Impact of Dehazing on Underwater Marker Detection for Augmented Reality

Žuži, Marek; Čejka, Jan; Bruno, Fabio; Skarlatos, Dimitrios; Liarokapis, Fotis

doi:10.3389/frobt.2018.00092

Cited by 11 publications

(4 citation statements)

References 48 publications

(63 reference statements)

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“…The experimental results showed that the new method performed better than original one. In [19], Zuzi used the three dehazing techniques to enhance the underwater images where there were artificial markers. The experimental results showed that SP (Screened Poisson Equation for image contrast enhancement) outperformed the other two enhancement algorithms, BCP (Bright Channel Prior) and ACE (Automatic Color Enhancement), while all of them presented that the marker detection was completed in a shorter time.…”

Section: Related Workmentioning

confidence: 99%

An Underwater Visual Navigation Method Based on Multiple ArUco Markers

Haroutunian

Murphy

et al. 2021

JMSE

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Underwater navigation presents crucial issues because of the rapid attenuation of electronic magnetic waves. The conventional underwater navigation methods are achieved by acoustic equipment, such as the ultra-short-baseline localisation systems and Doppler velocity logs, etc. However, they suffer from low fresh rate, low bandwidth, environmental disturbance and high cost. In the paper, a novel underwater visual navigation is investigated based on the multiple ArUco markers. Unlike other underwater navigation approaches based on the artificial markers, the noise model of the pose estimation of a single marker and an optimal algorithm of the multiple markers are developed to increase the precision of the method. The experimental tests are conducted in the towing tank. The results show that the proposed method is able to localise the underwater vehicle accurately.

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Section: Related Workmentioning

confidence: 99%

An Underwater Visual Navigation Method Based on Multiple ArUco Markers

Haroutunian

Murphy

et al. 2021

JMSE

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“…The evaluation of circular self-similar markers [32] in open sea environments was presented in [33]. The performance of marker detection and image-improving algorithms was also evaluated by Žuži et al [34] andČejka et al [35] on videos taken in the sea. Several authors focused on the registration of images and based their comparisons on the number of detected and matched SIFT features.…”

Section: Related Workmentioning

confidence: 99%

Detecting Square Markers in Underwater Environments

et al. 2019

Self Cite

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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.

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“…All these conditions are inevitable, and although the progress of underwater camera’s sensors technology together with the knowledge on the capturing process of objects in motion Silvatti et al (2013) can help to reduce some of their effects, adding complexity to the system will always have an impact in the cost of developing any underwater robotic platform. In recent years, the problem of detection and tracking of underwater moving objects has received considerable attention Zuzi et al (2018) ; Panda and Nanda (2020) due to wide applications in oceanographic research. It is clear thus, the need of an autonomous navigation system capable of detecting and tracking underwater moving objects of interest.…”

Section: Introductionmentioning

confidence: 99%

A Mirror-Based Active Vision System for Underwater Robots: From the Design to Active Object Tracking Application

Noel

Torres-Méndez

2021

Front. Robot. AI

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A mirror-based active system capable of changing the view’s direction of a pre-existing fixed camera is presented. The aim of this research work is to extend the perceptual tracking capabilities of an underwater robot without altering its structure. The ability to control the view’s direction allows the robot to explore its entire surroundings without any actual displacement, which can be useful for more effective motion planning and for different navigation strategies, such as object tracking and/or obstacle evasion, which are of great importance for natural preservation in environments as complex and fragile as coral reefs. Active vision systems based on mirrors had been used mainly in terrestrial platforms to capture the motion of fast projectiles using high-speed cameras of considerable size and weight, but they had not been used on underwater platforms. In this sense, our approach incorporates a lightweight design adapted to an underwater robot using affordable and easy-access technology (i.e., 3D printing). Our active system consists of two arranged mirrors, one of which remains static in front of the robot’s camera, while the orientation of the second mirror is controlled by two servomotors. Object tracking is performed by using only the pixels contained on the homography of a defined area in the active mirror. HSV color space is used to reduce lighting change effects. Since color and geometry information of the tracking object are previously known, a window filter is applied over the H-channel for color blobs detection, then, noise is filtered and the object’s centroid is estimated. If the object is lost, a Kalman filter is applied to predict its position. Finally, with this information, an image PD controller computes the servomotor articular values. We have carried out experiments in real environments, testing our active vision system in an object-tracking application where an artificial object is manually displaced on the periphery of the robot and the mirror system is automatically reconfigured to keep such object focused by the camera, having satisfactory results in real time for detecting objects of low complexity and in poor lighting conditions.

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Impact of Dehazing on Underwater Marker Detection for Augmented Reality

Cited by 11 publications

References 48 publications

An Underwater Visual Navigation Method Based on Multiple ArUco Markers

An Underwater Visual Navigation Method Based on Multiple ArUco Markers

Detecting Square Markers in Underwater Environments

A Mirror-Based Active Vision System for Underwater Robots: From the Design to Active Object Tracking Application

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