# A Pseudo-Boolean Polynomials Approach for Image Edge Detection

arXiv: https://arxiv.org/abs/2308.15557 · HTML: https://mlsanity.com/paper/2308.15557/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 4 of its 34 references that are in the map.
- First posted on arXiv 29 Aug 2023.

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

- [A Comprehensive Review of Image Analysis Methods for Microorganism Counting: From Classical Image Processing to Deep Learning Approaches](https://mlsanity.com/paper/2103.13625/) (2021)
- [A Feature Memory Rearrangement Network for Visual Inspection of Textured Surface Defects Toward Edge Intelligent Manufacturing](https://mlsanity.com/paper/2206.10830/) (2022)
- [Quantifying Explainability of Saliency Methods in Deep Neural Networks with a Synthetic Dataset](https://mlsanity.com/paper/2009.02899/) (2020)
- [Performing edge detection by difference of Gaussians using q-Gaussian kernels](https://mlsanity.com/paper/1311.2561/) (2013)

## Abstract (arXiv)

We introduce a novel approach for image edge detection based on pseudo-Boolean polynomials for image patches. We show that patches covering edge regions in the image result in pseudo-Boolean polynomials with higher degrees compared to patches that cover blob regions. The proposed approach is based on reduction of polynomial degree and equivalence properties of penalty-based pseudo-Boolean polynomials.