Direct linear and refraction-invariant pose estimation and calibration model for underwater imaging

Elnashef, Bashar; Filin, Sagi

doi:10.1016/j.isprsjprs.2019.06.004

Cited by 28 publications

(27 citation statements)

References 19 publications

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“…Here a Nikon D70s with a fixed 24 mm lens shielded by an IKELITE housing, and two 2-D rigid plates were immersed in water, both are used for testing the quality of the pose estimation. A dry calibration phase was performed while the camera was in the housing (Elnashef and Filin, 2019), then, a wet calibration was carried out using a total of five images. The a posteriori standard deviation of the adjustment wasσ0 = ±0.42 pixels.…”

Section: Real World -Open Sea Experimentsmentioning

confidence: 99%

Direct Estimation of the Relative Orientation in Underwater Environment

Elnashef

Filin

2020

ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci.

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Abstract. While accuracy, detail, and limited time on site make photogrammetry a valuable means for underwater mapping, the establishment of reference control networks in such settings is oftentimes difficult. In that respect, the use of the coplanarity constraint becomes a valuable solution as it requires neither knowledge of object space coordinates nor setting a reference control network. Nonetheless, imaging in such domains is subjected to non-linear and depth-dependent distortions, which are caused by refractive media that alter the standard single viewpoint geometry. Accordingly, the coplanarity relation, as formulated for the in-air case does not hold in such environment and methods that have been proposed thus far for geometrical modeling of its effect require knowledge of object-space quantities. In this paper we propose a geometrically-driven approach which fulfills the coplanarity condition and thereby requires no knowledge of object space data. We also study a linear model for the establishment of this constraints. Clearly, a linear form requires neither first approximations nor iterative convergence scheme. Such an approach may prove useful not only for object space reconstruction but also as a preparatory step for application of bundle block adjustment and for outlier detection. All are key features in photogrammetric practices. Results show that no unique setup is needed for estimating the relative orientation parameters using the model and that high levels of accuracy can be achieved.

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Section: Real World -Open Sea Experimentsmentioning

confidence: 99%

Direct Estimation of the Relative Orientation in Underwater Environment

Elnashef

Filin

2020

ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci.

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“…Even though aerial imagery is well established for monitoring and 3D recording of dry landscapes and urban areas, when it comes to bathymetric applications, errors are introduced due, mainly, to the water refraction. According to the literature [7][8][9], thorough calibration is sufficient to correct the effects of refraction in the case of in-water photogrammetric procedures. On the contrary, in through-water (two-media) cases, the sea surface undulations due to waves [10,11] and the magnitude of refraction, which differ practically at each point of every image, lead to unstable solutions [12,13].…”

Section: The Impact Of the Refraction Effect On Structure From Motionmentioning

confidence: 99%

DepthLearn: Learning to Correct the Refraction on Point Clouds Derived from Aerial Imagery for Accurate Dense Shallow Water Bathymetry Based on SVMs-Fusion with LiDAR Point Clouds

et al. 2019

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The determination of accurate bathymetric information is a key element for near offshore activities; hydrological studies, such as coastal engineering applications, sedimentary processes, hydrographic surveying, archaeological mapping and biological research. Through structure from motion (SfM) and multi-view-stereo (MVS) techniques, aerial imagery can provide a low-cost alternative compared to bathymetric LiDAR (Light Detection and Ranging) surveys, as it offers additional important visual information and higher spatial resolution. Nevertheless, water refraction poses significant challenges on depth determination. Till now, this problem has been addressed through customized image-based refraction correction algorithms or by modifying the collinearity equation. In this article, in order to overcome the water refraction errors in a massive and accurate way, we employ machine learning tools, which are able to learn the systematic underestimation of the estimated depths. In particular, an SVR (support vector regression) model was developed, based on known depth observations from bathymetric LiDAR surveys, which is able to accurately recover bathymetry from point clouds derived from SfM-MVS procedures. Experimental results and validation were based on datasets derived from different test-sites, and demonstrated the high potential of our approach. Moreover, we exploited the fusion of LiDAR and image-based point clouds towards addressing challenges of both modalities in problematic areas.

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“…Heng Tian et al designed a Rapid underwater target enhancement method with polarimetric imaging [7]. A linear model was introduced by Bashar Elnashef and Sagi Filin to enhance accuracy with minimal time [8]. However, with reduced visibility and imaging distance, the accuracy was found to be less.…”

Section: Related Workmentioning

confidence: 99%

Retinex Color Balanced Piecewise Contrast and Fuzzy Trilateral Filter for Underwater Image Enhancement

Parameswari*,

Srinath

2019

IJRTE

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 Abstract: Over the past few years, underwater observation has become an active research area. Due to the higher rate of image degradation in the underwater environment, image enhancement has become one of the problems to be addressed for the underwater research. Underwater images face limitations like color correction, white balance, color contrast and haze. To overcome those problems, a novel fusion method based on the Retinex Color-balanced Piecewise-contrast and Fuzzy Reinforced Trilateral Filter (RCP-FRTF) method is presented for underwater image improvement. With the underwater image given as input, to start with, a color correction model based on the Retinex multi proportions is presented. With the color corrected output obtained, an Eigen-based White Balancing method is applied to generate color balanced model. With the color balanced underwater image, color contrasting is performed using the Piecewise Linear Color Contrast model. After obtaining the latter, the contrast is said to be improved to a better level. Finally, to generate a haze-free image a Fuzzy Reinforced Trilateral filter is applied. The enhanced and de-hazed images are distinguished by reduced noise level, thus enhanced visibility and contrast while the finest edges are enhanced. The proposed RCP-FRTF method provides better performance in terms of PSNR, computational time, complexity and accuracy as compared to conventional methods.

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Direct linear and refraction-invariant pose estimation and calibration model for underwater imaging

Cited by 28 publications

References 19 publications

Direct Estimation of the Relative Orientation in Underwater Environment

Direct Estimation of the Relative Orientation in Underwater Environment

DepthLearn: Learning to Correct the Refraction on Point Clouds Derived from Aerial Imagery for Accurate Dense Shallow Water Bathymetry Based on SVMs-Fusion with LiDAR Point Clouds

Retinex Color Balanced Piecewise Contrast and Fuzzy Trilateral Filter for Underwater Image Enhancement

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