(Retracted) Role of video sensors in observing visual image design in the construction of smart cities

Abstract

Due to the advancement of software and hardware technologies such as geographic information systems, for example, optical fiber storage, mobile network, digital camera, and high-definition network, it has promoted the establishment of a data digital management system. Network sharing, to a certain extent, makes it the smart city video sensor of today's era. It is a key component of the city's smart management system. In the context of the rapid development of the information age, I explore the role of video sensors in the visual design of smart cities. I propose three methods to analyze the theory of big data video sensors: theoretical analysis of big data video sensors, information visualization, and visual signal output, and then model the video signal output to visualize the complex multilayer network decision. Starting from the RBF neural network, I simulate a video perception prediction model system, import the data into Matlab for experimental analysis and simulation, and compare RBF and BP are based on both data predictions. RBF obviously has stronger predictive video perception. Experimental data show that the error between video perception and actual data is within 3%. It can be seen that video sensors play a key role in the construction of smart cities.

Digital Visual Design Reengineering and Application Based on K-means Clustering Algorithm

“…Applying intelligent visual gene extraction methods to digital design reengineering problems, constructing a multi-system digital visual graphic gene extraction model, and carrying out intelligent digital design reengineering methods [6] are increasingly being paid attention to and researched by experts in the field [7]. Digital visual design reengineering graphic visual gene extraction methods are divided into color system extraction [8], graphic line body extraction [9], texture feature extraction [10] and other methods from the perspective of the type of gene extraction. Literature [11] proposes a color system extraction method based on K-means clustering algorithm, and constructs a color database of aesthetic artifacts; literature [12] adopts an image processing method, extracts the mural color system from the color three-channel, and puts forward a gene extraction method based on machine learning algorithms for the digital visual design recreation; literature [13] studies the color particles of the mural paintings, and through the particle swarm optimization algorithm Improved K-means clustering analysis of color features; Literature [14] uses deep learning methods to extract and learn graphic shapes, and constructs a digital reengineering model of multi-dimensional linear structure shapes; Literature [15] improves the K-means clustering method to digitally extract texture representations by using the peak density strategy, and at the same time, uses a self-coder neural network to construct the artifacts graphic texture expression model ; Literature [16] combines the gray wolf optimization algorithm, K-means algorithm and convolutional neural network method table mural texture aesthetics for feature extraction and reengineering representation; Literature [17] combines the color characteristics of aesthetic artifacts and line characteristics, to build a digital visual design reengineering evaluation system.…”