Graphical Geometric and Learning/Optimization-Based Methods in Statistical Signal and Image Processing Object Recognition and Data Fusion
Abstract
This report summarizes our accomplishments under this grant. Our research covers several interrelated areas: (a) the use of graphical, hierarchical, and multiresolution representations for the development of statistical modeling methodologies for complex phenomena and for the construction of scalable algorithms for the fusion of multiple heterogeneous sources of information; (b) the development of first principles methods for constructing statistical models for shapes and for the use of these models in developing robust and statistically optimal methods of shape estimation and recognition; and (c) the development of new statistical learning and optimization algorithms for feature extraction, signal and image restoration, and sensor fusion. Our research blends methods from several fields-statistics and probability, signal and image processing, mathematical physics, scientific computing, statistical learning theory, and differential geometry-to produce new approaches to emerging and challenging problems in signal and image processing, and each aspect of our program contains both fundamental research in mathematical sciences and important applications of direct relevance to Air Force missions. In particular, our research is relevant to automatic target recognition based on synthetic aperture radar and laser radar imagery; wide-area surveillance and information preparation of the battlefield; global awareness and higher-level fusion for situational assessment; and fusion of multiple heterogeneous sensors. In all of these areas we have contacts and interactions with AFRL staff and with industry involved in Air Force programs.
Document Details
- Document Type
- Technical Report
- Publication Date
- Mar 01, 2008
- Accession Number
- ADA480487
Entities
People
- Alan S. Willsky
Organizations
- Massachusetts Institute of Technology