Generalized morphological pattern spectrum for classification

One of the most important problems in computer vision and image processing is signal representation and shape description. This usually involves mapping the signal from an original domain to a set of coefficients describing feature contents of the signal. These methods can be classified in two, global and local, categories. Global methods are sensitive to inaccurate segmentation and masking effects. Local methods, on the other hand, are better in partial pattern recognition but are generally application-dependent. Generalized pattern spectrum (GPS) is a local method and it has proved to be a robust shape descriptor which can also be used in partial pattern recognition. We have investigated the classification properties of this mapping on both noisy and partially missed objects. The results show that it is superior to the pecstrum method in the case of partially missed patterns