# Performing edge detection by difference of Gaussians using q-Gaussian kernels

arXiv: https://arxiv.org/abs/1311.2561 · HTML: https://mlsanity.com/paper/1311.2561/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 11 Nov 2013.

## Built on by (most cited first, 3 of 3)

- [Pseudo-Boolean Polynomials Approach To Edge Detection And Image Segmentation](https://mlsanity.com/paper/2308.15453/) (2023)
- [A Pseudo-Boolean Polynomials Approach for Image Edge Detection](https://mlsanity.com/paper/2308.15557/) (2023)
- [Online-Adaptive Anomaly Detection for Defect Identification in Aircraft Assembly](https://mlsanity.com/paper/2406.12698/) (2024)

## Abstract (arXiv)

In image processing, edge detection is a valuable tool to perform the extraction of features from an image. This detection reduces the amount of information to be processed, since the redundant information (considered less relevant) can be unconsidered. The technique of edge detection consists of determining the points of a digital image whose intensity changes sharply. This changes are due to the discontinuities of the orientation on a surface for example. A well known method of edge detection is the Difference of Gaussians (DoG). The method consists of subtracting two Gaussians, where a kernel has a standard deviation smaller than the previous one. The convolution between the subtraction of kernels and the input image results in the edge detection of this image. This paper introduces a method of extracting edges using DoG with kernels based on the q-Gaussian probability distribution, derived from the q-statistic proposed by Constantino Tsallis. To demonstrate the method's potential, we compare the introduced method with the traditional DoG using Gaussians kernels. The results showed that the proposed method can extract edges with more accurate details.