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Data Mining Approach to Digital Image Processing in Old Painting Restoration

  • Joanna Gancarczyk
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 185)

Abstract

In this paper an attempt has been made to apply data mining techniques to the task of separation and categorization features in digital images of artworks. Both craquelure separation and retouching identification are important steps in art restoration process. Since the main goal is to enable recognition of character and cause of damage, as well as forecasting its further enlargement, a proper tool for precise detection of the pattern is needed. However, the complex nature of the pattern is a reason why a simple, universal detection algorithm is not always possible to implement. Algorithms presented in this work apply mining structures which depend of expandable set of attributes forming a feature vector, and thus offer an elastic structure for analysis.

Keywords

Feature Vector Digital Image Processing Mathematical Morphology Mining Model Image Processing Method 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  1. 1.University of Bielsko-BialaBielsko-BialaPoland

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