Abstract
The main approaches to microdevice production are microelectromechanical systems (MEMS) [1, 2] and microequipment technology (MET) [3–7]. To get the most out of these technologies, it is important to have advanced image recognition systems. In this chapter, we propose the Random Subspace Neural Classifier (RSC) for metal surface texture recognition. Examples of metal surfaces are presented in Fig. 10.1. Due to changes in viewpoint and illumination, the visual appearance of different surfaces can vary greatly, making recognition very difficult [8]. Different lighting conditions and viewing angles greatly affect the grayscale properties of an image due to such effects as shading, shadowing, and local occlusions. The real surface images, which it is necessary to recognize in industrial environments, have all these problems and more, such as dust on the surface.
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Kussul, E., Baidyk, T., Wunsch, D.C. (2010). Texture Recognition in Micromechanics. In: Neural Networks and Micromechanics. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-02535-8_10
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DOI: https://doi.org/10.1007/978-3-642-02535-8_10
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