Optical Image Blending for Underwater Mosaics

Typical problems for creation of consistent underwater mosaic are misalignment and inhomogeneous illumination of the image frames, which causes visible seams and consequently complicates post-processing of the mosaics such as object recognition and shape extraction. Two recently developed image blending methods were explored in the literature: gradient domain stitching and graph-cut method, and they allow for improvement of illumination inconsistency and ghosting effects, respectively. However, due to the specifics of underwater imagery, these two methods cannot be used within a straightforward manner. In this paper, a new improved blending algorithm is proposed based on these two methods. By comparing with the previous methods from a perceptual point of view and as a potential input for pattern recognition algorithms, our results show an improvement in decreasing the mosaic degradation due to feature doubling and rapid illumination change

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

Measurement of Micro-bathymetry with a GOPRO Underwater Stereo Camera Pair

A GO-PRO underwater stereo camera kit has been used to measure the 3D topography (bathymetry) of a patch of seafloor producing a point cloud with a spatial data density of 15 measurements per 3 mm grid square and an standard deviation of less than 1 cm A GO-PRO camera is a fixed focus, 11 megapixel, still-frame (or 1080p high-definition video) camera, whose small form-factor and water-proof housing has made it popular with sports enthusiasts. A stereo camera kit is available providing a waterproof housing (to 61 m / 200 ft) for a pair of cameras. Measures of seafloor micro-bathymetrycapable of resolving seafloor features less than 1 cm in amplitude were possible from the stereoreconstruction. Bathymetric measurements of this scale provide important ground-truth data and boundary condition information for modeling of larger scale processes whose details depend on small-scale variations. Examples include modeling of turbulent water layers, seafloor sediment transfer and acoustic backscatter from bathymetric echo sounders

UVSD: Software for Detection of Color Underwater Features

Underwater Video Spot Detector (UVSD) is a software package designed to analyze underwater video for continuous spatial measurements (path traveled, distance to the bottom, roughness of the surface etc.) Laser beams of known geometry are often used in underwater imagery to estimate the distance to the bottom. This estimation is based on the manual detection of laser spots which is labor intensive and time consuming so usually only a few frames can be processed this way. This allows for spatial measurements on single frames (distance to the bottom, size of objects on the sea-bottom), but not for the whole video transect. We propose algorithms and a software package implementing them for the semi-automatic detection of laser spots throughout a video which can significantly increase the effectiveness of spatial measurements. The algorithm for spot detection is based on the Support Vector Machines approach to Artificial Intelligence. The user is only required to specify on certain frames the points he or she thinks are laser dots (to train an SVM model), and then this model is used by the program to detect the laser dots on the rest of the video. As a result the precise (precision is only limited by quality of the video) spatial scale is set up for every frame. This can be used to improve video mosaics of the sea-bottom. The temporal correlation between spot movements changes and their shape provides the information about sediment roughness. Simultaneous spot movements indicate changing distance to the bottom; while uncorrelated changes indicate small local bumps. UVSD can be applied to quickly identify and quantify seafloor habitat patches, help visualize habitats and benthic organisms within large-scale landscapes, and estimate transect length and area surveyed along video transects

A Robust Quasi-dense Matching Approach for Underwater Images

While different techniques for finding dense correspondences in images taken in air have achieved significant success, application of these techniques to underwater imagery still presents a serious challenge, especially in the case of “monocular stereo” when images constituting a stereo pair are acquired asynchronously. This is generally because of the poor image quality which is inherent to imaging in aquatic environments (blurriness, range-dependent brightness and color variations, time-varying water column disturbances, etc.). The goal of this research is to develop a technique resulting in maximal number of successful matches (conjugate points) in two overlapping images. We propose a quasi-dense matching approach which works reliably for underwater imagery. The proposed approach starts with a sparse set of highly robust matches (seeds) and expands pair-wise matches into their neighborhoods. The Adaptive Least Square Matching (ALSM) is used during the search process to establish new matches to increase the robustness of the solution and avoid mismatches. Experiments on a typical underwater image dataset demonstrate promising results

On Importance of Acoustic Backscatter Corrections for Texture-based Seafloor Characterization

Seafloor segmentation and characterization based on local textural properties of acoustic backscatter has been a subject of research since 1980s due to the highly textured appearance of sonar images. The approach consists of subdivision of sonar image in a set of patches of certain size and calculation of a vector of features reflecting the patch texture. Advance of multibeam echosounders (MBES) allowed application of texture-based techniques to real geographical space, and predicted boundaries between acoustic facies became experimentally verifiable. However, acoustic return from uncalibrated MBES produces artifacts in backscatter mosaics, which in turn affects accuracy of delineation. Development of Geocoder allowed creation of more visually consistent images, and reduced the number of factors influencing mosaic creation. It is intuitively clear that more accurate backscatter mosaics lead to more reliable classification results. However, this statement has never been thoroughly verified. It has not been investigated which corrections are important for texture-based characterization and which are not essential. In this paper the authors are investigating the Stanton Banks common dataset. Raw data files from the dataset have been processed by the Geocoder at different levels of corrections. Each processing resulted in a backscatter mosaic demonstrating artifacts of different levels of severity. Mosaics then underwent textural analysis and unsupervised classification using Matlab package SonarClass. Results of seafloor characterization corresponding to varying levels of corrections were finally compared to the one generated by the best possible mosaic (the one embodying all the available corrections), providing an indicator of classification accuracy and giving guidance about which mosaic corrections are crucial for acoustic classification and which could be safely ignored

Probabilistic Reconstruction of Color for Species’ Classification Underwater

Color is probably the most informative cue for object recognition and classification in natural scenes. Difference in shades can indicate to the biologist the potential for diversity of species or stress on the habitats. However, severe color distortions may occur in underwater imagery due to wavelength-dependent attenuation of light. Affordable tri-chromatic sensors are used to record the ambient light condition and color correct the imagery, but results show that this approach works reliably only under highly controllable conditions. This paper proposes an approach that combines hyperspectral data collected for the object of interest, hardware properties of the imaging sensor, and exterior conditions (optical properties of water and illumination) with tri-chromatic underwater imagery. Due to ambiguity of color reconstruction underwater, demonstrated in the paper, a probabilistic approach is used for classification that allows the identification of the object of interest from other objects

Ambiguity of Underwater Color Measurement and Color-based Habitat Classification

The paper discusses ambiguities in recording color underwater. Routinely collected RGB imagery can be used for classification and recognition utilizing the proposed probabilistic approach. The device for collection of spectral signatures, necessary for this approach is described

Seafloor Segmentation Based on Bathymetric Measurements from Multibeam Echosounders Data

Bathymetric data depicts the geomorphology of the seabottom and allows characterization of spatial distributions of apparent benthic habitats. The variability of seafloor topography can be defined as a texture. This prompts for the application of well developed image processing techniques for automatic delineation of regions with clucially different physiographic characteristics. In the present paper histograms of biologically motivated invariant image attributes are used for characterization of local geomorphological feahires. This technique can be naturally applied in a range of spatial scales. Local feature vectors are then submitted to a procedure which divides the set into a number of clusters each representing a distinct type of the seafloor. Prior knowledge about benthic habitat locations allows the use of supervised classification, by training a Suppolt Vector Machine on a chosen data set, and then applying the developed model to a full set. The classification method is shown to perform well on the multibeam echosounder (MBES) data from Piscataqua River, New Hampshire, USA