Ceramic Texture Extraction Method Based on Convolution Neural Network and Image Processing

Ceramic texture extraction is a key task in the field of ceramic analysis and identification. Traditional texture extraction methods are time-consuming and require manual intervention. However, with the advancement of technology, a new method based on convolutional neural networks (CNN) and image processing has been developed. This method involves training CNN models on large datasets of ceramic images to extract unique features of ceramic textures. Then, the trained model is used to classify the new image into different categories based on its texture features. The use of cellular neural networks in this method allows for automatic feature extraction, which eliminates the need for manual intervention. In addition, image processing techniques such as edge detection and threshold processing are used to enhance the quality of the extracted texture. Overall, this new method provides an efficient and accurate method for extracting ceramic textures. It has enormous potential in various applications such as archaeological research, art conservation, and forensic science. This technology is based on the idea that images are composed of patterns called textures. Then use a set of filters or kernels to extract these patterns. In this article, we will explain how these filters work and how they can be applied to images.