13 research outputs found

    Hybrid knowledge-based system/multilayer perceptron approach for the post-assembly tuning of electronic filters

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    Foreground-background segmentation by cellular neural networks

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    A common procedure in digital postproduction is roto-scoping, the segmentation of independently moving fore-ground elements from background in a sequence of im-ages. Still often carried out manually, rotoscoping is time-consuming and requires great skill in determining the boundary between foreground and background. Errors lead to a bubbling artefact in the final composited sequence. The industry is interested in automated rotoscoping. Any automatic segmentation method must correctly locate the boundary and be robust given rapid motion and non-static backgrounds. A cellular neural network for segmentation is presented that labels pixels by colour, estimated motion and neighbouring labels. The method is accurate, labour-saving and many times faster than manual rotoscoping
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