# Affine Differential Invariants for Invariant Feature Point Detection

arXiv: https://arxiv.org/abs/1803.01669 · HTML: https://mlsanity.com/paper/1803.01669/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 5 Mar 2018.

## Built on by (most cited first, 2 of 2)

- [Affine steerers for structured keypoint description](https://mlsanity.com/paper/2408.14186/) (2024)
- [Deep Signatures -- Learning Invariants of Planar Curves](https://mlsanity.com/paper/2202.05922/) (2022)

## Abstract (arXiv)

Image feature points are detected as pixels which locally maximize a detector function, two commonly used examples of which are the (Euclidean) image gradient and the Harris-Stephens corner detector. A major limitation of these feature detectors are that they are only Euclidean-invariant. In this work we demonstrate the application of a 2D affine-invariant image feature point detector based on differential invariants as derived through the equivariant method of moving frames. The fundamental equi-affine differential invariants for 3D image volumes are also computed.