Scene motion detection in imagery with anisoplanatic optical turbulence using a tilt-variance-based Gaussian mixture model
Abstract
In long-range imaging applications, anisoplanatic atmospheric optical turbulence imparts spatially- and temporally varying blur and geometric distortions in acquired imagery. The ability to distinguish true scene motion from turbulence warping is important for many image-processing and analysis tasks. The authors present a scene-motion detection algorithm specifically designed to operate in the presence of anisoplanatic optical turbulence. The method models intensity fluctuations in each pixel with a Gaussian mixture model (GMM). The GMM uses knowledge of the turbulence tilt-variance statistics. We provide both quantitative and qualitative performance analyses and compare the proposed method to several state-of-the art algorithms. The image data are generated with an anisoplanatic numerical wave-propagation simulator that allows us to have motion truth. The subject technique outperforms the benchmark methods in our study.
Document Details
- Document Type
- Pub Defense Publication
- Publication Date
- Jul 12, 2021
- Source ID
- 10.1364/ao.424181
Entities
People
- Richard L. Van Hook
- Russell C Hardie
Organizations
- Air Force Research Laboratory
- University of Dayton