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Object Segmentation using Parametric Representation
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Publication Year
2022-01-01
Journal
Proceedings of 2022 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA ASC 2022
Publisher
Institute of Electrical and Electronics Engineers Inc.
Citation
Proceedings of 2022 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA ASC 2022, pp.770-778
Mesh Keyword
Boundary smoothnessHigh-orderHigher-orderLearning-based segmentationNon-differentiable pointsObjects segmentationParametric representationsPropertyRegion connectivitiesSegmentation methods
All Science Classification Codes (ASJC)
Computer Networks and CommunicationsInformation SystemsSignal Processing
Abstract
Most deep learning-based segmentation methods focus on accurate pixel-wise classification, disregarding higher order properties such as region connectivity, boundary smoothness, or the number of non-differentiable points on the object contour. To address these issues, we propose a new parameter-based object segmentation framework. Specifically, we represent the target object's boundary with parametric curves to handle the higher-order properties and find the parameters through a convolutional neural network. We also introduce a novel silhouette loss to train the network, enabling efficient parameter-based contour fitting. The proposed silhouette loss is based on a differentiable renderer and is suitable for the segmentation task because it has the same property as IoU (intersection over union). Experimental results show that the proposed method yields object masks with desirable properties and achieves comparable performance to the state-of-the-art on various tasks.
Language
eng
URI
https://aurora.ajou.ac.kr/handle/2018.oak/36848
https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85146276054&origin=inward
DOI
https://doi.org/10.23919/apsipaasc55919.2022.9980318
Journal URL
http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=9979726
Type
Conference
Funding
This work was supported in part by Institute of Information & communications Technology Planning & Evaluation (IITP) grant funded by Korea government (MSIT) (No. 2021-0-01062). This work was also supported by the BK21 FOUR program of the Education and Research Program for Future ICT Pioneers, Seoul National University in 2022.
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 KOO, HYUNG IL Image
KOO, HYUNG IL구형일
Department of Electrical and Computer Engineering
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