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The detection, recognition and classification of features in a digital image is an important component of quality control systems in production and process engineering and industrial systems monitoring, in general. In this paper, a new pattern recognition system is presented that has been designed for the specific task of monitoring the quality of sheet-steel production in a rolling mill. The system is based on using both the Euclidean and Fractal geometric properties of an imaged object to develop training data that is used in conjunction with a supervised learning procedure based on the application of a fuzzy inference engine. Thus, the classification method includes the application of a set of features which include fractal parameters such as the Lacunarity and Fractal Dimension and thereby incorporates the characterisation of an object in terms of texture that, in this application, has metallurgical significance.
The principal issues associated with object recognition are presented including a new segmentation algorithm. The selflearning procedure for designing a decision making engine using fuzzy logic and membership function theory is also presented and a new technique for the creation and extraction of information from a membership function considered.
The methods discussed, and the system developed, have a range of applications in ‘machine vision’ and automatic inspection. However, in this publication, we focus on the development and implementation of a surface inspection system designed specifically for monitoring surface quality in the manufacture of sheet-steel. For this publication, we include a demonstration version of the system which can be downloaded, installed and utilised by interested readers as discussed in Section VI.
Blackledge, J., Dubovitskiy, D.: A Surface Inspection Machine Vision System that Includes Fractal Texture Analysis. International Society for Advanced Science and Technology, Journal of Intelligent Systems, Vol 3 (ISSN 1797-2329), issue: No 2, pages: 76-89, 2008. doi:10.21427/D7QS5J