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2020 | OriginalPaper | Buchkapitel

Structured Landmark Detection via Topology-Adapting Deep Graph Learning

verfasst von : Weijian Li, Yuhang Lu, Kang Zheng, Haofu Liao, Chihung Lin, Jiebo Luo, Chi-Tung Cheng, Jing Xiao, Le Lu, Chang-Fu Kuo, Shun Miao

Erschienen in: Computer Vision – ECCV 2020

Verlag: Springer International Publishing

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Abstract

Image landmark detection aims to automatically identify the locations of predefined fiducial points. Despite recent success in this field, higher-ordered structural modeling to capture implicit or explicit relationships among anatomical landmarks has not been adequately exploited. In this work, we present a new topology-adapting deep graph learning approach for accurate anatomical facial and medical (e.g., hand, pelvis) landmark detection. The proposed method constructs graph signals leveraging both local image features and global shape features. The adaptive graph topology naturally explores and lands on task-specific structures which are learned end-to-end with two Graph Convolutional Networks (GCNs). Extensive experiments are conducted on three public facial image datasets (WFLW, 300W, and COFW-68) as well as three real-world X-ray medical datasets (Cephalometric (public), Hand and Pelvis). Quantitative results comparing with the previous state-of-the-art approaches across all studied datasets indicating the superior performance in both robustness and accuracy. Qualitative visualizations of the learned graph topologies demonstrate a physically plausible connectivity laying behind the landmarks.

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Vorheriges Kapitel Deep Hough Transform for Semantic Line Detection

Nächstes Kapitel 3D Human Shape and Pose from a Single Low-Resolution Image with Self-Supervised Learning

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Titel: Structured Landmark Detection via Topology-Adapting Deep Graph Learning
verfasst von: Weijian Li
Yuhang Lu
Kang Zheng
Haofu Liao
Chihung Lin
Jiebo Luo
Chi-Tung Cheng
Jing Xiao
Le Lu
Chang-Fu Kuo
Shun Miao
Verlag: Springer International Publishing
Buch: Computer Vision – ECCV 2020
Print ISBN: 978-3-030-58544-0

Electronic ISBN: 978-3-030-58545-7

Copyright-Jahr: 2020
DOI: https://doi.org/10.1007/978-3-030-58545-7_16

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