Abstract
With ever increasing demands regarding product quality and documentation, industrial vision has become a key technology. Meanwhile the use of industrial vision systems in automated manufacturing goes without saying. However, there is in many cases a lack of understanding for this modern technology. This book was written in order to remedy this condition, which was in part created by the vision industry itself. As with all areas in which PCs are increasingly used, a trend to give the user more possibilities for application development became apparent in image processing. This makes it also necessary to equip the user with adequate know-how.
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Notes
- 1.
Actually this is true for every image, even those seen by the human eye or taken as a photograph, but in these cases the resolution is much higher so that the sampling is not recognized.
- 2.
Greek (eikon): image.
- 3.
Chapters 3 and 7 discuss how to deal with position shifts and how to perform gauging with an accuracy better than one image pixel.
References
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Russ JC (2007) The image processing handbook, 5th edn. CRC Press, Boca Raton
Sonka M, Hlavac V, Roger B (2008) Image processing, analysis, and machine vision, 3rd edn. Cengage Learning, Stamford
Steger C, Ulrich M, Wiedemann C (2008) Machine vision algorithms and applications. Wiley, VCH-Verlag Weinheim
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Demant, C., Garnica, C., Streicher-Abel, B. (2013). Introduction. In: Industrial Image Processing. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-33905-9_1
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DOI: https://doi.org/10.1007/978-3-642-33905-9_1
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