  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.
