Abstract

Han Hu will present, “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.

Josh Humberston will present, “Estimating surficial mud fraction in an estuarine environment using empirical orthogonal functions of acoustic waveform properties from an SBES.”

Seafloor classification and environmental assessment in shallow marine waters are crucial to habitat mapping, coastal management policies and maintaining navigational waterways. Unfortunately, software packages aimed to make these assessments using remote acoustic methods have had limited success in shallow waters, often leading to sparse quantifiable data to support marine policy decisions. The problem is exacerbated by the highly variable bottom composition of typical coastal and estuarine environments. In this work, field observations from an Odom Echotrac vertical-incidence echosounder with a dual-frequency (24 and 200 khz) transducer is used to estimate seafloor sediment characteristics in estuarine environment with variable bottom types. Observations were obtained in water depths ranging 0.5-24 m of the Little Bay, NH, during February and March, 2013. Comparison between backscatter waveform properties and sediment grain size distributions show varied degrees of predictive capability. In an effort to better capture the collective effects of seafloor sediment's composition on acoustic returns, empirical orthogonal functions (EOF’s) are computed from waveform properties and compared with observed mud-sand fractions. Results from this analysis will be presented and discussed. This empirical analysis provides an objective means to interpret acoustic backscatter, an important step towards a widespread quantitative assessment of shallow water seafloor sediments. This work was funded by both NOAA and NH DES.

Publication Date

3-14-2014

Document Type

Presentation

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