Impact perforation image processing using a neural network

The evaluation of material's characteristics from the impact perforation images has been studied in the material engineering fields. In this method, the steel ball is shot into the material specimen, and the characteristic of the material is estimated from the steel ball's behavior. However, the observation of steel ball's behavior is often difficult because of the scattered fragments of the specimen. We have proposed to use the neural network to estimate the steel ball position in the impact perforation image. However, the miss-recognition of the steel ball was often seen because of the influence on the scattered fragments of the specimen. In this study, the preprocessing of the image with the high-pass filter is introduced to improve the performance of the recognition of the steel ball. We examine two types of filters using the Harming window and the Blackman window.