BayesBD: An R Package for Bayesian Inference on Image Boundaries

TL;DR

BayesBD is an R package that provides efficient Bayesian methods for accurately estimating image boundaries in noisy images, with guarantees on geometric restrictions and adaptive convergence rates.

Contribution

It introduces a flexible Gaussian process prior framework for boundary detection that is computationally efficient and adaptable to complex image structures.

Findings

Achieves nearly minimax optimal convergence rates

Demonstrates superior performance in simulations and real data

Provides user-friendly tools for boundary inference in noisy images

Abstract

We present the BayesBD package providing Bayesian inference for boundaries of noisy images. The BayesBD package implements flexible Gaussian process priors indexed by the circle to recover the boundary in a binary or Gaussian noised image, with the benefits of guaranteed geometric restrictions on the estimated boundary, (nearly) minimax optimal and smoothness adaptive convergence rates, and convenient joint inferences under certain assumptions. The core sampling tasks for our model have linear complexity, and our implementation in c++ using packages Rcpp and RcppArmadillo is computationally efficient. Users can access the full functionality of the package in both Rgui and the corresponding shiny application. Additionally, the package includes numerous utility functions to aid users in data preparation and analysis of results. We compare BayesBD with selected existing packages using both simulations and real data applications, and demonstrate the excellent performance and flexibility of BayesBD even when the observation contains complicated structural information that may violate its assumptions.