A Stochastic Framework for Object Localisation

We describe a Bayesian architecture to estimate the position and pose of a 3D object. The system starts with knowledge of the 3D structure of the object and the prior probability distribution of its position and orientation in the workspace. This information is used to guide the search for focus features in the image, and the information recovered from the image processing is used to reene the estimates of the x-y position and pose of the object. The results of intermediate stages of processing is propagated using a Bayesian methodology. After iteration around the network, the peaks of the nal probability distributions are used to estimate the position and pose, and the widths of the distributions provide a measure of conndence. The results of the study suggest that grey-level image processing algorithms and a simple 3D model, embedded in a Bayesian statistical reasoning architecture can provide a highly eeective, albeit specialised object localisation system.