#Human brain 3d model ceramic portable#
Therefore, integrating modern science and technology to develop an automatic portable device for the general public that can quickly identify ancient ceramics and perform basic evaluation is not only a hot spot of public concern but also a research topic in the industry.
#Human brain 3d model ceramic series#
But the active market also brings a series of problems for example, consumers suffer from price fraud due to the lack of background knowledge of ancient ceramics. The development of the domestic national economy also means that people’s spiritual civilization market will become more and more active, and the soaring market price of blue and white porcelain in the Ming and Qing Dynasties has repeatedly verified this view.
Studying this period of history and investigating blue and white porcelain samples from this period can obtain a lot of historical information and ancient ceramic technology information. Ming and Qing blue and white porcelains have always been the mainstream varieties of ancient porcelain. Since entering the 21st century, with the increasing progress of the country’s big data, Internet+, and intelligent manufacturing technology, various technologies in the ceramic light industry have been vigorously developed, and the ancient ceramic industry is no exception. This dataset minimizes the difference between the same type of base text in the same period to lay the foundation for good big data recognition in the future. Therefore, taking into account the requirements for future big data experimental training, this article mainly uses jpg/csv two standardized database datasets after segmentation. And the dataset extracted by the classification method used in this paper accounts for 20% of the total dataset, and at the same time, the PSNR value of 0.1 is improved on average. A super-resolution model for certain texture features can improve the restoration effect of such texture images. This method is not very sensitive to the relative position, density, spacing, and thickness of the text. The production of big data that meets the characteristics of the background text is actually an image-based normalization method. Under the condition of the same sampling rate, this algorithm can retain more image texture details and big data than the algorithm. In compressed sensing, to further increase each feature component, the sparseness of tight framework wavelet-based shearlet transform is constructed and combined with wave atoms as a joint sparse dictionary big data. This paper uses the layered variational image decomposition method to decompose the image into structural components and texture components and uses a compressed sensing algorithm based on hybrid basis to reconstruct the structure and texture components with large data. This paper conducts a systematic research on image decomposition based on variational method and compressed sensing reconstruction of convolutional neural network. It maps the image to a suitable space and can effectively decompose the image structure, texture, and noise.
The texture image decomposition of porcelain fragments based on convolutional neural network is a functional algorithm based on energy minimization.