Edge Tracing using Gaussian Process Regression

TL;DR

This paper presents a new edge tracing algorithm that uses Gaussian process regression to model and iteratively refine edge detection, offering robustness to artefacts and occlusions without requiring prior training.

Contribution

The novel edge tracing method combines local and global information through Gaussian process regression, enabling robust, training-free edge detection adaptable to various imaging domains.

Findings

Effective in medical and satellite imaging applications

Robust to artefacts and occlusions

Outperforms two common edge tracing algorithms

Abstract

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.