Research on Image Texture Feature Extraction Based on Digital Twin

Abstract

The purpose of image smoothing is to improve the visual effect of the image and improve the clarity of the image, so as to make the image more conducive to computer processing and various feature analysis. Because the current technology fails to smooth the preprocessed image, it leads to the extraction of image texture features. The anti-interference performance is weak. For this reason, an image texture feature extraction technology based on the digital twin is proposed. Similarity analysis is carried out through the internal structure of the image, and the image is smoothed by the semisupervised learning method. On the basis of optimizing the denoised image through digital twinning, detect target feature points in the original image, then remove the abnormal and split feature points, assign the direction of image texture feature points, and build a fuzzy back propagation neural network model. Image texture feature extraction technology is implemented. The experimental results show that, compared with the traditional method, the proposed technique has a strong identification of original image features, and has a strong consistency with original data, and has a strong ability to resist the influence of abnormal data, noise, or redundant feature points.

Research on the Design of Wedding Supplies from the Perspective of Aesthetic Preferences

“…In this process, "0" and "1" are determined as dummy variables [9] , representing the "absence" and "presence" of the elements respectively. Based on this transformation, the design element coding table of the samples is created using {0, 1} assignment [10] .…”

The Identification of Beef and Pork Using Neural Network Based on Texture Features

“…Research Online First Article 2 Until this decade, many methods have been presented to analyze image texture. In general, this method presents a technique for extracting repetition patterns that are presented with numbers so that they can be used for machine learning processes (Li, 2022). Features are unique identity belong to an image.…”