Automated detection and classification of galaxies based on their brightness patterns - Mohamed Eassa (Elsevier, 2022)
Clues and traces of the universe's origin and its developmental process are deeply buried in galaxy shapes and formations. Automated galaxies classification from their images is complicated due to the faintness of the galaxy images, conflicting bright background stars, and image noise. For this purpose, the current work proposes a novel logically structured modular algorithm that analyses galaxy morphological raw brightness data to automatically detect galaxy visual center, region, and classification. First, a novel selective brightness threshold is employed to eliminate the effect of bright background stars on detecting galaxy visual centers. Second, a galaxy region detection technique is developed. Finally, a novel technique based on galaxy brightness variation patterns is employed for galaxies classification. The current work has been tested with a run on a collection of 1000 galaxies from the EFIGI catalog. Results demonstrated a success rate of 97.2% in galaxies classification with an average processing time of 0.37 s per galaxy. The high success rates and the low processing time proved the efficiency of the proposed work.