Computer Vision, Graphics, and Image Processing
A critical view of pyramid segmentation algorithms
Pattern Recognition Letters
Hierarchical Image Analysis Using Irregular Tessellations
IEEE Transactions on Pattern Analysis and Machine Intelligence
The adaptive pyramid: a framework for 2D image analysis
CVGIP: Image Understanding
A Pyramid Framework for Early Vision: Multiresolutional Computer Vision
A Pyramid Framework for Early Vision: Multiresolutional Computer Vision
Pattern Recognition Letters - Special issue: Graph-based representations in pattern recognition
Graph theory: An algorithmic approach (Computer science and applied mathematics)
Graph theory: An algorithmic approach (Computer science and applied mathematics)
Vision pyramids that do not grow too high
Pattern Recognition Letters - Special issue: In memoriam Azriel Rosenfeld
Pyramid segmentation algorithms revisited
Pattern Recognition
Annotated Contraction Kernels for Interactive Image Segmentation
GbRPR '09 Proceedings of the 7th IAPR-TC-15 International Workshop on Graph-Based Representations in Pattern Recognition
Comparison of Perceptual Grouping Criteria within an Integrated Hierarchical Framework
GbRPR '09 Proceedings of the 7th IAPR-TC-15 International Workshop on Graph-Based Representations in Pattern Recognition
Construction of combinatorial pyramids
GbRPR'03 Proceedings of the 4th IAPR international conference on Graph based representations in pattern recognition
Constructing stochastic pyramids by MIDES: maximal independent directed edge set
GbRPR'03 Proceedings of the 4th IAPR international conference on Graph based representations in pattern recognition
A graph-based concept for spatiotemporal information in cognitive vision
GbRPR'05 Proceedings of the 5th IAPR international conference on Graph-Based Representations in Pattern Recognition
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We present a new method to determine contraction kernels for the construction of graph pyramids. The new method works with undirected graphs and yields a reduction factor of at least 2.0. This means that with our method the number of vertices in the subgraph induced by any set of contractible edges is reduced to half or less by a single parallel contraction. Our method yields better reduction factors than the stochastic decimation algorithm, in all tests. The lower bound of the reduction factor becomes crucial with large images.