Fitting Skeletal Models via Graph-based Learning
Fuente:
arXiv
Saved in:
| Main Authors: | , , , |
|---|---|
| Format: | Preprint |
| Published: |
2024
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866909308862595072 |
|---|---|
| author | Gaggion, Nicolás Ferrante, Enzo Paniagua, Beatriz Vicory, Jared |
| author_facet | Gaggion, Nicolás Ferrante, Enzo Paniagua, Beatriz Vicory, Jared |
| contents | Skeletonization is a popular shape analysis technique that models an object's interior as opposed to just its boundary. Fitting template-based skeletal models is a time-consuming process requiring much manual parameter tuning. Recently, machine learning-based methods have shown promise for generating s-reps from object boundaries. In this work, we propose a new skeletonization method which leverages graph convolutional networks to produce skeletal representations (s-reps) from dense segmentation masks. The method is evaluated on both synthetic data and real hippocampus segmentations, achieving promising results and fast inference. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2409_05311 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Fitting Skeletal Models via Graph-based Learning Gaggion, Nicolás Ferrante, Enzo Paniagua, Beatriz Vicory, Jared Computer Vision and Pattern Recognition Skeletonization is a popular shape analysis technique that models an object's interior as opposed to just its boundary. Fitting template-based skeletal models is a time-consuming process requiring much manual parameter tuning. Recently, machine learning-based methods have shown promise for generating s-reps from object boundaries. In this work, we propose a new skeletonization method which leverages graph convolutional networks to produce skeletal representations (s-reps) from dense segmentation masks. The method is evaluated on both synthetic data and real hippocampus segmentations, achieving promising results and fast inference. |
| title | Fitting Skeletal Models via Graph-based Learning |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2409.05311 |