Trimap-free natural image matting problem is an important computer vision task in which we extract foreground objects from given images without extra trimap input.
Compared with trimap-based matting algorithms, trimap-free algorithms are easier to make false detection when the foreground object is not well defined. To solve the problem, we design a novel structure (SegMatting) to handle foreground segmentation and alpha matte prediction simultaneously, which is able to produce high-quality mattes based on RGB inputs alone. This entangled structure enables information exchange between the binary segmentation task and the alpha matte prediction task interactively, and we further design a hybrid loss to adaptively balance two tasks during the multitask learning process.
Additionally, we adopt a salient object detection dataset to pretrain our network so that we could obtain a more accurate foreground segment before our training process.
Experiments indicate that the proposed SegMatting qualitatively and quantitatively outperforms most previous trimap-free models with a significant margin, while remains competitive among trimap-based methods. / Thesis / Master of Science in Electrical and Computer Engineering (MSECE)
| Identifer | oai:union.ndltd.org:mcmaster.ca/oai:macsphere.mcmaster.ca:11375/27011 |
| Date | January 2021 |
| Creators | LI, CHENGQI |
| Contributors | CHEN, JUN |
| Source Sets | McMaster University |
| Language | English |
| Detected Language | English |
| Type | Thesis |
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