Abstract
We present a method for surface texture evaluation using machine vision by studying the phenomenon of reflection from a real surface. A parameterized anisotropic bi-directional reflectance distribution function (BRDF) is proposed along with a fusion reconstruction method which is analogous to the human visual system. The proposed reflection model operates on an image dataset to output the reconstructed shape. Machined surfaces obtained by performing mechanical grinding at varying machining conditions are analyzed using gray level co-occurrence matrix (GLCM), wavelet decomposition, photometric stereo and fusion reconstruction based texture analysis to study and benchmark performance of both statistical and topographical surface texture evaluation methods. The four methods are implemented in MATLAB™ to estimate surface roughness parameters – Ra, Rq, and Rz. Error analysis is performed by comparing estimated roughness values against stylus profilometer measurements. The comparison reveals that fusion reconstruction estimates surface roughness closer to stylus profilometer measurements as compared to GLCM, wavelet decomposition and photometric stereo based texture analysis.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.