This thesis presents the design and development of a hand-held electronic reader, designed to decode conductive patterns printed on a paper substrate. Data read from the patterns, by the reader, is used to trigger events in the digital domain. The reader and associated conductive patterns are devices for linking paper documents with the digital world. The patterns are formed by masking conductive-coated paper with a non-conductive, printed lacquer. The reader is a low cost and ergonomic device, capable of transmitting the embedded data from the conductive paper to the computer. The first reader designed and developed was tethered to a computer by data cable, using the USB communication protocol. The second design was developed further, with transmission of data achieved by replacing the cable with short-range Bluetooth wireless technology. Both devices were designed and developed using embedded systems and low cost electronic components. Additional work was undertaken to optimise the device's mechanical structure, ergonomics and integration of hardware. Alongside the development of the reader, test and development work was carried out to optimise the printed media, in materials and design. User trials demonstrated that the complete printed and reading system was functional, with varied rates of success among participants. Further work is required to improve the conductivity of the coated paper, and the accuracy of the decoding algorithm.

<p>The problem of separating a non-rectangular foreground image from a background image is a classical problem in image processing and analysis, known as matting or keying. A common example is a film frame where an actor is extracted from the background to later be placed on a different background. Compositing of these objects against a new background is one of the most common operations in the creation of visual effects. When the original background is of non-constant color the matting becomes an under determined problem, for which a unique solution cannot be found. </p><p>This thesis describes a framework for computing mattes from images with backgrounds of non-constant color, using Bayesian statistics. Foreground and background color distributions are modeled as oriented Gaussians and optimal color and opacity values are determined using a maximum a posteriori approach. Together with information from optical flow algorithms, the framework produces mattes for image sequences without needing user input for each frame. </p><p>The approach used in this thesis differs from previous research in a few areas. The optimal order of processing is determined in a different way and sampling of color values is changed to work more efficiently on high-resolution images. Finally a gradient-guided local smoothness constraint can optionally be used to improve results for cases where the normal technique produces poor results.</p>

The problem of separating a non-rectangular foreground image from a background image is a classical problem in image processing and analysis, known as matting or keying. A common example is a film frame where an actor is extracted from the background to later be placed on a different background. Compositing of these objects against a new background is one of the most common operations in the creation of visual effects. When the original background is of non-constant color the matting becomes an under determined problem, for which a unique solution cannot be found. This thesis describes a framework for computing mattes from images with backgrounds of non-constant color, using Bayesian statistics. Foreground and background color distributions are modeled as oriented Gaussians and optimal color and opacity values are determined using a maximum a posteriori approach. Together with information from optical flow algorithms, the framework produces mattes for image sequences without needing user input for each frame. The approach used in this thesis differs from previous research in a few areas. The optimal order of processing is determined in a different way and sampling of color values is changed to work more efficiently on high-resolution images. Finally a gradient-guided local smoothness constraint can optionally be used to improve results for cases where the normal technique produces poor results.