The theory and practice of Bayesian image labeling
International Journal of Computer Vision
Performance Evaluation and Analysis of Monocular Building Extraction From Aerial Imagery
IEEE Transactions on Pattern Analysis and Machine Intelligence
Statistical Pattern Recognition: A Review
IEEE Transactions on Pattern Analysis and Machine Intelligence
Normalized Cuts and Image Segmentation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Dynamic shadow removal from front projection displays
Proceedings of the conference on Visualization '01
IEEE Transactions on Pattern Analysis and Machine Intelligence
ECCV '02 Proceedings of the 7th European Conference on Computer Vision-Part IV
Illuminant and gamma comprehensive normalisation in log RGB space
Pattern Recognition Letters - Special issue: Colour image processing and analysis
Combining color and geometry for the active, visual recognition of shadows
ICCV '95 Proceedings of the Fifth International Conference on Computer Vision
Illumination Normalization with Time-Dependent Intrinsic Images for Video Surveillance
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Fourier Theory for Cast Shadows
IEEE Transactions on Pattern Analysis and Machine Intelligence
Shadow identification and classification using invariant color models
ICASSP '01 Proceedings of the Acoustics, Speech, and Signal Processing, 2001. on IEEE International Conference - Volume 03
A shadow detection method for remote sensing images using affinity propagation algorithm
SMC'09 Proceedings of the 2009 IEEE international conference on Systems, Man and Cybernetics
GPS coordinates estimation and camera calibration from solar shadows
Computer Vision and Image Understanding
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A hierarchical shadow detection algorithm for color aerial images is presented in this paper to meet two challenges for static shadow detection in the literature: different brightness and illumination conditions in different images and the complexity of aerial images. The hierarchical algorithm consists of two levels of processing: the pixel level classification, achieved through modelling an image as a reliable graph (RG) and maximizing the graph reliability using the EM algorithm, and the region level verification, achieved through minimizing the Bayesian error by further exploiting the domain knowledge. Further analyses show that MRF model based segmentation is a special case of the RG model. The relationship between the RG model and the relaxation labeling model is also discussed. A quantitative comparison between this method and a state-of-the-art shadow detection algorithm clearly indicates that this method is promising for delivering effective shadow detection performance under different illumination and brightness conditions.