FCN-Boosted Historical Map Segmentation with Little Training Data

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F49777513%3A23520%2F23%3A43969346" target="_blank" >RIV/49777513:23520/23:43969346 - isvavai.cz</a>

Výsledek na webu

<a href="https://link.springer.com/chapter/10.1007/978-3-031-41676-7_30" target="_blank" >https://link.springer.com/chapter/10.1007/978-3-031-41676-7_30</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1007/978-3-031-41676-7_30" target="_blank" >10.1007/978-3-031-41676-7_30</a>

Jazyk výsledku

angličtina

Název v původním jazyce

FCN-Boosted Historical Map Segmentation with Little Training Data

Popis výsledku v původním jazyce

This paper deals with automatic image segmentation in poorly resourced areas. We concentrate on map content segmentation in historical maps as an example of such a domain. In such cases, conventional computer vision (CV) approaches fail in unexpected unique regions such as map content area exceeding the map frame, while deep learning methods lack boundary localization accuracy. Therefore, we propose an efficient approach that combines conventional CV techniques with deep learning and practically eliminates their drawbacks. To do so, we redefine the learning objective of a simple fully convolutional network to make the training easier and the model more robust even with few training samples. The presented method provides excellent results compared to more sophisticated but solely deep learning or traditional computer vision techniques as shown in “MapSeg” segmentation competition, where all other approaches were significantly outperformed. We further propose two additional approaches that improve the original method and set a new state-of-the-art result on the MapSeg dataset. The methods are further tested on an extended version of the Map Border dataset to show their robustness.

Název v anglickém jazyce

FCN-Boosted Historical Map Segmentation with Little Training Data

Popis výsledku anglicky

This paper deals with automatic image segmentation in poorly resourced areas. We concentrate on map content segmentation in historical maps as an example of such a domain. In such cases, conventional computer vision (CV) approaches fail in unexpected unique regions such as map content area exceeding the map frame, while deep learning methods lack boundary localization accuracy. Therefore, we propose an efficient approach that combines conventional CV techniques with deep learning and practically eliminates their drawbacks. To do so, we redefine the learning objective of a simple fully convolutional network to make the training easier and the model more robust even with few training samples. The presented method provides excellent results compared to more sophisticated but solely deep learning or traditional computer vision techniques as shown in “MapSeg” segmentation competition, where all other approaches were significantly outperformed. We further propose two additional approaches that improve the original method and set a new state-of-the-art result on the MapSeg dataset. The methods are further tested on an extended version of the Map Border dataset to show their robustness.

Druh

D - Stať ve sborníku

CEP obor

—

OECD FORD obor

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Projekt

—

Návaznosti

S - Specificky vyzkum na vysokych skolach

Rok uplatnění

2023

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název statě ve sborníku

Document Analysis and Recognition – ICDAR 2023

ISBN

978-3-031-41675-0

ISSN

0302-9743

e-ISSN

1611-3349

Počet stran výsledku

14

Strana od-do

520-533

Název nakladatele

Springer

Místo vydání

neuveden

Místo konání akce

San José, CA, USA

Datum konání akce

21. 8. 2023

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

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