Video image registration evaluation for a layered sensing environment

Abstract

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 [1] 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)1 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.