Enhancement of Underwater Video Mosaics for Post-Processing

Mosaics of seafloor created from still images or video acquired underwater have proved to be useful for construction of maps of forensic and archeological sites, species\u27 abundance estimates, habitat characterization, etc. Images taken by a camera mounted on a stable platform are registered (at first pair-wise and then globally) and assembled in a high resolution visual map of the surveyed area. While this map is usually sufficient for a human orientation and even quantitative measurements, it often contains artifacts that complicate an automatic post-processing (for example, extraction of shapes for organism counting, or segmentation for habitat characterization). The most prominent artifacts are inter-frame seams caused by inhomogeneous artificial illumination, and local feature misalignments due to parallax effects - result of an attempt to represent a 3D world on a 2D map. In this paper we propose two image processing techniques for mosaic quality enhancement - median mosaic-based illumination correction suppressing appearance of inter-frame seams, and micro warping decreasing influence of parallax effects

Visually pleasant blending techniques in underwater mosaicing

of two or more images that are then combined into a single and usually larger one. The applications of mosaicing comprehend panoramic photography, super-resolution, virtual environments and vision based navigation systems, as a most relevant exponents. Besides generic camera issues as geometric and chromatic distortions, underwa-ter images are aff ected by particular factors as non-uniform illumination, caustics, blurring, suspended particles and scattering, making even more diffi cult the alignment and blend-ing. The aim of this work is to perform a re-view on the existing image blending techniques specially focusing the study on its application on the underwater imaging

Blending techniques for underwater photomosaics

The creation of consistent underwater photomosaics is typically hampered by local misalignments and inhomogeneous illumination of the image frames, which introduce visible seams that complicate post processing of the mosaics for object recognition and shape extraction. In this thesis, methods are proposed to improve blending techniques for underwater photomosaics and the results are compared with traditional methods. Five specific techniques drawn from various areas of image processing, computer vision, and computer graphics have been tested: illumination correction based on the median mosaic, thin plate spline warping, perspective warping, graph-cut applied in the gradient domain and in the wavelet domain. A combination of the first two methods yields globally homogeneous underwater photomosaics with preserved continuous features. Further improvements are obtained with the graph-cut technique applied in the spatial domain

Advances in Simultaneous Localization and Mapping in Confined Underwater Environments Using Sonar and Optical Imaging.

This thesis reports on the incorporation of surface information into a probabilistic simultaneous localization and mapping (SLAM) framework used on an autonomous underwater vehicle (AUV) designed for underwater inspection. AUVs operating in cluttered underwater environments, such as ship hulls or dams, are commonly equipped with Doppler-based sensors, which---in addition to navigation---provide a sparse representation of the environment in the form of a three-dimensional (3D) point cloud. The goal of this thesis is to develop perceptual algorithms that take full advantage of these sparse observations for correcting navigational drift and building a model of the environment. In particular, we focus on three objectives. First, we introduce a novel representation of this 3D point cloud as collections of planar features arranged in a factor graph. This factor graph representation probabalistically infers the spatial arrangement of each planar segment and can effectively model smooth surfaces (such as a ship hull). Second, we show how this technique can produce 3D models that serve as input to our pipeline that produces the first-ever 3D photomosaics using a two-dimensional (2D) imaging sonar. Finally, we propose a model-assisted bundle adjustment (BA) framework that allows for robust registration between surfaces observed from a Doppler sensor and visual features detected from optical images. Throughout this thesis, we show methods that produce 3D photomosaics using a combination of triangular meshes (derived from our SLAM framework or given a-priori), optical images, and sonar images. Overall, the contributions of this thesis greatly increase the accuracy, reliability, and utility of in-water ship hull inspection with AUVs despite the challenges they face in underwater environments. We provide results using the Hovering Autonomous Underwater Vehicle (HAUV) for autonomous ship hull inspection, which serves as the primary testbed for the algorithms presented in this thesis. The sensor payload of the HAUV consists primarily of: a Doppler velocity log (DVL) for underwater navigation and ranging, monocular and stereo cameras, and---for some applications---an imaging sonar.PhDElectrical Engineering: SystemsUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/120750/1/paulozog_1.pd

A Low-Complexity Mosaicing Algorithm for Stock Assessment of Seabed-Burrowing Species

Peer-reviewed This article has been accepted for inclusion in a future issue of this journal. Content is final as presented, with the exception of pagination. Manuscript received January 27, 2017; revised August 17, 2017 and December 27, 2017; accepted February 16, 2018. Published in: IEEE Journal of Oceanic Engineering ( Early Access ) DOI: 10.1109/JOE.2018.2808973This paper proposes an algorithm for mosaicing videos generated during stock assessment of seabed-burrowing species. In these surveys, video transects of the seabed are captured and the population is estimated by counting the number of burrows in the video. The mosaicing algorithm is designed to process a large amount of video data and summarize the relevant features for the survey in a single image. Hence, the algorithm is designed to be computationally inexpensive while maintaining a high degree of robustness. We adopt a registration algorithm that employs a simple translational motion model and generates a mapping to the mosaic coordinate system using a concatenation of frame-by-frame homographies. A temporal smoothness prior is used in a maximum a posteriori homography estimation algorithm to reduce noise in the motion parameters in images with small amounts of texture detail. A multiband blending scheme renders the mosaic and is optimized for the application requirements. Tests on a large data set show that the algorithm is robust enough to allow the use of mosaics as a medium for burrow counting. This will increase the verifiability of the stock assessments as well as generate a ground truth data set for the learning of an automated burrow counting algorithm.This work was supported by the Science Foundation Ireland under Award SFI-PI 08/IN.1/I2112

Underwater archaeological mosaicing

Archaeological mosaicing is one of the challenges of the computer vision community and it can be faced in a 2D or 3D approach. This contribution regards a methodology to do a mosaic of an underwater bi-dimensional scene. A number of problems arise from the acquisition of images by a remote operated vehicle. Radial distortion, poor luminosity, cloud water, presence of artefacts are part of the issues that can occur; for instance, the radial distortion has been corrected to improve the quality of the input images. Keypoints detection (through SIFT transform), Singular Value Decomposition, Random Samples Consensus are some of the techniques applied in our method. This contribution regards the mosaicing of seabed landscapes, in order to represent higher resolution photos of whole sites with wrecks in a fast and safe fashion. A stereo vision system has been arranged by adding two cameras to the payload aboard a Remotely Operated Vehicle. A number of problems arise due to poor luminosity, cloudy water, water distortion and presence of artifacts. A robust algorithm has been de¯ned to reduce the radial distortion of the camera lenses and to enhance the results

Seabed fluid flow-related processes: evidence and quantification based on high-resolution imaging techniques and GIS analyses

This work provides new insights on different aspects of seabed fluid flow processes based on seafloor observations. The methods used entirely rely on ROV-based high-resolution imaging and mapping techniques. Optical data are used to produce visual maps of the seafloor, in the form of geo-referenced video- and photo-mosaics, whereas acoustic techniques allow mapping the micro-bathymetry of the seabed, as well as the signal reflectivity of the sediment surface and of the water column. This work presents three case studies, about two sites of seabed fluid flow: the Menez Gwen hydrothermal vent on the MAR and the REGAB pockmark in the Lower Congo Basin. On the technical side, some of the high-resolution techniques used in this thesis are not commonly used by the marine scientific community. This is particularly the case for large-area photo-mosaics. Although the interest in mosaicking is growing, there are still no tools freely and readily available to scientists to routinely construct large-area photo-mosaics. Therefore, this work presents a MATLAB toolbox for large-area photo-mosaicking (LAPM toolbox), which was developed as part of this thesis

Development Of A High Performance Mosaicing And Super-Resolution Algorithm

In this dissertation, a high-performance mosaicing and super-resolution algorithm is described. The scale invariant feature transform (SIFT)-based mosaicing algorithm builds an initial mosaic which is iteratively updated by the robust super resolution algorithm to achieve the final high-resolution mosaic. Two different types of datasets are used for testing: high altitude balloon data and unmanned aerial vehicle data. To evaluate our algorithm, five performance metrics are employed: mean square error, peak signal to noise ratio, singular value decomposition, slope of reciprocal singular value curve, and cumulative probability of blur detection. Extensive testing shows that the proposed algorithm is effective in improving the captured aerial data and the performance metrics are accurate in quantifying the evaluation of the algorithm