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Content Provider | IET Digital Library |
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Author | Zhang, Zhan Wang, Yuehai Yang, Jianyi |
Abstract | Portrait matting is of vital importance for many applications such as portrait editing, background replacement, ecommerce demonstration, and augmented reality. The portrait matt can be accessed by predicting the α value of the original picture. Previous deep matting methods usually adopt a segmentation network to tackle portrait matting tasks. However, these traditional methods will introduce unpleasant blemishes in the matting results sometimes. The authors find that the key factor behind this phenomenon is how they model the matting problem. On the one hand, α value predicting can be modelled as a regression task. On the other hand, it can be viewed as a classification task of predicting background or foreground. To solve this problem, they explore different methods to model the nature of the α matting problem and propose a novel quantisation-based adaption. Their method comes up with an α quantisation loss to achieve multi-threshold filtering. Furthermore, they apply an α merging block to improve conventional regression methods. With their method, the gradient loss is reduced by 7.53% relatively, with mean square error and sum of absolute difference decreased by 14.7% relatively, leading to a more visually pleasant α matt in several segmentation backbones. |
Starting Page | 339 |
Ending Page | 349 |
Page Count | 11 |
ISSN | 17519632 |
Volume Number | 14 |
e-ISSN | 17519640 |
Issue Number | Issue 6, Sep (2020) |
Alternate Webpage(s) | https://digital-library.theiet.org/content/journals/iet-cvi/14/6 |
Alternate Webpage(s) | https://digital-library.theiet.org/content/journals/10.1049/iet-cvi.2019.0779 |
Journal | IET Computer Vision |
Publisher Date | 2020-07-14 |
Access Restriction | Open |
Rights Holder | © The Institution of Engineering and Technology |
Subject Keyword | Augmented Reality Background Replacement Classification Task Computer Vision And Image Processing Technique Deep Matting Method Deep Quantised Portrait Matting E-commerce Demonstration Filtering Method in Signal Processing Image Classification Image Colour Analysis Image Filtering Image Recognition Interpolation And Function Approximation Matting Problem Mean Square Error Method Multithreshold Filtering Numerical Analysis Portrait Editing Portrait Matting Task Quantisation Loss Quantisation-based Adaption Regression Analysis Regression Method Regression Task Virtual Reality |
Content Type | Text |
Resource Type | Article |
Subject | Computer Vision and Pattern Recognition Software |
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