Cement Texture Synthesis Based on Feedforward Neural Network

Abstract

Texture is of great significance to the study of cement field. It can reflect various information, such as cement strength and hydration age. However, the texture of cement hydration image is complex and diverse, and most of the methods are relatively inefficient at present. Therefore, we propose a fast way to synthesize texture through neural network. It uses the information of the causal neighborhood to extract their implicit features. This method is more perfect than the simple expression method, and can extract more implicit features and get a better neural network model. Through this model we can quickly and easily synthesize cement texture images. This algorithm is faster than the current popular methods and more diverse than the methods of gene expression programming.