22 May 2003 Classification of painting cracks for content-based analysis
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In this paper we present steps taken to implement a content-based analysis of crack patterns in paintings. Cracks are first detected using a morphological top-hat operator and grid-based automatic thresholding. From a 1-pixel wide representation of crack patterns, we generate a statistical structure of global and local features from a chain-code based representation. A well structured model of the crack patterns allows post-processing to be performed such as pruning and high-level feature extraction. High-level features are extracted from the structured model utilising information mainly based on orientation and length of line segments. Our strategy for classifying the crack patterns makes use of an unsupervised approach which incorporates fuzzy clustering of the patterns. We present results using the fuzzy k-means technique.
© (2003) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Fazly Salleh Abas, Fazly Salleh Abas, Kirk Martinez, Kirk Martinez, } "Classification of painting cracks for content-based analysis", Proc. SPIE 5011, Machine Vision Applications in Industrial Inspection XI, (22 May 2003); doi: 10.1117/12.474012; https://doi.org/10.1117/12.474012


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