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National Institutes of Health, Ben's Run/Ben's Gift, Albert and Elizabeth Tucker Foundation, Mex Frates Leukemia Fund, Jones Family fund, and Oklahoma Center for Adult Stem Cell Research.

We compute AM-FM models for infrared video frames depicting military targets immersed in structured clutter backgrounds. We show that independent correlation based detection processes can be implemented in the pixel and modulation domains and used to construct useful online track consistency checks that indicate when the detection process has been degraded due to nonstationary evolution of the target signature. Throughout the paper, we use the well-known AMCOM closure sequences as exemplars.

For the first time, we perform normalized correlation template tracking in the modulation domain. For each frame of the video sequence, we compute a multi-component AM-FM image model that characterizes the local texture structure of objects and backgrounds. Tracking is carried out by formulating a modulation domain correlation function in the derived feature space. Using visible and longwave infrared sequences as illustrative examples, we study the performance of this new approach relative to two basic pixel domain correlation template trackers. We also present preliminary results from a new dual domain tracker that operates simultaneously in both the pixel and modulation domains.

We introduce a multicomponent invertible AM-FM image transform and use it to define new nonlinear AM-FM filters for performing modulation domain image processing. The key elements of the transform are analysis and synthesis filterbanks based on the steerable image pyramid and perfect reconstruction demodulation algorithms based on analytic differentiation of continuous cubic tensor spline models fit to the unwrapped phase samples of a digital image. We demonstrate spatially and spectrally localized orientation and frequency selective filtering, simple image restoration, and image fusion in the modulation domain. These results are also among the first to demonstrate high fidelity image reconstructions from computed multicomponent AM-FM models.

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