Abstract
The aim of the following study was to examine the influence of image resolution in the pattern recognition in the grey scale images. In order to recognise the pattern, the authors used the method based on moment invariants which were the elements of feature vectors defining the features of the recognised object. The paper presents the influence of image resolution for exemplary images on both: the values of moment invariants and distances between feature vectors. The authors have paid a great attention to the fact that these distances are significant for distinguishing given object classes. One can conclude from the results that for a significant decrease in resolution there occur problems in pattern recognition. It results from the influence of image resolution on the value of moment invariants and at the same time on the value of the distance between the feature vectors defining the recognised objects. In this way, the paper shows that in order to recognise objects correctly, it is necessary to retain some necessary minimum resolution. It is indispensable despite the fact that we usually aim at decreasing the amount of processed data which is on the hand crucial because of short processing times in many practical applications. It is therefore essential because of the fact that we need to guarantee short times of image processing in many practical applications. Moreover, this study presents the examples of the algorithms in use.
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Kuś, Z., Nawrat, A. (2016). Minimizing the Image Resolution in Order to Increase the Computing Speed Without Losing the Separation of the Recognised Patterns. In: Nawrat, A., Jędrasiak, K. (eds) Innovative Simulation Systems. Studies in Systems, Decision and Control, vol 33. Springer, Cham. https://doi.org/10.1007/978-3-319-21118-3_1
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DOI: https://doi.org/10.1007/978-3-319-21118-3_1
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