Moving Digital Images

For over six years the Marquette University Archives managed patron-driven scanning requests using a desktop version of Extensis Portfolio while building thematically-based digital collections online using CONTENTdm. The purchase of a CONTENTdm license with an unlimited item limit allowed the department to move over 10,000 images previously cataloged in Portfolio into the online environment. While metadata in the Portfolio database could be exported to a text file and immediately imported into CONTENTdm’s project client, we recognized that we had an opportunity to analyze and clean our metadata using OpenRefine as a part of the process. We also hoped to update our Portfolio database and the metadata embedded into the files themselves to reflect the results of this cleanup. This article will discuss the process we used to clean metadata in OpenRefine for ingest into CONTENTdm as well as the use of Portfolio and the VRA Panel Export-Import Tool for writing metadata changes back to the original image files

Contextualising Mobile Presence with Digital Images

A series of Swarm mobile phone prototypes have been developed in response to the user needs identified in a three-year empirical study of young people’s use of mobile phones. The prototypes take cues from user led innovation and provide multiple avatars that allow individuals to define and manage their own virtual identity. This paper briefly maps the evolution of the prototypes and then describes how the pre-defined, color coded avatars in the latest version of the Swarm are being given greater context and personalization through the use of digital images

Optimum Watermark Detection and Embedding in Digital Images

This work concentrates on the problem of watermarking of still images using the luminance component, through the use of spread spectrum techniques, both in space (direct sequence spread spectrum or DSSS) and frequency (frequency hopping or FH), following the guidelines of Delaigle et al. (1998). The system described is able to embed watermarks and recover them with zero probability of error. The problem is faced from a statistical detection point of view through the analysis of the density function of the image to be marked. A Cauchy model is found to be very accurate and some tests are performed in order to assess improved detection quality. The resulting system turns out to be easy to encrypt and very robust to filtering and JPEG compression.Peer ReviewedPostprint (published version