Image feature extraction using mathematical morphology

Mathematical morphology (MM) is a very efficient tool for image processing, based on non- linear operators.In this paper MM is applied to extract the image's features. As a feature we understand specific information about the image i.e. location, size, orientation of certain image elements. Morphological operators are applied to find and measure objects on the image's surface. Two practical examples are considered. First is devoted to analysis of binary images, containing printed characters. Characters are separated and MM is used to extract some information from each character. These features are later measured and included in a feature vector. It contains the special kind of information - the number of elements of the character with its shape modified in different ways. Second examples shows how feature extraction by MM works on graytone images. Images for analysis contain human faces. Morphological operators extract some important elements of human face. This information is very important to identify the human face. Experiments show us how the morphological operators can be applied to the feature detection. The simplest operators as erosion and dilation, as well as more sophisticated tools like: morphological filtering, geodesic transformations are used for that purpose. Also directional operations are applied to extract some areas. This paper includes algorithms for feature extraction by MM, as well as the brief description of morphological tools, explication of experiments and the results of them.