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A study was conducted to characterize discontinuous motion estimation as a multicomponent harmonic retrieval concern. Various frequency estimation methods were correlated with an explicit model for discontinuous motion. Results indicated that the framework supports the processes of discontinuous motion in a non-iterative manner. Findings also showed that it can calculate for velocities regardless of motion discontinuities and can be utilized in multimedia and video communication applications.

Thermophysical algebraic invariance formulation is a robust method for interpretation of invariant features of radiometrically uncalibrated infrared (IR) imagery for ATR and site change detection. The approach focuses on internal object composition and thermophysical properties of imaged objects. Invariance to ambient scene conditions, ability to classify objects and focus on phenomena affecting long-wave IR imagery are the special features of the method. Symmetric alphastable models can adequately model all these features. Results of real IR imagery and ground truth data are presented.

Conventional image motion estimation schemes use spatiotemporal algorithms which assume that images move at a constant velocity. Such estimation schemes prove to be inappropriate when images move at a varying velocity. However, a novel image motion estimation approach has been developed with uses a polynomial or a trigonometric polynomial algorithm. Tests on the approach reveal that it produces more accurate estimates.

We address the problem of detection of targets obscured by a forest canopy using an ultrawideband (UWB) radar. The forest clutter observed in the radar imagery is a highly impulsive random process that is more accurately modeled with the recently proposed class of alpha-stable processes as compared with Gaussian, Weibull, and K-distribution models. With this more accurate model, segmentation is performed on the imagery into forest and clear regions. Further, a region-adaptive symmetric alpha stable (S[Alpha]S) constant false-alarm rate (CFAR) detector is introduced and its performance is compared with the Weibull and Gaussian CFAR detectors. The results on real data show that the S[Alpha]S CFAR performs better than the Weibull and Gaussian CFAR detectors in detecting obscured targets.