Sequential Monte Carlo for probabilistic object detection in images

Abstract

Object detection is a fundamental image analysis task that is relevant to many scientific disciplines. For example, astronomers detect stars and galaxies in astronomical images and biologists characterize cells and tissues in histological images. Distinguishing visually overlapping objects in images is challenging, as there is inherent ambiguity in the positions and properties of these objects. The Bayesian paradigm is well suited to this task because it provides calibrated uncertainty estimates for crowded scenes and enables scientists to incorporate prior knowledge about the imaged objects. We propose count-stratified SMC (CS-SMC), a novel Bayesian approach to object detection based on sequential Monte Carlo. Given an image, CS-SMC evaluates latent variable catalogs corresponding to various object counts and infers the posterior distribution over sets of objects. Although these sets vary in size, CS-SMC does not require transdimensional sampling, unlike existing methods for probabilistic object detection based on Markov chain Monte Carlo. We demonstrate the advantages of CS-SMC in a case study involving astronomical images of densely populated starfields.