Qualitative Probabilities for Image Interpretation

Two basic problems in image interpretation are: a) determining which interpretations are the most plausible amoungst many possibilities; and b) controlling the search for plausible interpretations. We address these issues using a Bayesian approach, with the plausibility ordering and search pruning based on the posterior probabilities of interpretations. However, due to the need for detailed quantitative prior probabilities and the need to evaluate complex integrals over various conditional distributions, a full Bayesian approach is currently impractical except in tightly constrained domains. To circumvent these difficulties we introduce the notion of qualitative probabilistic analysis. In particular, given spatial and contrast resolution parameters, we consider only the asymptotic order of the posterior probability for any interpretation as these resolutions are made finer. We introduce this approach for a simple card-world domain, and present computational results for blocks-world images.