Deep Automatic Portrait Matting
Refereed conference paper presented and published in conference proceedings

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AbstractWe propose an automatic image matting method for portrait images. This method does not need user interaction, which was however essential in most previous approaches. In order to accomplish this goal, a new end-to-end convolutional neural network (CNN) based framework is proposed taking the input of a portrait image. It outputs the matte result. Our method considers not only image semantic prediction but also pixel-level image matte optimization. A new portrait image dataset is constructed with our labeled matting ground truth. Our automatic method achieves comparable results with state-of-the-art methods that require specified foreground and background regions or pixels. Many applications are enabled given the automatic nature of our system.
All Author(s) ListXiaoyong Shen, Xin Tao, Hongyun Gao, Chao Zhou, Jiaya Jia
Name of ConferenceEuropean Conference on Computer Vision
Start Date of Conference08/10/2016
End Date of Conference16/10/2016
Place of ConferenceAmsterdam
Country/Region of ConferenceNetherlands
Proceedings TitleECCV 2016: Computer Vision – ECCV 2016
Volume Number9905
Pages92 - 107
LanguagesEnglish-United States

Last updated on 2022-13-01 at 00:50