The automated star/galaxy discrimination problem is a broadly studied issue of astronomical imaging. The success of a discrimination task depends on the features selected to characterize both classes of interest. In this work we propose an original approach to the characterization of these astronomical objects through the use of Mathematical Morphology, based on gray-level shape-size information. Our method consists of image preprocessing, segmentation and feature extraction steps, all of which employ Mathematical Morphology tools that were implemented using the MMach toolbox for the Khoros system. We briefly present a comparison between our segmentation results, based on the watershed method, and those of the SExtractor software package. The shape-size features are extracted through the use of the gray-level morphological pattern spectrum, which yields attractive analysis attributes that promise to be very suitable for future work in neural-network-based automatic classification
Star/Galaxy discrimination; gray-level morphology; granulometry; image preprocessing; segmentation; feature extraction
