Improving identification of subtle changes in wide-area sensing through dynamic zoom
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Abstract
The past decade has seen an abundance of applications that utilize sensors to collect data. One such example is a gigapixel image, which combines a multitude of high-quality images into a panorama capable of viewing hundreds of acres. The resulting datasets can be quite large, making analysis time consuming and resource intensive. Moreover, coverage of such broad environments can mean numerous sensor feeds to which one must attend. A suitable approach for analysis and sense-making of such data is to focus on "interesting" samples of data, namely regions of interest, or ROI. ROIs are especially useful in wide-area sensing situations that return datasets that are largely similar from one instance to the next, but also possess small differences. Identifying subtle changes is relevant to certain scenarios in surveillance, such as the evidence of human activity. Several ROI detection techniques exist in the research literature. My work focuses on ROI detection tuned to subtle differences for images at varying zoom levels. My thesis consists of developing a method that identifies regions of interest for subtle changes in images. In this pursuit, my contributions will address key questions including the characterization of image information dynamics through introduction of dynamic zoom, the definition and measurement of subtlety, and an approach for scoring and selecting ROIs. This work will provide an automated attention mechanism for zoomed images, but is also applicable to domains include satellite imagery and cyber security.