Research on 3D virtual simulation of geology based on GIS

Liang, Zhijian; Qiao, Daoji; Sung, Tulips

doi:10.1007/s12517-021-06615-4

Cited by 6 publications

(4 citation statements)

References 6 publications

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“…[1]. Based on computer technology, virtual reality technology creates a nearly real threedimensional virtual environment and realizes interactive functions [2,3]. The current virtual environment has been able to realize the modeling of scenes and models.…”

Section: Introductionmentioning

confidence: 99%

Optimization of Three-Dimensional Model of Landscape Space Based on Big Data Analysis

Wang

2022

Journal of Function Spaces

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Based on virtual reality technology, landscape 3D modeling provides users with the possibility to construct a simulated garden landscape environment design effect online, so it has high requirements for accuracy. With the continuous improvement of precision requirements, the number of people involved in the construction of 3D models is also increasing, which puts forward higher requirements for modeling. Based on this, this paper studies the optimization strategy of landscape space 3D model based on big data analysis. Based on the analysis of the establishment of the 3D model and the related algorithm research, this paper analyzes the optimal design of the 3D model under the background of big data. In the 3D modeling of the edge folded area, it is based on the traditional quadratic error measurement grid simplification algorithm, combined with the vertex error matrix to simplify, so as to shorten the modeling time. Based on an efficient search algorithm, an adaptive nonsearch fractal image compression and decoding method is proposed in the image compression and decoding stage of 3D modeling. The search is performed by specifying the defined area block. Finally, an experiment is designed to analyze the performance of the optimization algorithm. The results show that the improved edge folding region algorithm can reduce errors on the basis of ensuring image quality, and the adaptive search algorithm can shorten the search time and improve the compression rate. This method provides a technical reference for the visualization experience and simulation system of garden landscape design and improves the presentation quality of virtual garden landscape design scenes.

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Section: Introductionmentioning

confidence: 99%

Optimization of Three-Dimensional Model of Landscape Space Based on Big Data Analysis

Wang

2022

Journal of Function Spaces

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“…For example, Wang Xiaoyan et al have conducted in-depth research on the use of the three-frame difference method to extract targets. Yao Qian et al have used the idea of combining the three-frame difference method [15][16][17][18][19] and mean-shift to realize the detection and tracking of the human body. Wang Bin et al made vehicle recognition possible by combining two types of different methods: the background difference technique and the interframe difference method, whereas Wei et al use the three-frame difference approach in conjunction with better Gaussian modeling to detect moving objects.…”

Section: Interframe Difference Methodmentioning

confidence: 99%

Feature Extraction Algorithm of Basketball Trajectory Based on the Background Difference Method

Xing

2022

Mathematical Problems in Engineering

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People are increasingly interested in moving object detection technology as a part of computer vision technology, which has grown in importance in recent years. Moving target detection technology is widely employed in military defense, security monitoring, medical examinations, intelligent transportation, and many other industries at the present time. A moving target detection system relies on rapid and accurate video picture segmentation to identify, locate, and analyze the target. The trajectory of a basketball player’s shooting motion and the extraction of features are important to increase the accuracy of basketball players’ shots. This study proposes an area growth algorithm-based approach to tracing the shooting motion of basketball players. The trajectory of the shot motion is captured using a video sensor image tracking approach. The corner points and the edge contours of the trajectory image are extracted using the edge contour feature extraction method, the feature extraction from the firing motion trajectory image is merged with the regional linear growth approach, and the corner points are then marked. Feature extraction improves basketball players’ shooting accuracy and trajectory control ability. Simulation results show that the prediction accuracy of basketball players’ shooting trajectories using this method can reach up to 100%, which improves the accuracy of motion trajectory extraction and enhances basketball players’ shooting motion control.

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“…[15].This paper studies image classification on the basis of CNN, and further studies the influence of activation function on the accuracy of image classification, proposes a combination of linear and nonlinear function as the activation function, and uses it on the Chinese painting data set and general. The experimental results show that the improved activation function improves the classification performance [16].…”

Section: Introductionmentioning

confidence: 94%

Painting Modeling Language Based on Convolution Neural Networks in Digital Media Art

Zhou

2022

Wireless Communications and Mobile Computing

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Digital media, as a high-tech method, has gradually affected all aspects of social production, including artistic creation, in the information age. Contemporary painting art, as an important form of artistic expression, will inevitably be baptized by digital media in the tide of time. The color of digital media ushers in a new era in contemporary painting. The development of deep learning makes the research field of artificial intelligence move towards a deeper and more practical level. Among them, CNN (Convolutional Neural Network) has very important research value in image feature expression. The digitization of painting works is of great significance to the effective use of painting resources. The traditional image classification methods do not consider the subjective characteristics of painting works, and most of the features need to be extracted manually, resulting in the loss of detailed features. The research and design of image retrieval system in this paper is based on CNN. One is to extract the features of the image using convolution neural network. The scale of image data sets in the resource database is still small. For the field of deep learning research, data sets can only be regarded as small-scale data sets. Combined with the actual situation, the convolution neural network model is trained, and the better convolution neural network structure Artnet is selected through experimental analysis. It is a seven-layer network structure including two volume layers, two pool layers, and a full connection layer. The second is to design and implement a practical image retrieval system based on Artnet and effectively integrate it with other systems of the project as an important function of the project construction.

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Research on 3D virtual simulation of geology based on GIS

Cited by 6 publications

References 6 publications

Optimization of Three-Dimensional Model of Landscape Space Based on Big Data Analysis

Optimization of Three-Dimensional Model of Landscape Space Based on Big Data Analysis

Feature Extraction Algorithm of Basketball Trajectory Based on the Background Difference Method

Painting Modeling Language Based on Convolution Neural Networks in Digital Media Art

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