Detecting Square Markers in Underwater Environments

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

doi:10.3390/rs11040459

Cited by 24 publications

(16 citation statements)

References 50 publications

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“…For these reasons, their use has spread in a wide variety of fields, such as surgery [9]- [11], distributed autonomous 3D printing [12], human-robot interaction [13], autonomous aerial vehicle landing [14], [15], patient positioning in radiotherapy treatments [16], study animal behaviour [17], human cognitive processes [18], 3D body scanning [19], [20], robotic grasping [21], underwater manipulation [22], etc.…”

Section: Introductionmentioning

confidence: 99%

Fractal Markers: A New Approach for Long-Range Marker Pose Estimation Under Occlusion

Romero-Ramire¹,

Muñoz-Salinas²,

Medina-Carnicer³

2019

IEEE Access

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Squared fiducial markers are a powerful tool for camera pose estimation in applications such as robots, unmanned vehicles and augmented reality. The four corners of a single marker are enough to estimate the pose of a calibrated camera. However, they have some limitations. First, the methods proposed for detection are ineffective under occlusion. A small occlusion in any part of the marker makes it undetectable. Second, the range at which they can be detected is limited by their size. Very big markers can be detected from a far distance, but as the camera approaches them, they are not fully visible, and thus they can not be detected. Small markers, however, can not be detected from large distances. This paper proposes solutions to the above-mentioned problems. We propose the Fractal Marker, a novel type of marker that is built as an aggregation of squared markers, one into another, in a recursive manner. Also, we proposed a novel method for detecting Fractal Markers under severe occlusions. The results of our experiments show that the proposed method achieves a wider detection range than traditional markers and great robustness to occlusion.

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Section: Introductionmentioning

confidence: 99%

Fractal Markers: A New Approach for Long-Range Marker Pose Estimation Under Occlusion

Romero-Ramire¹,

Muñoz-Salinas²,

Medina-Carnicer³

2019

IEEE Access

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show abstract

“…Several researchers have pointed out that the camera parameters and the accuracy that can be derived processing underwater images can be different from the parameters and accuracy that can be derived by processing images captured in the air. However, while the processing of images underwater can be more challenging [16], it does not mean that the accuracy which can be achieved has to be worse [17].…”

Section: Introductionmentioning

confidence: 99%

Investigation of Chromatic Aberration and Its Influence on the Processing of Underwater Imagery

Helmholz

Lichti

2020

Remote Sensing

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The number of researchers utilising imagery for the 3D reconstruction of underwater natural (e.g., reefs) and man-made structures (e.g., shipwrecks) is increasing. Often, the same procedures and software solutions are used for processing the images as in-air without considering additional aberrations that can be caused by the change of the medium from air to water. For instance, several publications mention the presence of chromatic aberration (CA). The aim of this paper is to investigate CA effects in low-cost camera systems (several GoPro cameras) operated in an underwater environment. We found that underwater and in-air distortion profiles differed by more than 1000 times in terms of maximum displacement and in terms of curvature. Moreover, significant CA effects were found in the underwater profiles that did not exist in-air. Furthermore, the paper investigates the effect of adjustment constraints imposed on the underwater self-calibration and the reliability of the interior orientation parameters. The analysis of the precision shows that in-air RMS values are just due to random errors. In contrast, the underwater calibration RMS values are 3x-6x higher than the exterior orientation parameter (EOP) precision, so these values contain both random error and the systematic effects from the CA. The accuracy assessment shows significant differences.

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“…Underwater imaging has found important applications in diverse research areas such as marine biology and archaeology [1,2], underwater surveying and mapping [3], and underwater target detection [4,5]. However, captured underwater images are generally degraded by scattering and absorption.…”

Section: Introductionmentioning

confidence: 99%

Underwater Image Restoration Based on a Parallel Convolutional Neural Network

Wang

et al. 2019

Remote Sensing

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Restoring degraded underwater images is a challenging ill-posed problem. The existing prior-based approaches have limited performance in many situations due to the reliance on handcrafted features. In this paper, we propose an effective convolutional neural network (CNN) for underwater image restoration. The proposed network consists of two paralleled branches: a transmission estimation network (T-network) and a global ambient light estimation network (A-network); in particular, the T-network employs cross-layer connection and multi-scale estimation to prevent halo artifacts and to preserve edge features. The estimates produced by these two branches are leveraged to restore the clear image according to the underwater optical imaging model. Moreover, we develop a new underwater image synthesizing method for building the training datasets, which can simulate images captured in various underwater environments. Experimental results based on synthetic and real images demonstrate that our restored underwater images exhibit more natural color correction and better visibility improvement against several state-of-the-art methods.

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Detecting Square Markers in Underwater Environments

Cited by 24 publications

References 50 publications

Fractal Markers: A New Approach for Long-Range Marker Pose Estimation Under Occlusion

Fractal Markers: A New Approach for Long-Range Marker Pose Estimation Under Occlusion

Investigation of Chromatic Aberration and Its Influence on the Processing of Underwater Imagery

Underwater Image Restoration Based on a Parallel Convolutional Neural Network

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