Abstract

This paper details the development of an automated vision-based solution for identification of paint and substrate defects on painted slates. The developed vision system consists of two major components. The first component of the system addresses issues including the mechanical implementation and interfacing the inspection system with the sensing and optical equipment. The second component involves the development of an image processing algorithm that is able to identify the visual defects present on the slate surface. The process of imaging the slate proved to be very challenging as the slate surface is darkly coloured and presents depth non-uniformities. Hence, a key issue for this inspection system was to devise an adequate illumination system that was able to accommodate challenges including the slates' surface depth non-uniformities and vibrations generated by the conveying system. The visual defects are detected using a novel texture analysis solution where the greyscale (tonal characteristics) and texture information are embedded in a composite model. The developed inspection system was tested for robustness and experimental results are presented.

PurposeThe purpose of this paper is to propose a generic framework based on the colour and the texture features for colour‐textured image segmentation. The framework can be applied to any real‐world applications for appropriate interpretation.Design/methodology/approachThe framework derives the contributions of colour and texture in image segmentation. Local binary pattern and an unsupervised k‐means clustering are used to cluster pixels in the chrominance plane. An unsupervised segmentation method is adopted. A quantitative estimation of colour and texture performance in segmentation is presented. The proposed method is tested using different mosaic and natural images and other image database used in computer vision. The framework is applied to three different applications namely, Irish script on screen images, skin cancer images and sediment profile imagery to demonstrate the robustness of the framework.FindingsThe inclusion of colour and texture as distributions of regions provided a good discrimination of the colour and the texture. The results indicate that the incorporation of colour information enhanced the texture analysis techniques and the methodology proved effective and efficient.Originality/valueThe novelty lies in the development of a generic framework using both colour and texture features for image segmentation and the different applications from various fields.

Abstract. This paper describes a method for colour texture analysis, which performs segmentation based on colour and texture information. The main goal of this approach is to examine the contribution of chromaticity features in the analysis of texture. Local binary pattern and discrete cosine transform are the techniques utilised as a tool to perform feature extraction. Segmentation is carried out based on an unsupervised texture segmentation method. The performance of the method is evaluated using different chromaticity features and also using the ROC curves. The results indicate that the inclusion of colour information improves the segmentation performance.