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Feature Learning

Code released as part of the published paper:

Barbed, O. L., Chadebecq, F., Morlana, J., Montiel, J. M., & Murillo, A. C. (2022, December). SuperPoint features in endoscopy. In Imaging Systems for GI Endoscopy, and Graphs in Biomedical Image Analysis: First MICCAI Workshop, ISGIE 2022, and Fourth MICCAI Workshop, GRAIL 2022, Held in Conjunction with MICCAI 2022, Singapore, September 18, 2022, Proceedings (pp. 45-55). Springer link ArXiV link

This repository is a modified implementation of:

DeTone, Daniel, Tomasz Malisiewicz, and Andrew Rabinovich. "Superpoint: Self-supervised interest point detection and description." Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops. 2018. CVF Open Access Link.

It is based on another implementation by You-Yi Jau and Rui Zhu: https://github.com/eric-yyjau/pytorch-superpoint

The code has been developed as part of the european project EndoMapper.

Feature extraction in endoscopy videos

Consecutive frames Frames 1 second apart
v33_s14_31_32 v33_s14_31_70
v33_s19_1_2 v33_s19_1_40

Results

Well-known local feature detectors

SIFT ORB SuperPoint
SIFT ORB SuperPoint
SIFT ORB SuperPoint
SIFT ORB SuperPoint
SIFT ORB SuperPoint

Our models

E-SuperPoint E-SuperPoint+S
E-SuperPoint E-SuperPoint+S
E-SuperPoint E-SuperPoint+S
E-SuperPoint E-SuperPoint+S
E-SuperPoint E-SuperPoint+S

Authors

Developed by Óscar León Barbed, Ana C. Murillo & François Chadebecq, Javier Morlana, José María Martínez Montiel.

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SuperPoint features in endoscopy

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