# Texture Edge detection by Patch consensus (TEP)

arXiv: https://arxiv.org/abs/2403.11038 · HTML: https://mlsanity.com/paper/2403.11038/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 7 of its 35 references that are in the map.
- First posted on arXiv 16 Mar 2024.

## Builds on (most cited first, 7 of 7)

- [Holistically-Nested Edge Detection](https://mlsanity.com/paper/1504.06375/) (2015)
- [Richer Convolutional Features for Edge Detection](https://mlsanity.com/paper/1612.02103/) (2016)
- [Dense Extreme Inception Network: Towards a Robust CNN Model for Edge Detection](https://mlsanity.com/paper/1909.01955/) (2019)
- [Bi-Directional Cascade Network for Perceptual Edge Detection](https://mlsanity.com/paper/1902.10903/) (2019)
- [DeepEdge: A Multi-Scale Bifurcated Deep Network for Top-Down Contour Detection](https://mlsanity.com/paper/1412.1123/) (2014)
- [A survey of exemplar-based texture synthesis](https://mlsanity.com/paper/1707.07184/) (2017)
- [On the Self-Similarity of Natural Stochastic Textures](https://mlsanity.com/paper/1906.06768/) (2019)

## Abstract (arXiv)

We propose Texture Edge detection using Patch consensus (TEP) which is a training-free method to detect the boundary of texture. We propose a new simple way to identify the texture edge location, using the consensus of segmented local patch information. While on the boundary, even using local patch information, the distinction between textures are typically not clear, but using neighbor consensus give a clear idea of the boundary. We utilize local patch, and its response against neighboring regions, to emphasize the similarities and the differences across different textures. The step of segmentation of response further emphasizes the edge location, and the neighborhood voting gives consensus and stabilize the edge detection. We analyze texture as a stationary process to give insight into the patch width parameter verses the quality of edge detection. We derive the necessary condition for textures to be distinguished, and analyze the patch width with respect to the scale of textures. Various experiments are presented to validate the proposed model.