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- W3202022162 abstract "3D anatomical landmarks play an important role in health research. Their automated prediction/localization thus becomes a vital task. In this paper, we introduce a deformation method for 3D anatomical landmarks prediction. It utilizes a source model with anatomical landmarks which are annotated by clinicians and deforms this model non-rigidly to match the target model. Two constraints are presented in the optimization, which are responsible for alignment and smoothness, respectively. Experiments are performed on our dataset and the results demonstrate the robustness of our method and show that it yields better performance than the previous techniques in most cases." @default.
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- W3202022162 date "2023-01-01" @default.
- W3202022162 modified "2023-09-26" @default.
- W3202022162 title "Anatomical Landmarks Localization for 3D Foot Point Clouds" @default.
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- W3202022162 doi "https://doi.org/10.1007/978-3-031-30111-7_53" @default.
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