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Grouping of crack patterns using proximity and characteristic rules

Grouping of crack patterns using proximity and characteristic rules
Grouping of crack patterns using proximity and characteristic rules
In this paper, we present a 2-stage approach to connected curve grouping. The algorithm is experimented and demonstrated on crack-detected images of paintings. Some features are left undetected and this tends to produce disconnected curves. In order to extract high-level features for content-based application, these supposedly connected curves have to be grouped together. It is one of the many steps needed to produce a content-based platform for digital analysis of crack patterns in paintings particularly for classification purpose. The prime objective of the grouping algorithm is to segment or partition areas of an image to produce reliable representations of content. The first stage of the algorithm utilizes the Minimum Bounding Rectangle (MBR) of a crack network as means of finding overlapping features. We demonstrate the use of the both the rotated and the un-rotated MBR. In the second stage, curve characteristics represented by the rotated MBR such as the dimension ratio, the axis of minimum inertia, object centroid and node density are used as features for an N-dimensional clustering.
image processing, Computer Vision, data clustering, pattern analysis
0-88986-382-2
Fazly, Abas
b92ab8ff-6872-4612-9496-ea60a70d289f
Kirk, Martinez
5f711898-20fc-410e-a007-837d8c57cb18
Fazly, Abas
b92ab8ff-6872-4612-9496-ea60a70d289f
Kirk, Martinez
5f711898-20fc-410e-a007-837d8c57cb18

(2003) Grouping of crack patterns using proximity and characteristic rules Fazly, Abas and Kirk, Martinez (eds.) At Proceedings of the Third IASTED International Conference on Visualization, Imaging and Image Processing, Spain. 08 - 10 Sep 2003.

Record type: Conference or Workshop Item (Other)

Abstract

In this paper, we present a 2-stage approach to connected curve grouping. The algorithm is experimented and demonstrated on crack-detected images of paintings. Some features are left undetected and this tends to produce disconnected curves. In order to extract high-level features for content-based application, these supposedly connected curves have to be grouped together. It is one of the many steps needed to produce a content-based platform for digital analysis of crack patterns in paintings particularly for classification purpose. The prime objective of the grouping algorithm is to segment or partition areas of an image to produce reliable representations of content. The first stage of the algorithm utilizes the Minimum Bounding Rectangle (MBR) of a crack network as means of finding overlapping features. We demonstrate the use of the both the rotated and the un-rotated MBR. In the second stage, curve characteristics represented by the rotated MBR such as the dimension ratio, the axis of minimum inertia, object centroid and node density are used as features for an N-dimensional clustering.

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More information

Published date: 2003
Additional Information: Event Dates: Sept. 8-10
Venue - Dates: Proceedings of the Third IASTED International Conference on Visualization, Imaging and Image Processing, Spain, 2003-09-08 - 2003-09-10
Keywords: image processing, Computer Vision, data clustering, pattern analysis
Organisations: Web & Internet Science

Identifiers

Local EPrints ID: 258225
URI: http://eprints.soton.ac.uk/id/eprint/258225
ISBN: 0-88986-382-2
PURE UUID: 2b588210-b96d-4663-88ac-a73a77d97a1b
ORCID for Martinez Kirk: ORCID iD orcid.org/0000-0003-3859-5700

Catalogue record

Date deposited: 18 Oct 2003
Last modified: 18 Jul 2017 09:34

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Contributors

Editor: Abas Fazly
Editor: Martinez Kirk ORCID iD

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