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Craquelure Analysis for Content-Based Retrieval

Craquelure Analysis for Content-Based Retrieval
Craquelure Analysis for Content-Based Retrieval
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.
image processing, art and science, feature detection
111-114
Abas, F. S.
368323dd-5a71-476d-a89b-04d8eaabc145
Martinez, K.
5f711898-20fc-410e-a007-837d8c57cb18
Skodras, A.N.
99a4fe88-306f-4b12-91e7-1b2e7b7432fa
Constantinides, A.G.
ba0c2ece-9042-4893-b0be-a461abed6f62
Abas, F. S.
368323dd-5a71-476d-a89b-04d8eaabc145
Martinez, K.
5f711898-20fc-410e-a007-837d8c57cb18
Skodras, A.N.
99a4fe88-306f-4b12-91e7-1b2e7b7432fa
Constantinides, A.G.
ba0c2ece-9042-4893-b0be-a461abed6f62

Abas, F. S. and Martinez, K., (2002) Craquelure Analysis for Content-Based Retrieval Skodras, A.N. and Constantinides, A.G. (eds.) At 14th International Conference on Digital Signal Processing, Greece. 01 - 03 Jul 2002. , pp. 111-114.

Record type: Conference or Workshop Item (Other)

Abstract

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.

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More information

Published date: July 2002
Additional Information: Event Dates: July 1-3, 2002 Organisation: IEEE
Venue - Dates: 14th International Conference on Digital Signal Processing, Greece, 2002-07-01 - 2002-07-03
Keywords: image processing, art and science, feature detection
Organisations: Web & Internet Science

Identifiers

Local EPrints ID: 257382
URI: http://eprints.soton.ac.uk/id/eprint/257382
PURE UUID: de0d5e3b-9d02-415a-af30-c1d23912da39
ORCID for K. Martinez: ORCID iD orcid.org/0000-0003-3859-5700

Catalogue record

Date deposited: 26 Jun 2003
Last modified: 18 Jul 2017 09:39

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Contributors

Author: F. S. Abas
Author: K. Martinez ORCID iD
Editor: A.N. Skodras
Editor: A.G. Constantinides

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