Exploring Qualitative Probabilities for image understanding

grantor: University of TorontoIn this thesis we explore and work with a particular probabilistic framework for image interpretation called Qualitative Probabilities. Introduced by Jepson and Mann, Qualitative Probabilities formalize the notion of non-accidentalness, a cornerstone of object recognition. First we examine the search space associated with Qualitative Probabilities. We also experimentally verify one of the underlying principles of the theory, the asymptotic rate of 'accidents'. Then we incorporate Qualitative Probabilities into a relatively simple search which we find to be efficient and effective. Comparing search for interpretations using Qualitative Probabilities to search using a more standard 'cover' measure, we find that the former is far superior both in terms of efficiency and quality of block models found. Lastly, we design and test a new search algorithm, called Cascade search that uses Qualitative Probabilities.M.Sc