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Object Extraction from Colour Cadastral Maps

Authors: Romain Raveaux; Jean-Christophe Burie; Jean-Marc Ogier;

Object Extraction from Colour Cadastral Maps

Abstract

In this paper, an object extraction method from ancient colour maps is proposed. It consists on the localization of quarters inside a given cadastral map. The colour aspect is exploited thanks to a colour restoration algorithm and the selection of a relevant hybrid colour model. Objects composing the map are located using a multi-components gradient. To identify quarters, a peeling the onion method is adopted. This selective method starts by separated text and graphics. On the graphic layer, a connected component analysis is carried out through the use of a neighbourhood graph. This graph is smartly pruned to consider only significant areas. Consequently, the quarter boundaries are found using a snake which is a computer-generated curve that moves within an image to fit a given object. The performance of our method is measured up in two steps: Firstly, the colour space selection is assessed according to the colour distinction capacity while being robust to variations/noise then the automatic extraction approach is compared to the user ground truth. Results show the good behaviour of the whole system.

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
7
Average
Top 10%
Average
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