<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>
| Identifer | oai:union.ndltd.org:UPSALLA/oai:DiVA.org:liu-2355 |
| Date | January 2004 |
| Creators | Karlsson, Fredrik |
| Publisher | Linköping University, Department of Science and Technology, Institutionen för teknik och naturvetenskap |
| Source Sets | DiVA Archive at Upsalla University |
| Language | Swedish |
| Detected Language | English |
| Type | Student thesis, text |
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