THIS GRANT IS A CONTINUATION OF N000141410368 Multi-aspect underwater scene understanding
Abstract
1. Develop physics-based parametric sensing models and associated hierarchical Bayesian parameter estimation frameworks in order to characterize multiple seabed types with invariant parameters. The parameters of interest are invariant to sensing geometry including factors such as aspect angle, height of the SAS array off of the sea-floor, range, etc. The sensing models would be valid over typical sonar ranges and for a variety of sensing aspects. 2. Investigate Bayesian approaches for combining information obtained from multiple passes, ranges, and aspects to describe spatial regions and the associated sea-bed types. 3. Leverage previously-developed algorithms for feature extraction and sea-bed segmentation to develop an autonomous end-to-end system for underwater scene understanding.
Document Details
- Document Type
- DoD Grant Award
- Publication Date
- Aug 12, 2016
- Source ID
- N000141612384
Entities
People
- Alina Zare
Organizations
- Office of Naval Research
- United States Navy
- University of Missouri System