Improving color correction for underwater image surveys

Kaeli, Jeffrey W.; Singh, Hanumant; Murphy, Chris; Kunz, C.

doi:10.23919/oceans.2011.6107143

Cited by 33 publications

(21 citation statements)

References 9 publications

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“…In practice, approximately 12 iterations are required for convergence. Figure 3 illustrates the estimation process as well as the validity of both proposed assumptions with the upper line showing the result of the ambient light estimate (7), and the lower curve the attenuation coefficient from (6).…”

Section: B Estimating Visibility Coefficientsmentioning

confidence: 99%

Simultaneous underwater visibility assessment, enhancement and improved stereo

Röser

Dunbabin

Geiger

2014

2014 IEEE International Conference on Robotics and Automation (ICRA)

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Vision-based underwater navigation and obstacle avoidance demands robust computer vision algorithms, particularly for operation in turbid water with reduced visibility. This paper describes a novel method for the simultaneous underwater image quality assessment, visibility enhancement and disparity computation to increase stereo range resolution under dynamic, natural lighting and turbid conditions. The technique estimates the visibility properties from a sparse 3D map of the original degraded image using a physical underwater light attenuation model. Firstly, an iterated distance-adaptive image contrast enhancement enables a dense disparity computation and visibility estimation. Secondly, using a light attenuation model for ocean water, a color corrected stereo underwater image is obtained along with a visibility distance estimate. Experimental results in shallow, naturally lit, high-turbidity coastal environments show the proposed technique improves range estimation over the original images as well as image quality and color for habitat classification. Furthermore, the recursiveness and robustness of the technique allows implementation onboard an Autonomous Underwater Vehicle for improving navigation and obstacle avoidance performance.

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Section: B Estimating Visibility Coefficientsmentioning

confidence: 99%

Simultaneous underwater visibility assessment, enhancement and improved stereo

Röser

Dunbabin

Geiger

2014

2014 IEEE International Conference on Robotics and Automation (ICRA)

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“…For restoring color, methods range from simple white balancing [23,113] to Markov Random Fields [167], fusion-based techniques [5], and even colored strobes [172]. In the case of many robotic imaging platforms, additional sensor information may be useful for correction as well [11,15,81,85,131,137].…”

Section: Underwater Imagingmentioning

confidence: 99%

“…Velocity Log (DVL), can be used under the assumption that the bottom is locally planar [85]. Stereo camera pairs [15] or a sheet laser in the camera's field of view [11] can similarly provide bathymetry information for modeling attenuation path lengths.…”

Section: Beyond a Single Imagementioning

confidence: 99%

Computational strategies for understanding underwater optical image datasets

Kaeli¹

2013

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A fundamental problem in autonomous underwater robotics is the high latency between the capture of image data and the time at which operators are able to gain a visual understanding of the survey environment. Typical missions can generate imagery at rates hundreds of times greater than highly compressed images can be transmited acoustically, delaying that understanding until after the vehicle has been recovered and the data analyzed. While automated classification algorithms can lessen the burden on human annotators after a mission, most are too computationally expensive or lack the robustness to run in situ on a vehicle. Fast algorithms designed for mission-time performance could lessen the latency of understanding by producing low-bandwidth semantic maps of the survey area that can then be telemetered back to operators during a mission.This thesis presents a lightweight framework for processing imagery in real time aboard a robotic vehicle. We begin with a review of pre-processing techniques for correcting illumination and attenuation artifacts in underwater images, presenting our own approach based on multi-sensor fusion and a strong physical model. Next, we construct a novel image pyramid structure that can reduce the complexity necessary to compute features across multiple scales by an order of magnitude and recommend features which are fast to compute and invariant to underwater artifacts. Finally, we implement our framework on real underwater datasets and demonstrate how it can be used to select summary images for the purpose of creating low-bandwidth semantic maps capable of being transmitted acoustically.

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“…This combination is simple, robust to image rotation, and effective at finding features in images with low frame-to-frame overlap [Pizarro and Singh, 2003]. Image preprocessing, and color correction in particular, is an ongoing research problem; see [Kaeli et al, 2011] for recent developments, and [Vasilescu et al, 2010] for a formulation based on adaptive lighting. A typical image pair with matched features is shown in figure 3.…”

Section: Image Matchingmentioning

confidence: 99%

Map Building Fusing Acoustic and Visual Information using Autonomous Underwater Vehicles

Kunz

Singh

2013

Journal of Field Robotics

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We present a system for automatically building 3-D maps of underwater terrain fusing visual data from a single camera with range data from multibeam sonar. The six-degree of freedom location of the camera relative to the navigation frame is derived as part of the mapping process, as are the attitude offsets of the multibeam head and the on-board velocity sensor. The system uses pose graph optimization and the square root information smoothing and mapping framework to simultaneously solve for the robot's trajectory, the map, and the camera location in the robot's frame. Matched visual features are treated within the pose graph as images of 3-D landmarks, while multibeam bathymetry submap matches are used to impose relative pose constraints linking robot poses from distinct tracklines of the dive trajectory. The navigation and mapping system presented works under a variety of deployment scenarios, on robots with diverse sensor suites. Results of using the system to map the structure and appearance of a section of coral reef are presented using data acquired by the Seabed autonomous underwater vehicle.

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Improving color correction for underwater image surveys

Cited by 33 publications

References 9 publications

Simultaneous underwater visibility assessment, enhancement and improved stereo

Simultaneous underwater visibility assessment, enhancement and improved stereo

Computational strategies for understanding underwater optical image datasets

Map Building Fusing Acoustic and Visual Information using Autonomous Underwater Vehicles

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