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Estimation and monitoring of product aesthetics: application to manufacturing of “engineered stone” countertops

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Abstract

A new machine vision approach for quantitatively estimating and monitoring the appearance and aesthetics of manufactured products is presented. The approach is composed of three steps: (1) wavelet-based textural feature extraction from product images, (2) estimation of measures of the product appearance through subspace projection of the textural features, and (3) monitoring of the appearance in the latent variable subspace of the textural features. The methodology is specifically designed to treat the stochastic nature of the visual appearance of many manufactured products. This nondeterministic aspect of product appearance has been an obstacle for the success of machine vision in many industries. The emphasis of this approach is on the consistent and quantitative estimation of continuous variations in visual appearance rather than on classification into discrete classes. This allows for the on-line monitoring and the eventual feedback control of product appearance. This approach is successfully applied to the estimation and monitoring of the aesthetic quality of manufactured stone countertops.

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Author notes

  1. J. Jay Liu

    Present address: Samsung Electronics, Asan, Chungcheongnam-Do, Korea

Authors and Affiliations

  1. Department of Chemical Engineering, McMaster University, 1280 Main Street West, Hamilton, Ontario, L8S 4L7, Canada

    J. Jay Liu & John F. MacGregor

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  1. J. Jay Liu
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  2. John F. MacGregor
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Liu, J.J., MacGregor, J.F. Estimation and monitoring of product aesthetics: application to manufacturing of “engineered stone” countertops. Machine Vision and Applications 16, 374–383 (2006). https://doi.org/10.1007/s00138-005-0009-8

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  • DOI: https://doi.org/10.1007/s00138-005-0009-8

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