IEEE Transactions on Pattern Analysis and Machine Intelligence
Computer Vision, Graphics, and Image Processing
Scale-Space and Edge Detection Using Anisotropic Diffusion
IEEE Transactions on Pattern Analysis and Machine Intelligence
Scale-Space Theory in Computer Vision
Scale-Space Theory in Computer Vision
Multiscale Gradient Magnitude Watershed Segmentation
ICIAP '97 Proceedings of the 9th International Conference on Image Analysis and Processing-Volume I - Volume I
Scale-Space Filters and Their Robustness
SCALE-SPACE '97 Proceedings of the First International Conference on Scale-Space Theory in Computer Vision
The hierarchy of the cocoons of a graph and its application to image segmentation
Pattern Recognition Letters - Special issue: Graph-based representations in pattern recognition
Feature Hierarchies for Object Classification
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision (ICCV'05) Volume 1 - Volume 01
Learning a Knowledge Base of Ontological Concepts for High-Level Scene Interpretation
ICMLA '07 Proceedings of the Sixth International Conference on Machine Learning and Applications
IJCAI'83 Proceedings of the Eighth international joint conference on Artificial intelligence - Volume 2
Hierarchical watersheds within the combinatorial pyramid framework
DGCI'05 Proceedings of the 12th international conference on Discrete Geometry for Computer Imagery
Image segmentation and analysis via multiscale gradient watershed hierarchies
IEEE Transactions on Image Processing
A Bayesian approach for scene interpretation with integrated hierarchical structure
DAGM'11 Proceedings of the 33rd international conference on Pattern recognition
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We present an irregular image pyramid which is derived from multi-scale analysis of segmented watershed regions. Our framework is based on the development of regions in the Gaussian scale-space, which is represented by a region hierarchy graph. Using this structure, we are able to determine geometrically precise borders of our segmented regions using a region focusing. In order to handle the complexity, we select only stable regions and regions resulting from a merging event, which enables us to keep the hierarchical structure of the regions. Using this framework, we are able to detect objects of various scales in an image. Finally, the hierarchical structure is used for describing these detected regions as aggregations of their parts. We investigate the usefulness of the regions for interpreting images showing building facades with parts like windows, balconies or entrances.