Feature Hourglass Network for Skeleton Detection
| Autor: | Dezhao Luo, Yifei Zhang, Nan Jiang, Chang Liu, Yu Zhou, Zhenjun Han |
|---|---|
| Rok vydání: | 2019 |
| Předmět: |
Pixel
business.industry Computer science Deep learning Feature extraction 020206 networking & telecommunications Pattern recognition 02 engineering and technology Geometric shape Feature (computer vision) 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing Point (geometry) Artificial intelligence business Representation (mathematics) Pruning (morphology) ComputingMethodologies_COMPUTERGRAPHICS |
| Zdroj: | CVPR Workshops |
| DOI: | 10.1109/cvprw.2019.00154 |
| Popis: | Geometric shape understanding provides an intuitive representation of object shapes. Skeleton is typical geometrical information. Lots of traditional approaches are developed for skeleton extraction and pruning, while it is still a new area to investigate deep learning for geometric shape understanding. In this paper, we build a fully convolutional network named Feature Hourglass Network (FHN) for skeleton detection. FHN uses rich features of a fully convolutional network by hierarchically integrating side-outputs with a deep-to-shallow manner to decrease the residual between the prediction result and the ground-truth. Experiment data shows that FHN achieves better performance compared with baseline on both Pixel SkelNetOn and Point SkelNetOn datasets. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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