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Craquelure Analysis for Content-Based Retrieval
http://jb4-2.eprints-hosting.org/2086

In this paper, we describe a method for the extraction of distinguishable features from crack patterns, particularly those in paintings. First, we filter the selected crack image using 8 differently oriented Gabor filters. Then we thin the image to 1 pixel wide using a morphological thinning algorithm. Next we implement a crack following algorithm and generate statistical structure of global and local features from a chain code based representation. We describe an orientation-based feature extraction method to represent a crack network from sets of local orientation features. The resultant features are used as a guide towards classifying crack network patterns into several predefined classes, i.e circular, rectangular, spider-web, unidirectional and random. A simple classification experiment is presented to describe the significance of those extracted features towards classifying craquelure patterns.

F. S. Abas
K. Martinez

This list was generated on Sat Aug 31 06:55:18 2019 UTC.