True and Proper Pictures

This chapter assesses the representation of exotica in European art. This depiction sheds considerable light on the constructs of veracity and the bounds of imagery, from the Middle Ages to the nineteenth century. In addition to their subsidiary but vital roles in historical landscape and commemorative narrative, exotica served as the principals in natural history illustration. Among the first works with detailed, if schematized, illuminations of flora and fauna were religious texts and medieval editions of ancient medical treatises. These largely didactic presentations of European plants and animals provided the pictorial structure for the earliest renderings of exotica. Whether artists drew them from life in the course of their travels, viewed them in menageries and botanical gardens, or based their illustrations on collections of dried or stuffed specimens, they placed their subjects against uniformly plain backgrounds. Land mammals, aquatic creatures, and plants were suspended in a pristine world, while birds were shown perched upon accessory branches.

Tracking of Deformable Objects Using Dynamically and Robustly Updating Pictorial Structures

The problem posed by complex, articulated or deformable objects has been at the focus of much tracking research for a considerable length of time. However, it remains a major challenge, fraught with numerous difficulties. The increased ubiquity of technology in all realms of our society has made the need for effective solutions all the more urgent. In this article, we describe a novel method which systematically addresses the aforementioned difficulties and in practice outperforms the state of the art. Global spatial flexibility and robustness to deformations are achieved by adopting a pictorial structure based geometric model, and localized appearance changes by a subspace based model of part appearance underlain by a gradient based representation. In addition to one-off learning of both the geometric constraints and part appearances, we introduce a continuing learning framework which implements information discounting i.e., the discarding of historical appearances in favour of the more recent ones. Moreover, as a means of ensuring robustness to transient occlusions (including self-occlusions), we propose a solution for detecting unlikely appearance changes which allows for unreliable data to be rejected. A comprehensive evaluation of the proposed method, the analysis and discussing of findings, and a comparison with several state-of-the-art methods demonstrates the major superiority of our algorithm.