Computational Strategies for Model-Based Scene Interpretation

Scene interpretation, in the sense of detecting and localizing instances from multiple object classes, is formulated as a two-step process in which non-contextual detection primes global interpretation. During detection a list of instantiations (object identities and poses) is compiled constrained only by invariance no missed detections at the expense of false positives. Contextual information, such as expected relationships among poses, is incorporated afterwards to remove ambiguities. This division is motivated by computational efficiency. In addition, detection itself is organized as a coarse-to-fine search simultaneously in class and pose. This search can be interpreted as successive approximations to likelihood ratio tests arising from a simple (“naive Bayes”) statistical model for the edge maps extracted from the original images. The key to constructing efficient “hypothesis tests” for multiple classes and poses is local ORing; in particular, spread edges provide imprecise but common and locally invariant features. Natural tradeoffs then emerge between discrimination and the shape and extent of spreading. These are analyzed mathematically within the model-based framework and the whole procedure is illustrated by experiments in reading license plates.