Title: Performance evaluation of various texture analysis techniques for machine vision-based characterisation of machined surfaces

Authors: Ketaki Joshi; Bhushan Patil

Addresses: Fr. Conceicao Rodrigues College of Engineering, Fr. Agnel Ashram, BandStand, Bandra (W), Mumbai, Maharashtra 400050, India; Affiliated to: University of Mumbai, India ' Fr. Conceicao Rodrigues College of Engineering, Fr. Agnel Ashram, BandStand, Bandra (W), Mumbai, Maharashtra 400050, India; Affiliated to: University of Mumbai, India

Abstract: Machine vision-based inspection of surface quality leverages the principle of surface-texture characterisation, capitalising on image data characteristics. Frequently, surface-texture analysis adopts statistical and filter-based techniques, for this purpose. For surface texture characterisation, traditionally researchers prefer – parameterised histograms, grey level co-occurrence matrices, discrete Fourier transforms as well as discrete wavelet transforms. Despite popular usage, extant literature features very little in terms of comparative analyses amongst these techniques. Accordingly, this paper evaluates comparative performance of these techniques, for characterisation of machined surfaces and also recommends a novel hybrid technique that leverages higher discriminating capability. This hybrid discriminant-analysis methodology is derived from characterisation of 532 images of multi-textured machined surfaces. The results prove that the proposed technique, provides superior performance with higher accuracy, while requiring reduced optimal set of parameters, for inspection of surface quality.

Keywords: machine vision; texture analysis; image processing; discriminant analysis; multivariate techniques; surface texture; surface quality; histogram; grey level co-occurrence matrix; discrete Fourier transform; DFT; discrete wavelet transform; DWT; computational vision.

DOI: 10.1504/IJCVR.2020.107249

International Journal of Computational Vision and Robotics, 2020 Vol.10 No.3, pp.242 - 259

Received: 01 Nov 2018
Accepted: 19 May 2019

Published online: 29 Apr 2020 *

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