Texture Extraction and Identification of 3D Engineering Surfaces

W. Zeng; X. Jiang; Y. Gao; T. Xie

doi:10.4028/www.scientific.net/KEM.295-296.453

Paper Titles

Advances in Micro and Nano-Scale Surface Metrology
p.431

2D and 3D Surface Texture Comparisons Using Autocorrelation Functions
p.437

Spline Filters for Surface Texture Analysis
p.441

Web-Based Virtual Measurement, Analysis and Evaluation Software Development for 3D Surface Topography
p.447

Texture Extraction and Identification of 3D Engineering Surfaces
p.453

Combination of M-Estimation and Gaussian Filtering for Characterizing 3D Engineering Surfaces
p.459

A Comparative Study of the Current 3D Motif Combination Methods
p.465

A New Three-Dimensional Profilometer for Surface Profile Measurement Using Digital Fringe Projection and Phase Shifting
p.471

A Two-Dimensional Surface Profile Imaging Technique Based on Heterodyne Interferometer
p.477

HomeKey Engineering MaterialsKey Engineering Materials Vols. 295-296Texture Extraction and Identification of 3D...

Texture Extraction and Identification of 3D Engineering Surfaces

Abstract:

For linear textures, widely exist on 3D engineering surfaces, a method for characterization based on the spectrum analysis is proposed. Through an angular spectrum analysis of the power spectrum of 3D surface signals, the directional characteristic parameters of the linear texture distribution on the surface are extracted. By using the directional parameters, the engineering surface can be roughly identified. A texture detector based on the directional Gabor wavelet transformation is used to detect the texture signals. The linear texture features of different directions and scales on a complex engineering surface can be decomposed. A weighting multi-scale correlative analyzing method is presented. The correlation analysis results of the texture features of different scales are weighted according to the significance and summed to obtain the final correlation results. Through Laplacian differential operation of the correlative output, a sharper correlative peak is obtained. This method has been successfully used to extract and identify bullet marks.