Probabilistic Semantic Segmentation Refinement by Monte Carlo Region Growing

Understand

Semantic segmentation with fine-grained pixel-level accuracy is a fundamental component of a variety of computer vision applications.

• However, despite the large improvements provided by recent advances in the architectures of convolutional neural networks, segmentations provided by modern state-of-the-art methods still show limited boundary adherence.
• We introduce a fully unsupervised post-processing algorithm that exploits Monte Carlo sampling and pixel similarities to propagate high-confidence pixel labels into regions of low-confidence classification.
• Our algorithm, which we call probabilistic Region Growing Refinement (pRGR), is based on a rigorous mathematical foundation in which clusters are modelled as multivariate normally distributed sets of pixels.