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논문 기본 정보
- 자료유형
- 학술저널
- 저자정보
- 발행연도
- 2021.10
- 수록면
- 209 - 214 (6page)
- DOI
- 10.14372/IEMEK.2021.16.5.209
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초록· 키워드
오류제보하기
Panoptic segmentation, which is now widely used in computer vision such as medical image analysis, and autonomous driving, helps understanding an image with holistic view. It identifies each pixel by assigning a unique class ID, and an instance ID. Specifically, it can classify ‘thing’ from ‘stuff’, and provide pixel-wise results of semantic prediction and object detection. As a result, it can solve both semantic segmentation and instance segmentation tasks through a unified single model, producing two different contexts for two segmentation tasks.
Semantic segmentation task focuses on how to obtain multi-scale features from large receptive field, without losing low-level features. On the other hand, instance segmentation task focuses on how to separate ‘thing’ from ‘stuff’ and how to produce the representation of detected objects. With the advances of both segmentation techniques, several panoptic segmentation models have been proposed. Many researchers try to solve discrepancy problems between results of two segmentation branches that can be caused on the boundary of the object. In this survey paper, we will introduce the concept of panoptic segmentation, categorize the existing method into two representative methods and explain how it is operated on two methods: top-down method and bottom-up method. Then, we will analyze the performance of various methods with experimental results.
목차
등록된 정보가 없습니다.
참고문헌 (12)
참고문헌 신청
J. Long / 2015 / Fully Convolutional Networks for Semantic Segmentation / Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition :3431~3440
K. He / 2017 / Mask r-cnn / Proceedings of the IEEE International Conference on Computer Vision :2961~2969
A. Kirillov / 2019 / Panoptic Segmentation / Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition :9404~9413
A. Kirillov / 2019 / Panoptic Feature Pyramid Networks / Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition :6399~6408
Y. Xiong / 2019 / Upsnet : A Unified Panoptic Segmentation Network / Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition :8818~8826
K. He / 2017 / Mask r-cnn / Proceedings of the IEEE International Conference on Computer Vision :2961~2969
A. Kirillov / 2019 / Panoptic Segmentation / Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition :9404~9413
A. Kirillov / 2019 / Panoptic Feature Pyramid Networks / Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition :6399~6408
Y. Xiong / 2019 / Upsnet : A Unified Panoptic Segmentation Network / Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition :8818~8826
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