Building detection and description from a single intensity image
Computer Vision and Image Understanding
Building detection and reconstruction from mid-and high-resolution aerial imagery
Computer Vision and Image Understanding
Automatic object extraction from aerial imagery—a survey focusing on buildings
Computer Vision and Image Understanding
Markov Random Field Modeling in Computer Vision
Markov Random Field Modeling in Computer Vision
Conditional Random Fields: Probabilistic Models for Segmenting and Labeling Sequence Data
ICML '01 Proceedings of the Eighteenth International Conference on Machine Learning
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
Spatial Priors for Part-Based Recognition Using Statistical Models
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 1 - Volume 01
Guest Editors' Introduction to the Special Section on Syntactic and Structural Pattern Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
Man-made structure detection in natural images using a causal multiscale random field
CVPR'03 Proceedings of the 2003 IEEE computer society conference on Computer vision and pattern recognition
Delineating buildings by grouping lines with MRFs
IEEE Transactions on Image Processing
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We view the task of change detection as a problem of object recognition from learning. The object is defined in a 3D space where the time is the 3rd dimension. We propose two competitive probabilistic models. The first one has a traditional regard on change, characterized as a ’presence-absence’ within two scenes. The model is based on a logistic function, embedded in a framework called ’cut-and-merge’. The second approach is inspired from the Discriminative Random Fields (DRF) approach proposed by Ma and Hebert [KUMA2003]. The energy function is defined as the sum of an association potential and an interaction potential. We formulate the latter as a 3D anisotropic term. A simplified implementation enables to achieve fast computation in the 2D image space. In conclusion, the main contributions of this paper rely on : 1) the extension of the DRF to a 3D manifold ; 2) the cut-and-merge algorithm. The application proposed in the paper is on remote sensing images, for building change detection. Results on synthetic and real scenes and comparative analysis demonstrate the effectiveness of the proposed approach.