Causality: models, reasoning, and inference
Causality: models, reasoning, and inference
Modelling and Interpretation of Architecture from Several Images
International Journal of Computer Vision
Histograms of Oriented Gradients for Human Detection
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 1 - Volume 01
Hierarchical Support Vector Random Fields: Joint Training to Combine Local and Global Features
ECCV '08 Proceedings of the 10th European Conference on Computer Vision: Part II
Languages for constrained binary segmentation based on maximum a posteriori probability labeling
International Journal of Imaging Systems and Technology - Contemporary Challenges in Combinatorial Image Analysis
Multi-class image segmentation using conditional random fields and global classification
ICML '09 Proceedings of the 26th Annual International Conference on Machine Learning
An Irregular Pyramid for Multi-scale Analysis of Objects and Their Parts
GbRPR '09 Proceedings of the 7th IAPR-TC-15 International Workshop on Graph-Based Representations in Pattern Recognition
Evaluation of Structure Recognition Using Labelled Facade Images
Proceedings of the 31st DAGM Symposium on Pattern Recognition
Learning the Compositional Nature of Visual Object Categories for Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
Extraction and integration of window in a 3D building model from ground view images
CVPR'04 Proceedings of the 2004 IEEE computer society conference on Computer vision and pattern recognition
Contour Detection and Hierarchical Image Segmentation
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
ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part I
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We propose a concept for scene interpretation with integrated hierarchical structure. This hierarchical structure is used to detect mereological relations between complex objects as buildings and their parts, e. g., windows. We start with segmenting regions at many scales, arranging them in a hierarchy, and classifying them by a common classifier. Then, we use the hierarchy graph of regions to construct a conditional Bayesian network, where the probabilities of class occurrences in the hierarchy are used to improve the classification results of the segmented regions in various scales. The interpreted regions can be used to derive a consistent scene representation, and they can be used as object detectors as well. We show that our framework is able to learn models for several objects, such that we can reliably detect instances of them in other images.