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
July 2002
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.)
14th International Conference on Digital Signal Processing, Santorini, Greece.
01 - 03 Jul 2002.
.
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.
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, Santorini, 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
Catalogue record
Date deposited: 26 Jun 2003
Last modified: 15 Mar 2024 02:53
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Contributors
Author:
F. S. Abas
Author:
K. Martinez
Editor:
A.N. Skodras
Editor:
A.G. Constantinides
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