The document discusses semantic image segmentation techniques using deep convolutional neural networks. It introduces U-Net architecture and techniques used in DeepLab models including atrous convolution, atrous spatial pyramid pooling, and decoder methods. It compares DeepLab v1 to v3+ in terms of network architecture, convolution operations, pooling methods, and decoder approaches. Later versions of DeepLab show improved performance with increased computational cost.

1. 2018. 10. 29.
의료영상 분석 및 처리
- Semantic Segmentation -
이 동 헌

4. Slide credit: CS231n

5. v U-Net Architecture
https://lmb.informatik.uni-freiburg.de/people/ronneber/u-net/
http://brainiac2.mit.edu/isbi_challenge/

6. 512x512x1
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Tutorial 4 def UNet_like( ):

7. “Semantic Image Segmentation with Deep Convolutional Nets and Fully Connected CRFs.”
UCLA, 2016. 06. (ICLR 2015)
“DeepLab: Semantic Image Segmentation with Deep Convolutional Nets, Atrous Convolution, and Fully
Connected CRFs.” Google, 2017. 05. (TPAMI)
“Rethinking Atrous Convolution for Semantic Image Segmentation.”
Google, 2017. 12. (arXiv)
“Encoder-Decoder with Atrous Separable Convolution for Semantic Image Segmentation.”
Google, 2018. 08. (ECCV 2018)
Atrous Convolution
Atrous Spatial Pyramid Pooling (ASPP)
Atrous Separable Convolution + Atrous Separable Spatial Pyramid Pooling (ASSPP)
https://blog.lunit.io/2018/07/02/deeplab-v3-encoder-decoder-with-atrous-separable-convolution-for-semantic-image-segmentation/
Decoder: Bilinear upsampling

8. “Semantic Image Segmentation with Deep Convolutional Nets and Fully Connected CRFs.”
UCLA, 2016. 06. (ICLR 2015)
• Atrous Convolution
• Fully Connected CRFs
• VGG16

9. “DeepLab: Semantic Image Segmentation with Deep Convolutional Nets, Atrous Convolution, and Fully
Connected CRFs.” Google, 2017. 05. (TPAMI)
• Atrous Convolution
• Atrous Spatial Pyramid Pooling (ASPP)
• Fully Connected CRFs
• ResNet101

10. “Rethinking Atrous Convolution for Semantic Image Segmentation.”
Google, 2017. 12. (arXiv)
https://blog.lunit.io/2018/07/02/deeplab-v3-encoder-decoder-with-atrous-separable-convolution-for-semantic-image-segmentation/
• Encoder : ResNet101 + Atrous Convolution
• Decoder : Bilinear Upsampling

11. “Encoder-Decoder with Atrous Separable Convolution for Semantic Image Segmentation.”
Google, 2018. 08. (ECCV 2018)
https://blog.lunit.io/2018/07/02/deeplab-v3-encoder-decoder-with-atrous-separable-convolution-for-semantic-image-segmentation/
• Encoder : Xception
+ Atrous Separable Convolution
+ Atrous Separable Spatial Pyramid Pooling (ASSPP)
• Decoder : U-Net Style Upsampling

12. tps://blog.lunit.io/2018/07/02/deeplab-v3-encoder-decoder-with-atrous-separable-convolution-for-semantic-image-segmentation/
Convolution Depthwise Convolution Depthwise Separable Convolution

13. DeepLab v1 DeepLab v2 DeepLab v3 DeepLab v3+
Network
Architecture
VGG16 ResNet101 ResNet101 Xception
Convolution
Atrous
Convolution
Atrous
Convolution
Atrous
Convolution
Atrous Separable
Convolution
Pooling
Atrous Spatial
Pyramid Pooling
Atrous Spatial
Pyramid Pooling
Atrous Spatial
Pyramid Pooling
Atrous Separable
Spatial Pyramid
Pooling
Decoder
Fully Connected
CRFs
Fully Connected
CRFs
Bilinear
Upsampling
U-Net Style
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• 성능
• 연산량