3D Image Based on Visual Sensor in Public Space Landscape Model

Abstract

With the rapid development of the times, the computing of the internet of things and the industry go hand in hand, the application of visual sensors is becoming more and more extensive, and the image fusion technology and target recognition technology have been further improved. It can be said that it is a hot topic at home and abroad high-end technology. With the continuous improvement of people’s living standards, the specific requirements for the quality of life are gradually increasing. The landscape construction of public space has become a topic of discussion. People are becoming more and more stringent about the landscape environment around them. Landscape designers also feel distressed by this and pursue this higher level, which can not only improve the living environment, but also make people being able to live and work in peace, and contentment is an honor for the landscape architect. Therefore, this study builds an excellent public space landscape model based on the 3D image processing of the visual sensor, establishes the coverage relationship table through the genetic algorithm and the greedy algorithm, and then uses the infrared image segmentation method of the local entropy combined with the region growth method to segment the 3D image of the vision sensor. Then, we use the multi-image fusion method based on the dimensional closeness to merge and finally analyze the public space landscape model with extremely high integrity. Moreover, this study also discusses the analysis results of temperature and time on the integrity of the landscape model. Between 35.5°C and 36.7°C, from 12 o’clock to 14 o’clock, the integrity of the public space landscape model is as high as 97.2%.