DSpace Manual: Software version 1.5

DSpace is an open source software platform that enables organizations to: - Capture and describe digital material using a submission workflow module, or a variety of programmatic ingest options - Distribute an organization's digital assets over the web through a search and retrieval system - Preserve digital assets over the long term This system documentation includes a functional overview of the system, which is a good introduction to the capabilities of the system, and should be readable by nontechnical personnel. Everyone should read this section first because it introduces some terminology used throughout the rest of the documentation. For people actually running a DSpace service, there is an installation guide, and sections on configuration and the directory structure. Note that as of DSpace 1.2, the administration user interface guide is now on-line help available from within the DSpace system. Finally, for those interested in the details of how DSpace works, and those potentially interested in modifying the code for their own purposes, there is a detailed architecture and design section.

A Neural Network Architecture for Figure-ground Separation of Connected Scenic Figures

A neural network model, called an FBF network, is proposed for automatic parallel separation of multiple image figures from each other and their backgrounds in noisy grayscale or multi-colored images. The figures can then be processed in parallel by an array of self-organizing Adaptive Resonance Theory (ART) neural networks for automatic target recognition. An FBF network can automatically separate the disconnected but interleaved spirals that Minsky and Papert introduced in their book Perceptrons. The network's design also clarifies why humans cannot rapidly separate interleaved spirals, yet can rapidly detect conjunctions of disparity and color, or of disparity and motion, that distinguish target figures from surrounding distractors. Figure-ground separation is accomplished by iterating operations of a Feature Contour System (FCS) and a Boundary Contour System (BCS) in the order FCS-BCS-FCS, hence the term FBF, that have been derived from an analysis of biological vision. The FCS operations include the use of nonlinear shunting networks to compensate for variable illumination and nonlinear diffusion networks to control filling-in. A key new feature of an FBF network is the use of filling-in for figure-ground separation. The BCS operations include oriented filters joined to competitive and cooperative interactions designed to detect, regularize, and complete boundaries in up to 50 percent noise, while suppressing the noise. A modified CORT-X filter is described which uses both on-cells and off-cells to generate a boundary segmentation from a noisy image.