A stochastic grammar of images
Foundations and Trends® in Computer Graphics and Vision
Describing Visual Scenes Using Transformed Objects and Parts
International Journal of Computer Vision
Seeing the Objects Behind the Dots: Recognition in Videos from a Moving Camera
International Journal of Computer Vision
Unsupervised modeling of objects and their hierarchical contextual interactions
Journal on Image and Video Processing - Special issue on patches in vision
The number of all possible meaningful or discernible pictures
Pattern Recognition Letters
The generalized A* architecture
Journal of Artificial Intelligence Research
A new compositional technique for hand posture recognition
ICCOMP'09 Proceedings of the WSEAES 13th international conference on Computers
A Hierarchical and Contextual Model for Aerial Image Parsing
International Journal of Computer Vision
International Journal of Computer Vision
A Numerical Study of the Bottom-Up and Top-Down Inference Processes in And-Or Graphs
International Journal of Computer Vision
Inference and Learning with Hierarchical Shape Models
International Journal of Computer Vision
Predicate Logic Based Image Grammars for Complex Pattern Recognition
International Journal of Computer Vision
Context, Computation, and Optimal ROC Performance in Hierarchical Models
International Journal of Computer Vision
Recursive Compositional Models for Vision: Description and Review of Recent Work
Journal of Mathematical Imaging and Vision
Context models and out-of-context objects
Pattern Recognition Letters
A probabilistic model for component-based shape synthesis
ACM Transactions on Graphics (TOG) - SIGGRAPH 2012 Conference Proceedings
Learning a generative model of images by factoring appearance and shape
Neural Computation
Objects as attributes for scene classification
ECCV'10 Proceedings of the 11th European conference on Trends and Topics in Computer Vision - Volume Part I
Using grammars for pattern recognition in images: A systematic review
ACM Computing Surveys (CSUR)
Object Bank: An Object-Level Image Representation for High-Level Visual Recognition
International Journal of Computer Vision
Using adaptive background subtraction into a multi-level model for traffic surveillance
Integrated Computer-Aided Engineering
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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, unlike a Bayesian network, the distribution is non-Markovian, and therefore more like a probabilistic context-sensitive grammar. The conceptualization and construction of a composition machine is facilitated by its formulation as the result of a series of non-Markovian perturbations of a "Markov backbone."