图像分割就是把图像分成若干个特定的、具有独特性质的区域并提出感兴趣目标的技术和过程。它是由图像处理到图像分析的关键步骤。 所谓图像分割指的是根据灰度、颜色、纹理和形状等特征把图像划分成若干互不交迭的区域,并使这些特征在同一区域内呈现出相似性,而在不同区域间呈现出明显的差异性。

知识荟萃

图像分割 (Image Segmentation) 专知荟萃

入门学习

  1. A 2017 Guide to Semantic Segmentation with Deep Learning 概述——用深度学习做语义分割
  2. 从全卷积网络到大型卷积核:深度学习的语义分割全指南
  3. Fully Convolutional Networks
  4. 语义分割中的深度学习方法全解:从FCN、SegNet到各代DeepLab
  5. 图像语义分割之FCN和CRF
  6. 从特斯拉到计算机视觉之「图像语义分割」
  7. 计算机视觉之语义分割
  8. Segmentation Results: VOC2012 PASCAL语义分割比赛排名

综述

  1. A Review on Deep Learning Techniques Applied to Semantic Segmentation Alberto Garcia-Garcia, Sergio Orts-Escolano, Sergiu Oprea, Victor Villena-Martinez, Jose Garcia-Rodriguez 2017
  2. Computer Vision for Autonomous Vehicles: Problems, Datasets and State-of-the-Art
  3. 基于内容的图像分割方法综述 姜 枫 顾 庆 郝慧珍 李 娜 郭延文 陈道蓄 2017

进阶论文

  1. U-Net [https://arxiv.org/pdf/1505.04597.pdf]
  2. SegNet [https://arxiv.org/pdf/1511.00561.pdf]
  3. DeepLab [https://arxiv.org/pdf/1606.00915.pdf]
  4. FCN [https://arxiv.org/pdf/1605.06211.pdf]
  5. ENet [https://arxiv.org/pdf/1606.02147.pdf]
  6. LinkNet [https://arxiv.org/pdf/1707.03718.pdf]
  7. DenseNet [https://arxiv.org/pdf/1608.06993.pdf]
  8. Tiramisu [https://arxiv.org/pdf/1611.09326.pdf]
  9. DilatedNet [https://arxiv.org/pdf/1511.07122.pdf]
  10. PixelNet [https://arxiv.org/pdf/1609.06694.pdf]
  11. ICNet [https://arxiv.org/pdf/1704.08545.pdf]
  12. ERFNet [http://www.robesafe.uah.es/personal/eduardo.romera/pdfs/Romera17iv.pdf]
  13. RefineNet [https://arxiv.org/pdf/1611.06612.pdf]
  14. PSPNet [https://arxiv.org/pdf/1612.01105.pdf]
  15. CRFasRNN [http://www.robots.ox.ac.uk/%7Eszheng/papers/CRFasRNN.pdf]
  16. Dilated convolution [https://arxiv.org/pdf/1511.07122.pdf]
  17. DeconvNet [https://arxiv.org/pdf/1505.04366.pdf]
  18. FRRN [https://arxiv.org/pdf/1611.08323.pdf]
  19. GCN [https://arxiv.org/pdf/1703.02719.pdf]
  20. DUC, HDC [https://arxiv.org/pdf/1702.08502.pdf]
  21. Segaware [https://arxiv.org/pdf/1708.04607.pdf]
  22. Semantic Segmentation using Adversarial Networks [https://arxiv.org/pdf/1611.08408.pdf]

综述

  1. A Review on Deep Learning Techniques Applied to Semantic Segmentation Alberto Garcia-Garcia, Sergio Orts-Escolano, Sergiu Oprea, Victor Villena-Martinez, Jose Garcia-Rodriguez 2017
  2. Computer Vision for Autonomous Vehicles: Problems, Datasets and State-of-the-Art
  3. 基于内容的图像分割方法综述 姜 枫 顾 庆 郝慧珍 李 娜 郭延文 陈道蓄 2017

Tutorial

  1. Semantic Image Segmentation with Deep Learning
  2. A 2017 Guide to Semantic Segmentation with Deep Learning
  3. Image Segmentation with Tensorflow using CNNs and Conditional Random Fields

视频教程

  1. CS231n: Convolutional Neural Networks for Visual Recognition Lecture 11 Detection and Segmentation 
  2. Machine Learning for Semantic Segmentation - Basics of Modern Image Analysis

代码

Semantic segmentation

  1. U-Net (https://arxiv.org/pdf/1505.04597.pdf)
  2. SegNet (https://arxiv.org/pdf/1511.00561.pdf)
  3. DeepLab (https://arxiv.org/pdf/1606.00915.pdf)
  4. FCN (https://arxiv.org/pdf/1605.06211.pdf)
  5. ENet (https://arxiv.org/pdf/1606.02147.pdf)
  6. LinkNet (https://arxiv.org/pdf/1707.03718.pdf)
  7. DenseNet (https://arxiv.org/pdf/1608.06993.pdf)
  8. Tiramisu (https://arxiv.org/pdf/1611.09326.pdf)
  9. DilatedNet (https://arxiv.org/pdf/1511.07122.pdf)
  10. PixelNet (https://arxiv.org/pdf/1609.06694.pdf)
  11. ICNet (https://arxiv.org/pdf/1704.08545.pdf)
  12. ERFNet (http://www.robesafe.uah.es/personal/eduardo.romera/pdfs/Romera17iv.pdf)
  13. RefineNet (https://arxiv.org/pdf/1611.06612.pdf)
  14. PSPNet (https://arxiv.org/pdf/1612.01105.pdf)
  15. CRFasRNN (http://www.robots.ox.ac.uk/%7Eszheng/papers/CRFasRNN.pdf)
  16. Dilated convolution (https://arxiv.org/pdf/1511.07122.pdf)
  17. DeconvNet (https://arxiv.org/pdf/1505.04366.pdf)
  18. FRRN (https://arxiv.org/pdf/1611.08323.pdf)
  19. GCN (https://arxiv.org/pdf/1703.02719.pdf)
  20. DUC, HDC (https://arxiv.org/pdf/1702.08502.pdf)
  21. Segaware (https://arxiv.org/pdf/1708.04607.pdf)
  22. Semantic Segmentation using Adversarial Networks (https://arxiv.org/pdf/1611.08408.pdf)

Instance aware segmentation

  1. FCIS [https://arxiv.org/pdf/1611.07709.pdf]
  2. MNC [https://arxiv.org/pdf/1512.04412.pdf]
  3. DeepMask [https://arxiv.org/pdf/1506.06204.pdf]
  4. SharpMask [https://arxiv.org/pdf/1603.08695.pdf]
  5. Mask-RCNN [https://arxiv.org/pdf/1703.06870.pdf]
  6. https://github.com/jasjeetIM/Mask-RCNN [Caffe]
  7. RIS [https://arxiv.org/pdf/1511.08250.pdf]
  8. FastMask [https://arxiv.org/pdf/1612.08843.pdf]

Satellite images segmentation

Video segmentation

Autonomous driving

Annotation Tools:

Datasets

  1. Stanford Background Dataset[http://dags.stanford.edu/projects/scenedataset.html]
  2. Sift Flow Dataset[http://people.csail.mit.edu/celiu/SIFTflow/]
  3. Barcelona Dataset[http://www.cs.unc.edu/~jtighe/Papers/ECCV10/]
  4. Microsoft COCO dataset[http://mscoco.org/]
  5. MSRC Dataset[http://research.microsoft.com/en-us/projects/objectclassrecognition/]
  6. LITS Liver Tumor Segmentation Dataset[https://competitions.codalab.org/competitions/15595]
  7. KITTI[http://www.cvlibs.net/datasets/kitti/eval_road.php]
  8. Stanford background dataset[http://dags.stanford.edu/projects/scenedataset.html]
  9. Data from Games dataset[https://download.visinf.tu-darmstadt.de/data/from_games/]
  10. Human parsing dataset[https://github.com/lemondan/HumanParsing-Dataset]
  11. Silenko person database[https://github.com/Maxfashko/CamVid]
  12. Mapillary Vistas Dataset[https://www.mapillary.com/dataset/vistas]
  13. Microsoft AirSim[https://github.com/Microsoft/AirSim]
  14. MIT Scene Parsing Benchmark[http://sceneparsing.csail.mit.edu/]
  15. COCO 2017 Stuff Segmentation Challenge[http://cocodataset.org/#stuff-challenge2017]
  16. ADE20K Dataset[http://groups.csail.mit.edu/vision/datasets/ADE20K/]
  17. INRIA Annotations for Graz-02[http://lear.inrialpes.fr/people/marszalek/data/ig02/]

比赛

  1. MSRC-21 [http://rodrigob.github.io/are_we_there_yet/build/semantic_labeling_datasets_results.html]
  2. Cityscapes [https://www.cityscapes-dataset.com/benchmarks/]
  3. VOC2012 [http://host.robots.ox.ac.uk:8080/leaderboard/displaylb.php?challengeid=11&compid=6]

领域专家

  1. Jonathan Long
  2. Liang-Chieh Chen
  3. Hyeonwoo Noh
  4. Bharath Hariharan
  5. Fisher Yu
  6. Vijay Badrinarayanan
  7. Guosheng Lin

初步版本,水平有限,有错误或者不完善的地方,欢迎大家提建议和补充,会一直保持更新,本文为专知内容组原创内容,未经允许不得转载,如需转载请发送邮件至fangquanyi@gmail.com 或 联系微信专知小助手(Rancho_Fang)

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VIP内容

摘要 随着深度学习算法在图像分割领域的成功应用,在图像实例分割方向上涌现出一大批优秀的算法架构.这些架构在分割效果、运行速度等方面都超越了传统方法.本文围绕图像实例分割技术的最新研究进展,对现阶段经典网络架构和前沿网络架构进行梳理总结,结合常用数据集和权威评价指标对各个架构的分割效果进行比较和分析.最后,对目前图像实例分割技术面临的挑战以及可能的发展趋势进行了展望.

http://www.ejournal.org.cn/CN/abstract/abstract12215.shtml

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This paper addresses the domain shift problem for segmentation. As a solution, we propose OLVA, a novel and lightweight unsupervised domain adaptation method based on a Variational Auto-Encoder (VAE) and Optimal Transport (OT) theory. Thanks to the VAE, our model learns a shared cross-domain latent space that follows a normal distribution, which reduces the domain shift. To guarantee valid segmentations, our shared latent space is designed to model the shape rather than the intensity variations. We further rely on an OT loss to match and align the remaining discrepancy between the two domains in the latent space. We demonstrate OLVA's effectiveness for the segmentation of multiple cardiac structures on the public Multi-Modality Whole Heart Segmentation (MM-WHS) dataset, where the source domain consists of annotated 3D MR images and the unlabelled target domain of 3D CTs. Our results show remarkable improvements with an additional margin of 12.5\% dice score over concurrent generative training approaches.

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