ITACTIC - Infrared Target Acquisition, Classification, and Tracking via Image Compression

ABSTRACT: Rice in concert with Inview Technologies will design, simulate, and evaluate compressive algorithms for object detection and classification in both static and dynamic imaging. The eventual goal is the application of the most suitable methods in novel short-wave and mid-wave infrared optical architectures for high-speed discovery and tracking by exploiting sparse signatures. We will compare our newly developed methods against state-of-the-art approaches such as the principal component and linear discriminate methods currently in use with traditional focal plane array imagers. We will utilize our experience compressive video imaging to design new mathematical projections to identify and isolate foreground targets from cluttered background. We will begin from our Hadamard approximations of both for video reconstruction but will further refine the compressive domain separation by exploiting machine vision algorithms such as our manifold secant approach as a means to improve signal-to-noise performance of the receiver operating characterstics. The second approach will build up from our successful use of Inviews Partial Complete methodology of building up anomaly detection patterns from select Hadamard kernels. While very generic, these exploit localized commonalities among the targets and will be further expanded to an optimal multiscale collection of patterns that accurately reflect the targets individualistic features. ; BENEFIT: We aim to show that compressive domain algorithms and processing techniques can provide actionable decision making capabilities to platforms such as weapons seekers that have limited processing power.