Global Detection of Salient Convex Boundaries
International Journal of Computer Vision
Pre-Attentive and Attentive Detection of Humans in Wide-Field Scenes
International Journal of Computer Vision
Edge affinity for pose-contour matching
Computer Vision and Image Understanding
Shape representation by a network of V4-like cells
Neural Networks
Detection and recognition of contour parts based on shape similarity
Pattern Recognition
Neural modeling of flow rendering effectiveness
Proceedings of the 5th symposium on Applied perception in graphics and visualization
A Global Contour-Grouping Algorithm Based on Spectral Clustering
ISNN '08 Proceedings of the 5th international symposium on Neural Networks: Advances in Neural Networks, Part II
Learning probabilistic structure to group image edges for object extraction
ICME'09 Proceedings of the 2009 IEEE international conference on Multimedia and Expo
An Approach to the Parameterization of Structure for Fast Categorization
International Journal of Computer Vision
Generalizing edge detection to contour detection for image segmentation
Computer Vision and Image Understanding
Segmentation of crystalline lens in photorefraction video
Proceedings of the Seventh Indian Conference on Computer Vision, Graphics and Image Processing
Computational-geometry approach to digital image contour extraction
Transactions on computational science XIII
Contour grouping: focusing on image patches around edges
VSMM'06 Proceedings of the 12th international conference on Interactive Technologies and Sociotechnical Systems
A tracking approach to parcellation of the cerebral cortex
MICCAI'05 Proceedings of the 8th international conference on Medical Image Computing and Computer-Assisted Intervention - Volume Part I
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Conventional approaches to perceptual grouping assume little specific knowledge about the object(s) of interest. However, there are many applications in which such knowledge is available and useful. Here, we address the problem of finding the bounding contour of an object in an image when some prior knowledge about the object is available. We introduce a framework for combining prior probabilistic knowledge of the appearance of the object with probabilistic models for contour grouping. A constructive search technique is used to compute candidate closed object boundaries, which are then evaluated by combining figure, ground, and prior probabilities to compute the maximum a posteriori estimate. A significant advantage of our formulation is that it rigorously combines probabilistic local cues with important global constraints such as simplicity (no self-intersections), closure, completeness, and nontrivial scale priors. We apply this approach to the problem of computing exact lake boundaries from satellite imagery, given approximate prior knowledge from an existing digital database. We quantitatively evaluate the performance of our algorithm and find that it exceeds the performance of human mapping experts and a competing active contour approach, even with relatively weak prior knowledge. While the priors may be task-specific, the approach is general, as we demonstrate by applying it to a completely different problem: the computation of human skin boundaries in natural imagery.