# Second-order Anisotropic Gaussian Directional Derivative Filters for Blob Detection

arXiv: https://arxiv.org/abs/2305.00435 · HTML: https://mlsanity.com/paper/2305.00435/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 4 of its 26 references that are in the map.
- First posted on arXiv 30 Apr 2023.

## Builds on (most cited first, 4 of 4)

- [LIFT: Learned Invariant Feature Transform](https://mlsanity.com/paper/1603.09114/) (2016)
- [LF-Net: Learning Local Features from Images](https://mlsanity.com/paper/1805.09662/) (2018)
- [D2D: Keypoint Extraction with Describe to Detect Approach](https://mlsanity.com/paper/2005.13605/) (2020)
- [SEKD: Self-Evolving Keypoint Detection and Description](https://mlsanity.com/paper/2006.05077/) (2020)

## Abstract (arXiv)

Interest point detection methods have received increasing attention and are widely used in computer vision tasks such as image retrieval and 3D reconstruction. In this work, second-order anisotropic Gaussian directional derivative filters with multiple scales are used to smooth the input image and a novel blob detection method is proposed. Extensive experiments demonstrate the superiority of our proposed method over state-of-the-art benchmarks in terms of detection performance and robustness to affine transformations.