<p> Chronic pain affects more than 100 million Americans and more than 1.5 billion people worldwide. Pain is a multidimensional construct, expressed through a variety of means. Facial expressions are one such type of pain expression. Automatic facial expression recognition, and in particular pain expression recognition, are fields that have been studied extensively. However, nothing has explored the possibility of an automatic pain quantification algorithm, able to output pain levels based upon a facial image. </p><p> Developed for a remote monitoring context, a computational pain quantification algorithm has been developed and validated by two distinct sets of data. The second set of data also included associated data for the fields of age, gender, culture and cause of pain. These four fields were investigated for their effect on automatic pain quantification, determining that age and gender have a definite impact and should be involved in the algorithm, while culture and cause require further investigation.</p>

<p> 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 &ldquo;interesting&rdquo; 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. </p><p>