Simulation of Intelligent Acquisition of Local Contour Information for 3D Printing Model
Image edge detection technology is the focus of image contour extraction technology,but due to the lack of target data,the traditional contour extraction algorithm has great limitations in application.In order to solve the problem of poor accuracy and low integrity of contour extraction in 3D printing model images,this paper proposes an image contour optimization combined with contour feature enhancement algorithm.Firstly,the image contour was opti-mized by Cartesian coordinate system planning,and then the image contour features were enhanced by using the guided filtering algorithm.Secondly,the optimized image information was processed based on CLAHE technology and Gaussian filtering technology to accelerate the model construction speed.Finally,the intelligent acquisition model of image local contour information(HOG-CFE model)was constructed based on the HOG features of the image.The simulation results show that the HOG-CFE model constructed by image contour enhancement processing effectively improves the integrity of model contour extraction and enhances the accuracy of the model.Compared with other base-line algorithms,the area ratio index of the HOG-CFE model is improved by 2.33%on average,and the overall accu-racy is improved by 6.01%and 5.42%.In this paper,the intelligent acquisition model of HOG-CFE local contour in-formation effectively improves the integrity of contour extraction and the accuracy of the model through contour en-hancement technology.
Contour enhancementImage processing3D printing model
NETL
NSTL
万方数据
