Abstract
The proliferation of digital cameras in commodity consumer devices, and the social trend for casually capturing and sharing media, has led to an explosive growth in personal visual media collections. However this wealth of digital material is infrequently accessed beyond the point of initial capture or sharing, and often lies dormant gathering digital dust in the media repository. This thesis proposes novel Computer Vision and Computer Graphics techniques to release the value in personal media collections - investigating new ways to stylise and present images and video in such collections. First, personal visual media tends to be shot casually in varied and challenging capture conditions by amateur operators. This necessitates some interactive manipulation prior to presentation. This thesis contributes a novel solution for editing such amateur home video into succinct clips of this nature, using a parse-tree representation of the video editing process. We also enable interactive manipulation of still images through a novel object segmentation algorithm dubbed TouchCut which enables object selection with a single touch and is intended for direct media manipulation on commodity media capture devices such as touch-screen digital cameras. Second, there is an interaction barrier to digital media. The casual nature of personal media encourages the capture of significantly larger collections than traditional media. This thesis explores the application of artistic stylisation to create digital ambient displays (DADs) of personal media in the style of cartoons, paintings and paper-cut out. Underpinning this contribution are two new algorithms for video segmentation that enforce temporal coherences within the stylised video. Furthermore, we explore how structuring the media collection hierarchically within the DAD can promote interest and engagement with the collection. Third, personal media collections often contains images of friends or family members. The artistic stylisation of such content using existing approaches rarely results in acceptable output. We propose a novel example-based approach to portrait stylisation driven by a high level model of facial structure that gives rise to improved aesthetics and enables the example-based rendering of a diverse range of portrait styles. This work was supported by the Hewlett Packard Laboratories Innovation Research Program.