ducha-aiki / hesaff-pytorch

This is implementation of Hessian-Affine local feature detector. It is heavily based on Michal Perdoch C++ implementation https://github.com/perdoch/hesaff

pytaff - current implementation hesamp - Michal Perdoch C++ one.

There are several differences:

1. No SIFT description, the output is image patches. If one needs to, patches could be feed into PyTorchSIFT

2. Subpixel precision is done via "center-of-responce-mass" inspired by LIFT paper, instead of original iterative quadratic fitting

3. Instead of setting threshold to control number of detection, this implementation simply outputs top-K local extreme points.

You also might be interested in HesAffNet, which gives significantly better results because of learned affine shape estimation procedure.

If you use this code for academic purposes, please cite the following paper:

@article{AffNet2017,
 author = {Dmytro Mishkin, Filip Radenovic, Jiri Matas},
    title = "{Learning Discriminative Affine Regions via Discriminability}",
     year = 2017,
    month = nov}