Enabling Trimap-Free Image Matting via Multitask Learning
| Autor: | LI, CHENGQI |
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| Jazyk: | angličtina |
| Rok vydání: | 2021 |
| Předmět: | |
| Druh dokumentu: | Diplomová práce |
| Popis: | 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) |
| Databáze: | Networked Digital Library of Theses & Dissertations |
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