Constraint-based grammar formalisms: parsing and type inference for natural and computer languages
Constraint-based grammar formalisms: parsing and type inference for natural and computer languages
A computational model for visual selection
Neural Computation
International Journal of Computer Vision - Special issue on statistical and computational theories of vision: Part II
Class-Specific, Top-Down Segmentation
ECCV '02 Proceedings of the 7th European Conference on Computer Vision-Part II
Using Hierarchical Shape Models to Spot Keywords in Cursive Handwriting Data
CVPR '98 Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition
Feature Hierarchies for Object Classification
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision (ICCV'05) Volume 1 - Volume 01
Learning Hierarchical Models of Scenes, Objects, and Parts
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision - Volume 2
Context and Hierarchy in a Probabilistic Image Model
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 2
Bottom-Up & Top-down Object Detection using Primal Sketch Features and Graphical Models
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 2
Robust Object Recognition with Cortex-Like Mechanisms
IEEE Transactions on Pattern Analysis and Machine Intelligence
POP: Patchwork of Parts Models for Object Recognition
International Journal of Computer Vision
A stochastic grammar of images
Foundations and Trends® in Computer Graphics and Vision
A Probabilistic Cascade of Detectors for Individual Object Recognition
ECCV '08 Proceedings of the 10th European Conference on Computer Vision: Part III
Statistical inference and probabilistic modeling in compositional vision
Statistical inference and probabilistic modeling in compositional vision
A Numerical Study of the Bottom-Up and Top-Down Inference Processes in And-Or Graphs
International Journal of Computer Vision
Object recognition based on visual grammars and Bayesian networks
IJCAI'13 Proceedings of the Twenty-Third international joint conference on Artificial Intelligence
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It is widely recognized that human vision relies on contextual information, typically arising from each of many levels of analysis. Local gradient information, otherwise ambiguous, is seen as part of a smooth contour or sharp angle in the context of an object's boundary or corner. A stroke or degraded letter, unreadable by itself, contributes to the perception of a familiar word in the context of the surrounding strokes and letters. The iconic Dalmatian dog stays invisible until a multitude of clues about body parts and posture, and figure and ground, are coherently integrated. Context is always based on knowledge about the composition of parts that make up a whole, as in the arrangement of strokes that make up a letter, the arrangement of body parts that make up an animal, or the poses and postures of individuals that make up a mob. From this point of view, the hierarchy of contextual information available to an observer derives from the compositional nature of the world being observed. We will formulate this combinatorial viewpoint in terms of probability distributions and examine the computational implications. Whereas optimal recognition performance in this formulation is NP-complete, we will give mathematical and experimental evidence that a properly orchestrated computational algorithm can achieve nearly optimal recognition within a feasible number of operations. We will interpret the notions of bottom-up and top-down processing as steps in the staging of one such orchestration.