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Conference Paper/Proceeding/Abstract 778 views 188 downloads

Extracting Lineage Information from Hand-Drawn Ancient Maps

Ehab Essa, Xianghua Xie Orcid Logo, Richard Turner, Matthew Stevens Orcid Logo, Daniel Power

Image Analysis and Recognition, Volume: 9730, Pages: 268 - 275

Swansea University Authors: Xianghua Xie Orcid Logo, Matthew Stevens Orcid Logo

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DOI (Published version): 10.1007/978-3-319-41501-7_30

Abstract

In this paper, we present an efficient segmentation technique that extracts piecewise linear patterns from hand-drawn maps. The user is only required to place the starting and end points and the method is capable of extracting the route that connects the two, which closely colocates with the hand-dr...

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Published in: Image Analysis and Recognition
ISBN: 978-3-319-41500-0 978-3-319-41501-7
ISSN: 0302-9743 1611-3349
Published: Springer International Publishing 2016
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URI: https://cronfa.swan.ac.uk/Record/cronfa49176
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last_indexed 2019-09-12T20:42:26Z
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spelling 2019-09-12T15:24:01.6166615 v2 49176 2019-03-12 Extracting Lineage Information from Hand-Drawn Ancient Maps b334d40963c7a2f435f06d2c26c74e11 0000-0002-2701-8660 Xianghua Xie Xianghua Xie true false 24e42c4652a3104d12bc7424d475408d 0000-0001-8646-951X Matthew Stevens Matthew Stevens true false 2019-03-12 SCS In this paper, we present an efficient segmentation technique that extracts piecewise linear patterns from hand-drawn maps. The user is only required to place the starting and end points and the method is capable of extracting the route that connects the two, which closely colocates with the hand-drawn map. It provides an effective approach to interactively process and understand those historical maps. The proposed method employs supervised learning to evaluate at every pixel location the probability that such a lineage pattern exists, followed by shortest path segmentation to extract the border of interest. Conference Paper/Proceeding/Abstract Image Analysis and Recognition 9730 268 275 Springer International Publishing 978-3-319-41500-0 978-3-319-41501-7 0302-9743 1611-3349 computer vision, medieval history, maps 1 7 2016 2016-07-01 10.1007/978-3-319-41501-7_30 Authored by Ehab Essa, Xianghua Xie, Richard Turner, Matthew Stevens, and Daniel Power COLLEGE NANME Computer Science COLLEGE CODE SCS Swansea University 2019-09-12T15:24:01.6166615 2019-03-12T15:42:48.0701442 Ehab Essa 1 Xianghua Xie 0000-0002-2701-8660 2 Richard Turner 3 Matthew Stevens 0000-0001-8646-951X 4 Daniel Power 5 0049176-12092019151812.pdf 49176.pdf 2019-09-12T15:18:12.3670000 Output 8166385 application/pdf Accepted Manuscript true 2019-09-12T00:00:00.0000000 true eng
title Extracting Lineage Information from Hand-Drawn Ancient Maps
spellingShingle Extracting Lineage Information from Hand-Drawn Ancient Maps
Xianghua Xie
Matthew Stevens
title_short Extracting Lineage Information from Hand-Drawn Ancient Maps
title_full Extracting Lineage Information from Hand-Drawn Ancient Maps
title_fullStr Extracting Lineage Information from Hand-Drawn Ancient Maps
title_full_unstemmed Extracting Lineage Information from Hand-Drawn Ancient Maps
title_sort Extracting Lineage Information from Hand-Drawn Ancient Maps
author_id_str_mv b334d40963c7a2f435f06d2c26c74e11
24e42c4652a3104d12bc7424d475408d
author_id_fullname_str_mv b334d40963c7a2f435f06d2c26c74e11_***_Xianghua Xie
24e42c4652a3104d12bc7424d475408d_***_Matthew Stevens
author Xianghua Xie
Matthew Stevens
author2 Ehab Essa
Xianghua Xie
Richard Turner
Matthew Stevens
Daniel Power
format Conference Paper/Proceeding/Abstract
container_title Image Analysis and Recognition
container_volume 9730
container_start_page 268
publishDate 2016
institution Swansea University
isbn 978-3-319-41500-0
978-3-319-41501-7
issn 0302-9743
1611-3349
doi_str_mv 10.1007/978-3-319-41501-7_30
publisher Springer International Publishing
document_store_str 1
active_str 0
description In this paper, we present an efficient segmentation technique that extracts piecewise linear patterns from hand-drawn maps. The user is only required to place the starting and end points and the method is capable of extracting the route that connects the two, which closely colocates with the hand-drawn map. It provides an effective approach to interactively process and understand those historical maps. The proposed method employs supervised learning to evaluate at every pixel location the probability that such a lineage pattern exists, followed by shortest path segmentation to extract the border of interest.
published_date 2016-07-01T03:59:58Z
_version_ 1763753064590737408
score 11.016235

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