Impact of Dehazing on Underwater Marker Detection for Augmented Reality

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216224%3A14330%2F18%3A00103492" target="_blank" >RIV/00216224:14330/18:00103492 - isvavai.cz</a>

Result on the web

<a href="http://dx.doi.org/10.3389/frobt.2018.00092" target="_blank" >http://dx.doi.org/10.3389/frobt.2018.00092</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.3389/frobt.2018.00092" target="_blank" >10.3389/frobt.2018.00092</a>

Result language

angličtina

Original language name

Impact of Dehazing on Underwater Marker Detection for Augmented Reality

Original language description

Underwater augmented reality is a very challenging task and amongst several issues, one of the most crucial aspects involves real-time tracking. Particles present in water combined with the uneven absorption of light decrease the visibility in the underwater environment. Dehazing methods are used in many areas to improve the quality of digital image data that is degraded by the influence of the environment. This paper describes the visibility conditions affecting underwater scenes and shows existing dehazing techniques that successfully improve the quality of underwater images. Four underwater dehazing methods are selected for evaluation of their capability of improving the success of square marker detection in underwater videos. Two reviewed methods represent approaches of image restoration: Multi-Scale Fusion, and Bright Channel Prior. Another two methods evaluated, the Automatic Color Enhancement and the Screened Poisson Equation, are methods of image enhancement. The evaluation uses diverse test data set to evaluate different environmental conditions. Results of the evaluation show an increased number of successful marker detections in videos pre-processed by dehazing algorithms and evaluate the performance of each compared method. The Screened Poisson method performs slightly better to other methods across various tested environments, while Bright Channel Prior and Automatic Color Enhancement shows similarly positive results.

Czech name

—

Czech description

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Type

J_imp - Article in a specialist periodical, which is included in the Web of Science database

CEP classification

—

OECD FORD branch

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

Project

—

Continuities

S - Specificky vyzkum na vysokych skolach

Publication year

2018

Confidentiality

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

Name of the periodical

Frontiers in Robotics and AI

ISSN

2296-9144

e-ISSN

2296-9144

Volume of the periodical

5

Issue of the periodical within the volume

92

Country of publishing house

CH - SWITZERLAND

Number of pages

13

Pages from-to

1-13

UT code for WoS article

000441708500001

EID of the result in the Scopus database

2-s2.0-85061399836