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dc.contributor.authorAl-Kabbany, Ahmaden_US
dc.contributor.authorDubois, Ericen_US
dc.contributor.editorMatthias Hullin and Marc Stamminger and Tino Weinkaufen_US
dc.description.abstractWe are concerned with the natural image matting problem, where the goal is to estimate the partial opacity of a foreground object so that it can be softly segmented from a background. In sampling-based matting techniques, user interactions are first acquired to provide prior information about foreground and background regions. Samples are then chosen from those interactions to calculate the alpha (opacity) value of every pixel in an image. In this research, we propose a new sampling approach that brings relevant samples to every pixel with an unknown alpha value; this yields accurate alpha maps. We also present two new formulations for objective functions used to assess the suitability of the chosen samples. The evaluation of the proposed method, on the alpha matting online benchmark, shows that its performance is close to the state-of-the-art techniques.en_US
dc.publisherThe Eurographics Associationen_US
dc.subjectI.4.6 [Image Processing and Computer Vision]
dc.subjectPixel classification
dc.titleMatting with Sequential Pair Selection Using Graph Transductionen_US
dc.description.seriesinformationVision, Modeling & Visualization
dc.description.sectionheadersImage Editing and Exploration

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  • VMV16
    ISBN 978-3-03868-025-3

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