esearch on the Application of Computer Visual Technology to Enhance Cultural Inheritance and National Identity in Ethnic Pattern Design Courses

In this paper, first of all, the data preprocessing of ethnic patterns is carried out through image segmentation and grayscaling processing methods, and then the image processing technology is applied to the feature extraction of ethnic dress patterns, and the improved SIFT algorithm is used for the feature extraction of images.The original DCGAN algorithm feature extraction ability is weak generates style picture fuzzy, the effect of the problem of poor, proposed the use of 32-layer deep neural network with residual structure instead of the original 5-layer shallow feature extraction network, significantly enhanced the algorithm's feature extraction ability, enhance the model of the style migration effect.By introducing the objective evaluation index PA of the improved SIFT algorithm, the algorithm was compared with other algorithms, and the segmentation algorithm experiments were carried out with the local patterns of several images, and the results of pixel accuracy PA were obtained to be greater than 0.95, which confirmed that the improved SIFT algorithm was able to realize the accurate extraction of the contours of local patterns.In terms of pattern quality evaluation dimension, the subjective average scores of the amateur group and the expert group are 4.87 and 4.89 respectively, indicating that the ethnic patterns generated by the algorithm of this paper have reached a high standard in quality.