Video image registration evaluation for a layered sensing environment

In this paper, several methods to register and stabilize a motion imagery video sequence under the layered sensing concept are evaluated. Utilizing the layered sensing paradigm, an area is surveyed by a multitude of sensors at many different altitudes and operating across many modalities. Utilizing a combination of sensors provides better insight into a situation than could ever be achieved with a single sensor. A fundamental requirement in layered sensing is to first register, stabilize, and normalize the data from each of the individual sensors. This paper extends our previous work to include experimental analysis. The paper contribution provides an evaluation of four registration algorithms now including the (1) Lucas-Kanade (LK) algorithm, (2) the Ohio State University (OSU)
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correlation-based method, (3) robust data alignment (RDA), and (4) scale invariant feature transform (SIFT). Results demonstrate that registration accuracy and robustness were achieved with the LK and correlation-based methods over the others for image-to-image registration, restricted adaptive tuning, and stabilization over warped images; while the SIFT outperformed the others for partial image overlap.