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In this paper we present a machine vision system for automatic inspection of defects in textured surfaces found in industry. The defects to be inspected are those that appear as local anomalies embedded in a homogeneous texture. The proposed method is based on a Gabor filtering scheme that computes the output response of energy from the convolution of a textured image with a specific Gabor filter. The best parameters of a Gabor filter is selected so that the energy of the homogeneous texture is zero, and any unpredictable defects will generate significantly large energy values. A simple thresholding scheme then follows to discriminate between homogeneous regions and defective regions in the filtered image. This transforms texture differences into detectable filter output. The experiments on structural textures such as textile fabrics and milled surfaces, and statistical textures such as leather and sandpaper have shown the effectiveness of the proposed method.

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Purpose – The purpose of this article is to develop a quantitative building accessibility assessment model for the construction industry. Design/methodology/approach – The building accessibility assessment criteria are incorporated in a hierarchy structure based on the relevant building regulations and British standards. The analytic hierarchy process (AHP) is employed to determine the priority of the accessibility criteria. A review of the application of AHP is included in the paper. Finally, a case scenario is used to illustrate the method. Findings – This paper provides a methodology to prioritize the building accessibility criteria and to indicate how well a building design meets accessibility requirements quantitatively. Practical limitations/implications – A model is advocated for use by accessibility consultants and building designers to establish a quantitative assessment for building accessibility. It can also be used in the development of accessibility assessment software. Originality/value – This paper presents a novel quantitative building accessibility assessment model.