# Edge Tracing using Gaussian Process Regression

arXiv: https://arxiv.org/abs/2111.03605 · HTML: https://mlsanity.com/paper/2111.03605/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 48 references that are in the map.
- First posted on arXiv 5 Nov 2021.

## Builds on (most cited first, 5 of 5)

- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks](https://mlsanity.com/paper/1506.01497/) (2015)
- [V-Net: Fully Convolutional Neural Networks for Volumetric Medical Image Segmentation](https://mlsanity.com/paper/1606.04797/) (2016)
- [A Joint introduction to Gaussian Processes and Relevance Vector Machines with Connections to Kalman filtering and other Kernel Smoothers](https://mlsanity.com/paper/2009.09217/) (2020)
- [Segmentation of Satellite Imagery using U-Net Models for Land Cover Classification](https://mlsanity.com/paper/2003.02899/) (2020)

## Built on by (most cited first, 4 of 4)

- [An open-source deep learning algorithm for efficient and fully-automatic analysis of the choroid in optical coherence tomography](https://mlsanity.com/paper/2307.00904/) (2023)
- [Choroidalyzer: An open-source, end-to-end pipeline for choroidal analysis in optical coherence tomography](https://mlsanity.com/paper/2312.02956/) (2023)
- [Domain-specific augmentations with resolution agnostic self-attention mechanism improves choroid segmentation in optical coherence tomography images](https://mlsanity.com/paper/2405.14453/) (2024)
- [A General Ambiguity Model for Binary Edge Images with Edge Tracing and its Implementation](https://mlsanity.com/paper/2408.01712/) (2024)

## Abstract (arXiv)

We introduce a novel edge tracing algorithm using Gaussian process regression. Our edge-based segmentation algorithm models an edge of interest using Gaussian process regression and iteratively searches the image for edge pixels in a recursive Bayesian scheme. This procedure combines local edge information from the image gradient and global structural information from posterior curves, sampled from the model's posterior predictive distribution, to sequentially build and refine an observation set of edge pixels. This accumulation of pixels converges the distribution to the edge of interest. Hyperparameters can be tuned by the user at initialisation and optimised given the refined observation set. This tunable approach does not require any prior training and is not restricted to any particular type of imaging domain. Due to the model's uncertainty quantification, the algorithm is robust to artefacts and occlusions which degrade the quality and continuity of edges in images. Our approach also has the ability to efficiently trace edges in image sequences by using previous-image edge traces as a priori information for consecutive images. Various applications to medical imaging and satellite imaging are used to validate the technique and comparisons are made with two commonly used edge tracing algorithms.