Academic Journal
Three-Dimensional Reconstruction Method for Machined Surface Topography Based on Gray Gradient Constraints
| Title: | Three-Dimensional Reconstruction Method for Machined Surface Topography Based on Gray Gradient Constraints |
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| Authors: | Wei-Chao Shi, Jian-Ming Zheng, Yan Li, Xu-Bo Li |
| Source: | Applied Sciences, Vol 9, Iss 3, p 591 (2019) |
| Publisher Information: | MDPI AG, 2019. |
| Publication Year: | 2019 |
| Collection: | LCC:Technology LCC:Engineering (General). Civil engineering (General) LCC:Biology (General) LCC:Physics LCC:Chemistry |
| Subject Terms: | shape from shading, surface gradient, gray gradient, analytic reconstruction, machined surface topography, Technology, Engineering (General). Civil engineering (General), TA1-2040, Biology (General), QH301-705.5, Physics, QC1-999, Chemistry, QD1-999 |
| More Details: | In the modern engineering field, recovering the machined surface topography is important for studying mechanical product function and surface characteristics by using the shape from shading (SFS)-based reconstruction method. However, due to the limitations of many constraints and oversmoothing, the existing SFS-based reconstruction methods are not suitable for machined surface topography. This paper presents a new three-dimensional (3D) reconstruction method of machined surface topography. By combining the basic principle of SFS and the analytic method, the analytic model of a surface gradient is established using the gray gradient as a constraint condition. By efficiently solving the effect of quantization errors and ambiguity of the gray scale on reconstruction accuracy using a wavelet denoising algorithm and image processing technology, the reconstruction algorithm is implemented for machined surface topography. Experimental results on synthetic images and machined surface topography images show that the proposed algorithm can accurately and efficiently recover the 3D shape of machined surface topography. |
| Document Type: | article |
| File Description: | electronic resource |
| Language: | English |
| ISSN: | 2076-3417 |
| Relation: | https://www.mdpi.com/2076-3417/9/3/591; https://doaj.org/toc/2076-3417 |
| DOI: | 10.3390/app9030591 |
| Access URL: | https://doaj.org/article/17e724dc3ae1408585ff8fa2871003e3 |
| Accession Number: | edsdoj.17e724dc3ae1408585ff8fa2871003e3 |
| Database: | Directory of Open Access Journals |
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| ISSN: | 20763417 |
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| DOI: | 10.3390/app9030591 |
| Published in: | Applied Sciences |
| Language: | English |