The University of Southampton
University of Southampton Institutional Repository

Analysis of craquelure patterns for content-based retrieval

Analysis of craquelure patterns for content-based retrieval
Analysis of craquelure patterns for content-based retrieval
The advent of multimedia technology has offered a new dimension in computerised applications. Art-based applications are among those which have and will continue to benefit from this advancement. Content-based image retrieval (CBIR) and analysis is attracting attention from museums and art institutions. One of the image-based requirements from museums is to automatically classify craquelure (cracks) in paintings for the purpose of aiding damage assessment using non-destructive monitoring and testing. Craquelure in paintings can be an important element in judging authenticity, use of material as well as environmental and physical impact, which these can contribute to different craquelure patterns. Mass screening of craquelure patterns will help to establish a better platform for conservators to identify cause of damage and a content-based approach is seen as an appropriate path.

This thesis covers the issues of crack enhancement and detection, using a mathematical morphology technique, namely the top-hat operator and also a grid-based automatic thresholding. Craquelure representation aids the processes of craquelure pattern analysis in which the Freeman chain-code is used as a basis for converting the image-based representation into a hierarchically structured numerical form. This hierarchical representation offers several advantages in the sense that detected craquelure patterns can be pruned, according to a certain rule for eliminating suspected noise and insignificant structures. Information can be retrieved in a flexible way, given multi-level access into structural detail. A grouping technique determines ‘objects-of-interest’ and structured craquelure patterns, named crack-networks are grouped using proximity and characteristic rules. Craquelure patterns are generalised by utilising conservative approximations based on the minimum bounding rectangle (MBR) and rotated minimum bounding rectangle (RMBR). Meaningful features based on orientation histograms and structural statistics are extracted to distinguish between craquelure patterns. The resultant features are used as inputs for a three-stage average distance k-nearest neighbour (k-NN) classifier with fuzzy outputs where the goal is to produce class memberships. A prototype architecture of a craquelure retrieval system is also discussed.
image processing, conten-based retrieval, pattern recognition
Abas, Fazly
fc3a04a1-0608-46ae-9d32-70bd50061509
Abas, Fazly
fc3a04a1-0608-46ae-9d32-70bd50061509
Martinez, Kirk
5f711898-20fc-410e-a007-837d8c57cb18

Abas, Fazly (2004) Analysis of craquelure patterns for content-based retrieval. University of Southampton, School of Electronics and Computer Science, Doctoral Thesis, 241pp.

Record type: Thesis (Doctoral)

Abstract

The advent of multimedia technology has offered a new dimension in computerised applications. Art-based applications are among those which have and will continue to benefit from this advancement. Content-based image retrieval (CBIR) and analysis is attracting attention from museums and art institutions. One of the image-based requirements from museums is to automatically classify craquelure (cracks) in paintings for the purpose of aiding damage assessment using non-destructive monitoring and testing. Craquelure in paintings can be an important element in judging authenticity, use of material as well as environmental and physical impact, which these can contribute to different craquelure patterns. Mass screening of craquelure patterns will help to establish a better platform for conservators to identify cause of damage and a content-based approach is seen as an appropriate path.

This thesis covers the issues of crack enhancement and detection, using a mathematical morphology technique, namely the top-hat operator and also a grid-based automatic thresholding. Craquelure representation aids the processes of craquelure pattern analysis in which the Freeman chain-code is used as a basis for converting the image-based representation into a hierarchically structured numerical form. This hierarchical representation offers several advantages in the sense that detected craquelure patterns can be pruned, according to a certain rule for eliminating suspected noise and insignificant structures. Information can be retrieved in a flexible way, given multi-level access into structural detail. A grouping technique determines ‘objects-of-interest’ and structured craquelure patterns, named crack-networks are grouped using proximity and characteristic rules. Craquelure patterns are generalised by utilising conservative approximations based on the minimum bounding rectangle (MBR) and rotated minimum bounding rectangle (RMBR). Meaningful features based on orientation histograms and structural statistics are extracted to distinguish between craquelure patterns. The resultant features are used as inputs for a three-stage average distance k-nearest neighbour (k-NN) classifier with fuzzy outputs where the goal is to produce class memberships. A prototype architecture of a craquelure retrieval system is also discussed.

PDF
fazlythesis.pdf - Other
Download (10MB)

More information

Published date: August 2004
Keywords: image processing, conten-based retrieval, pattern recognition
Organisations: University of Southampton, Electronics & Computer Science

Identifiers

Local EPrints ID: 260040
URI: https://eprints.soton.ac.uk/id/eprint/260040
PURE UUID: 1acd60c3-5526-4d97-bb38-130829ad9e5d
ORCID for Kirk Martinez: ORCID iD orcid.org/0000-0003-3859-5700

Catalogue record

Date deposited: 20 Oct 2004
Last modified: 06 Jun 2018 13:01

Export record

Download statistics

Downloads from ePrints over the past year. Other digital versions may also be available to download e.g. from the publisher's website.

View more statistics

Atom RSS 1.0 RSS 2.0

Contact ePrints Soton: eprints@soton.ac.uk

ePrints Soton supports OAI 2.0 with a base URL of https://eprints.soton.ac.uk/cgi/oai2

This repository has been built using EPrints software, developed at the University of Southampton, but available to everyone to use.

We use cookies to ensure that we give you the best experience on our website. If you continue without changing your settings, we will assume that you are happy to receive cookies on the University of Southampton website.

×