Artificial intelligence: a modern approach
Artificial intelligence: a modern approach
Building detection and description from a single intensity image
Computer Vision and Image Understanding
Extracting buildings from aerial images using hierachical aggregation in 2D and 3D
Computer Vision and Image Understanding
Automatic object extraction from aerial imagery—a survey focusing on buildings
Computer Vision and Image Understanding
Automatic Extraction of Generic House Roofs from High Resolution Aerial Imagery
ECCV '96 Proceedings of the 4th European Conference on Computer Vision-Volume I - Volume I
Supervised Learning of Edges and Object Boundaries
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 2
Automatic description of complex buildings from multiple images
Computer Vision and Image Understanding
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In this paper, we present a Self-Avoiding Polygon (SAP) model for describing and detecting complex gable rooftops from nadir-view aerial imagery We demonstrate that a broad range of gable rooftop shapes can be summarized as self-avoiding polygons, whose vertices correspond to roof corners The SAP model, defined over the joint space of all possible SAPs and images, combines the shape prior embedded in SAP and a set of appearance features (edge, color and texture) learned from training images Given an observed image, the posterior probability of the SAP model measures how well each SAP fits the observed data Our inference algorithm follows the MAP framework, i.e detecting the best gable roof is equivalent to finding the optimal self-avoiding polygon on the image plain Even though the entire state space of all SAPs is enormous, we find that by using A* search, commonly our algorithm can find the optimal solution in polynominal time Experiments on a set of challenging image shows promising performance.