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）
    • …
• 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