Context and Hierarchy in a Probabilistic Image Model

It is widely conjectured that the excellent ROC performance of biological vision systems is due in large part to the exploitation of context at each of many levels in a part/whole hierarchy. We propose a mathematical framework (a "composition machine") for constructing probabilistic hierarchical image models, designed to accommodate arbitrary contextual relationships, and we build a demonstration system for reading Massachusetts license plates in an image set collected at Logan Airport. The demonstration system detects and correctly reads more than 98% of the plates, with a negligible rate of false detection. Unlike a formal grammar, the architecture of a composition machine does not exclude the sharing of sub-parts among multiple entities, and does not limit interpretations to single trees (e.g. a scene can have multiple license plates, or no plates at all). In this sense, the architecture is more like a general Bayesian network than a formal grammar. On the other hand, unl...

• Type: Conference
• Year: 2006
• Where: CVPR
• Authors: Ya Jin, Stuart Geman
• Keywords: Composition Machine, Computer Vision, CVPR 2006, Formal Grammar, Massachusetts License Plates, Multiple License Plates, Probabilistic Context-sensitive Grammar