抠图
算法
Background-Matting：The World is Your Green Screen（2020）
[Background-Matting]
[Paper]
[Project]
分类
- Traditional approaches
- simpling-based techniques
- propagation-based techniques
- Learning-based approaches
- trimap based methods
- Context aware matting（CAM）
- Index Matting（IM）
- …
- automatic matting algorithm
- Late Fusion Matting（LFM）
- …
- trimap based methods
- Matting with known natural background
- Video Matting
历史
- 2019
- Disentangled image matting
- Context-aware image matting for simultaneous foreground and alpha estimation
- Learning to index for deep image matting
- A late fusion cnn for digital matting
- 2018
- Semantic soft segmentation
- Encoder-decoder with atrous separable convlution for semantic image segmentation
- Semantic human matting
- Alpha-gan: Generative adversarial networks for natural image matting
- 2017
- Designing effective inter-pixel information flow for natural image matting
- Deep image matting
- Fast deep matting for portrait animation on mobile phone
- 2016
- Natural image matting using deep convolutional neural networks
- Deep automatic portrait matting
- 2013
- KNN matting
- 2011
- A global sampling method for alpha matting
- Nonlocal matting
- 2010
- Shared sampling for real-time alpha matting
- Fast matting using large kernel matting laplacian matrics
- 2008
- Spectral matting
- 2007
- A closed-form solution to natural image matting
- 2004
- A bayesian approach to digital matting
网络结构
At the core of our approach is a deep matting network G that extracts foreground color and alpha for a given input frame, augmented with background, soft segmentation, and (optionally nearby video frames), and a discriminator network D that guides the training to generate realistic results.
损失函数
Attention-Guided Hierarchical Structure Aggregation for Image Matting（2020）
[CVPR2020-HAttMatting]
[Attention-Guided Hierarchical Structure Aggregation for Image Matting]
A Late Fusion CNN for Digital Matting(2019)
[FusionMatting]
[A Late Fusion CNN for Digital Matting]
[《A Late Fusion CNN for Digital Matting》论文阅读]
[[质疑][CVPR2019][A Late Fusion… Matting]]
[澄清误解-对CVPR 2019 LFM论文质疑的回复]
[阿里巴巴-浙江大学前言技术联合研究中心]
LFM是端到端的神经网络，输入包含前景的图像，输出为前景的alpha遮罩。利用神经网络来预测三个图：前景概率图、背景概率图和混合权重图。根据混合权重图将前景概率图和背景概率图进行融合得到alpha遮罩。需要训练的网络有分割网络预训练、融合网络预训练以及端到端的联合训练，训练损失加在输出alpha遮罩上。
Natural Image Matting via Guided Contextual Attention(2020)
[GCA-Matting]
[Natural Image Matting via Guided Contextual Attention]
网络结构
- GCA
损失函数
Deep image matting（2017）
[pytorch-deep-image-matting]
[Deep Image Matting]
[Project]
[[论文阅读]Deep Image matting(以及实现细节讨论)]
数据集
[人像分割不靠谱汇总【1】]
Matting 是将前景和背景进行软分割的方法，目标是找出前景和背景以及它们之间的融合程度。
注：trimap一般都是由matte扩张生成
汇总
[人像分割不靠谱汇总【1】]
挑战
[Alpha Matting Evaluation Website]
Evalution
- SAD(sum of absolution difference)
- MSE(mean square error)
References
