Multiresolution, Geometric, and Learning Methods in Statistical Image Processing, Object Recognition, and Sensor Fusion

Authors: Alan S. Willsky; MASSACHUSETTS INST OF TECH CAMBRIDGE LAB FOR INFORMATION AND DECISION SYSTEMS

Abstract:

This report summarizes our accomplishments during the most recent period of support under this grant. Our research covers several interrelated areas: (a) statistical modeling methods for complex phenomena using multiresolution, hierarchical, and relational structures; (b)sensor fusion for complex space-time phenomena and activities; (c) development of statistical models for shapes and their use in robust methods for shape estimation and recognition; and (d) methods for blending physics and statistical learning in image reconstruction, feature extraction, and 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.

Limitations:

APPROVED FOR PUBLIC RELEASE