Surface roughness extraction based on Markov random field model in wavelet feature domain

Lei Yang; Li-qiao Lei

doi:10.1117/1.OE.53.12.122414

9 September 2014 Surface roughness extraction based on Markov random field model in wavelet feature domain

Lei Yang, Li-qiao Lei

Author Affiliations +

Optical Engineering, Vol. 53, Issue 12, 122414 (September 2014). https://doi.org/10.1117/1.OE.53.12.122414

Abstract

Based on the computer texture analysis method, a new noncontact surface roughness measurement technique is proposed. The method is inspired by the nonredundant directional selectivity and highly discriminative nature of the wavelet representation and the capability of the Markov random field (MRF) model to capture statistical regularities. Surface roughness information contained in the texture features may be extracted based on an MRF stochastic model of textures in the wavelet feature domain. The model captures significant intrascale and interscale statistical dependencies between wavelet coefficients. To investigate the relationship between the texture features and surface roughness R_a, a simple research setup, which consists of a charge-coupled diode camera without a lens and a diode laser, was established, and the laser speckle texture patterns are acquired from some standard grinding surfaces. The research results have illustrated that surface roughness R_a has a good monotonic relationship with the texture features of the laser speckle pattern. If this measuring system is calibrated with the surface standard samples roughness beforehand, the surface roughness actual value R_a can be deduced in the case of the same material surfaces ground at the same manufacture conditions.

Citation Download Citation

Lei Yang and Li-qiao Lei "Surface roughness extraction based on Markov random field model in wavelet feature domain," Optical Engineering 53(12), 122414 (9 September 2014). https://doi.org/10.1117/1.OE.53.12.122414

Published: 9 September 2014

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