extension of mathematical morphology to color images by treating multi-channel data as vectors are presented. An overview of several approaches to this extension using different ordering relations is also provided. Basic vector morphological algorithms are introduced based on reduced ordering. We also present experimental results and show that these algorithms can be easily mapped into the generic-programming framework