Mathematical Modeling for Ceramic Shape 3D Image Based on Deep Learning Algorithm

Ceramic image shape 3D image modeling focuses on of ceramic that was obtained from the camera imaging equipment such as 2D images, by normalization, gray, filtering denoising, wavelet image sharpening edge enhancement, binarization, and shape contour extraction pretreatment processes such as extract...

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Autores principales:	Lijian Zhang, Guangfu Liu
Formato:	article
Lenguaje:	EN
Publicado:	Hindawi Limited 2021
Materias:	Physics QC1-999
Acceso en línea:	https://doaj.org/article/0043a22909d243efa62a926445769b8d
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spelling	oai:doaj.org-article:0043a22909d243efa62a926445769b8d2021-11-08T02:36:57ZMathematical Modeling for Ceramic Shape 3D Image Based on Deep Learning Algorithm1687-913910.1155/2021/4343255https://doaj.org/article/0043a22909d243efa62a926445769b8d2021-01-01T00:00:00Zhttp://dx.doi.org/10.1155/2021/4343255https://doaj.org/toc/1687-9139Ceramic image shape 3D image modeling focuses on of ceramic that was obtained from the camera imaging equipment such as 2D images, by normalization, gray, filtering denoising, wavelet image sharpening edge enhancement, binarization, and shape contour extraction pretreatment processes such as extraction ceramic image shape edge profile, again, according to the image edge extraction and elliptic rotator ceramics phenomenon. The image distortion effect was optimized by self-application, and then the deep learning modeler was used to model the side edge contour. Finally, the 3D ceramic model of the rotating body was restored according to the intersection and central axis of the extracted contour. By studying the existing segmentation methods based on deep learning, the automatic segmentation of target ceramic image and the effect of target edge refinement and optimization are realized. After extracting and separating the target ceramics from the image, we processed the foreground image of the target into a three-dimensional model. In order to reduce the complexity of the model, a 3D contextual sequencing model is adopted to encode the hidden space features along the channel dimensions, to extract the causal correlation between channels. Each module in the compression framework is optimized by a rate-distortion loss function. The experimental results show that the proposed 3D image modeling method has significant advantages in compression performance compared with the optimal 2D 3D image modeling method based on deep learning, and the experimental results show that the performance of the proposed method is superior to JP3D and HEVC methods, especially at low bit rate points.Lijian ZhangGuangfu LiuHindawi LimitedarticlePhysicsQC1-999ENAdvances in Mathematical Physics, Vol 2021 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Physics QC1-999
spellingShingle	Physics QC1-999 Lijian Zhang Guangfu Liu Mathematical Modeling for Ceramic Shape 3D Image Based on Deep Learning Algorithm
description	Ceramic image shape 3D image modeling focuses on of ceramic that was obtained from the camera imaging equipment such as 2D images, by normalization, gray, filtering denoising, wavelet image sharpening edge enhancement, binarization, and shape contour extraction pretreatment processes such as extraction ceramic image shape edge profile, again, according to the image edge extraction and elliptic rotator ceramics phenomenon. The image distortion effect was optimized by self-application, and then the deep learning modeler was used to model the side edge contour. Finally, the 3D ceramic model of the rotating body was restored according to the intersection and central axis of the extracted contour. By studying the existing segmentation methods based on deep learning, the automatic segmentation of target ceramic image and the effect of target edge refinement and optimization are realized. After extracting and separating the target ceramics from the image, we processed the foreground image of the target into a three-dimensional model. In order to reduce the complexity of the model, a 3D contextual sequencing model is adopted to encode the hidden space features along the channel dimensions, to extract the causal correlation between channels. Each module in the compression framework is optimized by a rate-distortion loss function. The experimental results show that the proposed 3D image modeling method has significant advantages in compression performance compared with the optimal 2D 3D image modeling method based on deep learning, and the experimental results show that the performance of the proposed method is superior to JP3D and HEVC methods, especially at low bit rate points.
format	article
author	Lijian Zhang Guangfu Liu
author_facet	Lijian Zhang Guangfu Liu
author_sort	Lijian Zhang
title	Mathematical Modeling for Ceramic Shape 3D Image Based on Deep Learning Algorithm
title_short	Mathematical Modeling for Ceramic Shape 3D Image Based on Deep Learning Algorithm
title_full	Mathematical Modeling for Ceramic Shape 3D Image Based on Deep Learning Algorithm
title_fullStr	Mathematical Modeling for Ceramic Shape 3D Image Based on Deep Learning Algorithm
title_full_unstemmed	Mathematical Modeling for Ceramic Shape 3D Image Based on Deep Learning Algorithm
title_sort	mathematical modeling for ceramic shape 3d image based on deep learning algorithm
publisher	Hindawi Limited
publishDate	2021
url	https://doaj.org/article/0043a22909d243efa62a926445769b8d
work_keys_str_mv	AT lijianzhang mathematicalmodelingforceramicshape3dimagebasedondeeplearningalgorithm AT guangfuliu mathematicalmodelingforceramicshape3dimagebasedondeeplearningalgorithm
_version_	1718443061801910272

Mathematical Modeling for Ceramic Shape 3D Image Based on Deep Learning Algorithm

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