Recognition of Eudiscoaster and Heliodiscoaster using SOM Neural Network - Raid AL-Nima (Al-Rafidain Journal of Computer Sciences and Mathematics)
This research is aimed to design an Eudiscoaster and Heliodiscoaster recognition system. There are two main steps to verify the goal. First: applying image processing techniques on the fossils picture for data acquisition. Second: applying neural networks techniques for recognition. The image processing techniques display the steps for getting a very clear image necessary for extracting data from the acquisition of image type (.jpg). This picture contains the fossils. The picture should be enhanced to bring out the pattern. The enhanced picture is segmented into            144 parts, then an average for every part can easily be computed. These values will be used in the neural network for the recognition. For neural network techniques, Self Organization Maps (SOM) neural network was used for clustering. The weights and output values will be stored to be used later in identification. The SOM network succeeded in identification and attained to (False Acceptance Rate = 15% - False Rejection Rate = 15%).