Cement is a kind of important building material, its performance is closely related to cement hydration. With the development of computer vision and deep learning, more and more researchers begin to study the hydration state or even mining or prediction the performance of cement by the cement hydration microstructure images. However, collecting real cement hydration microstructure image was extremely costly since the amount of human effort and expertise required. In this article, a fast cement microstructure texture image synthesis method based on PixelCNN was proposed. This method used CNN to extract more implicit features of various scales and achieved a pleased result. During the generation process, an optimization method of cyclic assignment prediction is adopted to avoid convolving all pixels of the image of each convolution, and thereby increasing the speed of texture synthesis. The validity of our method is proved by comparing it with other models.


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    Titel :

    A Fast Cement Microstructure Texture Image Synthesis Method Based on PixelCNN


    Weitere Titelangaben:

    Lect. Notes Electrical Eng.


    Beteiligte:
    Liang, Jianying (Herausgeber:in) / Jia, Limin (Herausgeber:in) / Qin, Yong (Herausgeber:in) / Liu, Zhigang (Herausgeber:in) / Diao, Lijun (Herausgeber:in) / An, Min (Herausgeber:in) / Huang, Xiaosheng (Autor:in) / Duan, Runtao (Autor:in) / Zhao, Yuxiao (Autor:in)

    Kongress:

    International Conference on Electrical and Information Technologies for Rail Transportation ; 2021 October 21, 2021 - October 23, 2021



    Erscheinungsdatum :

    2022-02-19


    Format / Umfang :

    8 pages





    Medientyp :

    Aufsatz/Kapitel (Buch)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch




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