Pattern Recognition by Splitting Images Into Trees of Fuzzy Regions

In this paper, a new method of pattern recognition based on images splitting into a set of trees composed of fuzzy regions is presented. First, either a gradient inverse function is applied on the raster image to define the fuzzy regions supports, or we manage with the basic grey level image if regions are easily topologically separable. Then, topologic features are computed on these sets. Therefore, a tree description of the image, which consists of fuzzy regions with associated topological features, is obtained. A set of sample trees is achieved from the application of the fuzzy segmentation algorithm on characteristic objects (small images). Then a tree isomorphism is defined to recognize a particular object in an image. At last, a new tree compression method is introduced in order to decrease the complexity when we have to manage with a large set of samples.