Time lagged recurrent neural network for temporal gene expression classification
International Journal of Computational Intelligence in Bioinformatics and Systems Biology
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Classification and identification of galaxy shape is an important issue for astronauts since it provides valuable information about the origin and the evolution of the universe. Statistical invariant features that are functions of moments have been used as global features of galaxy images in their pattern recognition. In this paper, an automated training based recognition system that can compute the statistical invariant features for different galaxy shapes is investigated. The proposed algorithm is robust, regardless of orientation, size and position of the galaxy inside the image. Feature vectors are computed via nonlinear moment invariant functions for each galaxy shape. After feature extraction, the recognition performance of classifier in conjunction with these moment---based features is introduced. Computer simulations show that Galaxy images are classified with an accuracy of about 90% compared to the human visual classification system.