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Open AccessArticle

Detecting Square Markers in Underwater Environments

Human Computer Interaction Laboratory, Faculty of Informatics, Masaryk University, Botanická 68a, 602 00 Brno, Czech Republic
3D Research s.r.l., University of Calabria, Rende, 87036 Cosenza, Italy
Photogrammetric Vision Laboratory, Department of Civil Engineering and Geomatics, Cyprus University of Technology, 3036 Limassol, Cyprus
Author to whom correspondence should be addressed.
Remote Sens. 2019, 11(4), 459;
Received: 16 January 2019 / Revised: 15 February 2019 / Accepted: 18 February 2019 / Published: 23 February 2019
(This article belongs to the Special Issue Underwater 3D Recording & Modelling)
Augmented reality can be deployed in various application domains, such as enhancing human vision, manufacturing, medicine, military, entertainment, and archeology. One of the least explored areas is the underwater environment. The main benefit of augmented reality in these environments is that it can help divers navigate to points of interest or present interesting information about archaeological and touristic sites (e.g., ruins of buildings, shipwrecks). However, the harsh sea environment affects computer vision algorithms and complicates the detection of objects, which is essential for augmented reality. This paper presents a new algorithm for the detection of fiducial markers that is tailored to underwater environments. It also proposes a method that generates synthetic images with such markers in these environments. This new detector is compared with existing solutions using synthetic images and images taken in the real world, showing that it performs better than other detectors: it finds more markers than faster algorithms and runs faster than robust algorithms that detect the same amount of markers. View Full-Text
Keywords: augmented reality; marker-based tracking; generating synthetic images; real time; cultural heritage augmented reality; marker-based tracking; generating synthetic images; real time; cultural heritage
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MDPI and ACS Style

Čejka, J.; Bruno, F.; Skarlatos, D.; Liarokapis, F. Detecting Square Markers in Underwater Environments. Remote Sens. 2019, 11, 459.

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