Improving Image Segmentation with Boundary Patch Refinement View Full Text


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Article Info

DATE

2022-08-12

AUTHORS

Xiaolin Hu, Chufeng Tang, Hang Chen, Xiao Li, Jianmin Li, Zhaoxiang Zhang

ABSTRACT

Tremendous efforts have been made on image segmentation but the mask quality is still not satisfactory. The boundaries of predicted masks are usually imprecise due to the low spatial resolution of feature maps and the imbalance problem caused by the extremely low proportion of boundary pixels. To address these issues, we propose a conceptually simple yet effective post-processing refinement framework, termed BPR, to improve the boundary quality of the prediction of any image segmentation model. Following the idea of looking closer to segment boundaries better, we extract and refine a series of small boundary patches along the predicted boundaries. The refinement is accomplished by a boundary patch refinement network at the higher resolution. The trained BPR model can be easily transferred to refine the results of other models as well. Extensive experiments show that the proposed BPR framework yields significant improvements on the semantic, instance, and panoptic segmentation tasks over a variety of baselines on the Cityscapes dataset. More... »

PAGES

2571-2589

References to SciGraph publications

  • 2018-10-06. Encoder-Decoder with Atrous Separable Convolution for Semantic Image Segmentation in COMPUTER VISION – ECCV 2018
  • 2020-11-03. Conditional Convolutions for Instance Segmentation in COMPUTER VISION – ECCV 2020
  • 2015-11-18. U-Net: Convolutional Networks for Biomedical Image Segmentation in MEDICAL IMAGE COMPUTING AND COMPUTER-ASSISTED INTERVENTION – MICCAI 2015
  • 2020-11-19. Improving Semantic Segmentation via Decoupled Body and Edge Supervision in COMPUTER VISION – ECCV 2020
  • 2020-11-07. Object-Contextual Representations for Semantic Segmentation in COMPUTER VISION – ECCV 2020
  • 2021-02-26. EfficientPS: Efficient Panoptic Segmentation in INTERNATIONAL JOURNAL OF COMPUTER VISION
  • 2020-10-29. Axial-DeepLab: Stand-Alone Axial-Attention for Panoptic Segmentation in COMPUTER VISION – ECCV 2020
  • 2020-10-07. SegFix: Model-Agnostic Boundary Refinement for Segmentation in COMPUTER VISION – ECCV 2020
  • 2020-11-13. Boundary-Preserving Mask R-CNN in COMPUTER VISION – ECCV 2020
  • 2020-12-04. SOLO: Segmenting Objects by Locations in COMPUTER VISION – ECCV 2020
  • 2014. Microsoft COCO: Common Objects in Context in COMPUTER VISION – ECCV 2014
  • Identifiers

    URI

    http://scigraph.springernature.com/pub.10.1007/s11263-022-01662-0

    DOI

    http://dx.doi.org/10.1007/s11263-022-01662-0

    DIMENSIONS

    https://app.dimensions.ai/details/publication/pub.1150189029


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