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dc.contributor.authorSubr, Karticen_US
dc.contributor.authorParis, Sylvainen_US
dc.contributor.authorSoler, Cyrilen_US
dc.contributor.authorKautz, Janen_US
dc.contributor.editorI. Navazo, P. Poulinen_US
dc.date.accessioned2015-02-28T15:21:12Z
dc.date.available2015-02-28T15:21:12Z
dc.date.issued2013en_US
dc.identifier.issn1467-8659en_US
dc.identifier.urihttp://dx.doi.org/10.1111/cgf.12024en_US
dc.description.abstractSelections are central to image editing, e.g., they are the starting point of common operations such as copy-pasting and local edits. Creating them by hand is particularly tedious and scribble-based techniques have been introduced to assist the process. By interpolating a few strokes specified by users, these methods generate precise selections. However, most of the algorithms assume a 100 percent accurate input, and even small inaccuracies in the scribbles often degrade the selection quality, which imposes an additional burden on users. In this paper, we propose a selection technique tolerant to input inaccuracies. We use a dense conditional random field (CRF) to robustly infer a selection from possibly inaccurate input. Further, we show that patch-based pixel similarity functions yield more precise selection than simple point-wise metrics. However, efficiently solving a dense CRF is only possible in low-dimensional Euclidean spaces, and the metrics that we use are high-dimensional and often non-Euclidean.We address this challenge by embedding pixels in a low-dimensional Euclidean space with a metric that approximates the desired similarity function. The results show that our approach performs better than previous techniques and that two options are sufficient to cover a variety of images depending on whether the objects are textured.en_US
dc.publisherThe Eurographics Association and Blackwell Publishing Ltd.en_US
dc.titleAccurate Binary Image Selection from Inaccurate User Inputen_US
dc.description.seriesinformationComputer Graphics Forumen_US


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