Surface texture evaluation using 3D reconstruction from images by parametric anisotropic BRDF. (September 2018)

• Record Type: Journal Article
• Authors: Kumar, Hitendra; Ramkumar, J.; Venkatesh, K.S.
• Is Part Of: Measurement. Volume 125(2018)
• Page Start: 612
• Page End: 633
• Publication Date: 2018-09
• Subjects: Approximate bi-directional reflectance distibution function -- Computer vision -- Shape reconstruction -- Surface roughness -- Machining -- Metrology -- 3D reconstruction; Weights and measures -- Periodicals; Measurement -- Periodicals; Measurement; Weights and measures; Periodicals; 530.8
• DOI: 10.1016/j.measurement.2018.04.090
• Languages: English
• ISSNs: 0263-2241

Abstract

Graphical abstract: Highlights: A function to generate anisotropic BRDF is proposed for 3D surface reconstruction. Experimental setup is presented for image acquisition with varying illumination. Parameters of function are estimated from test images of spherical ball. Comparison of GLCM, wavelet, photometric stereo and proposed method is done. Proposed method measures roughness close to true value obtained by profiler. 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.