Detecting buildings in aerial images
Computer Vision, Graphics, and Image Processing
Using Perceptual Organization to Extract 3D Structures
IEEE Transactions on Pattern Analysis and Machine Intelligence
Building detection and description from a single intensity image
Computer Vision and Image Understanding
The ascender system: automated site modeling from multiple aerial images
Computer Vision and Image Understanding
Extracting buildings from aerial images using hierachical aggregation in 2D and 3D
Computer Vision and Image Understanding
Detection and Modeling of Buildings from Multiple Aerial Images
IEEE Transactions on Pattern Analysis and Machine Intelligence
Automatic description of complex buildings from multiple images
Computer Vision and Image Understanding
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This paper presents a new building rooftop extraction method from aerial images. In our approach, we extract the useful building location information from the generated disparity map to segment the interested objects and consequently reduce unnecessary line segments extracted in low level feature extraction step. Hypothesis selection is carried out by using undirected graph, in which close cycles represent complete rooftops hypotheses. We test the proposed method with the synthetic images generated from Avenches dataset of Ascona aerial images. The experiment result shows that the extracted 3D line segments of the reconstructed buildings have an average error of 1.69m and our method can be efficiently used for the task of building detection and reconstruction from aerial images.