Hierarchy in Picture Segmentation: A Stepwise Optimization Approach
IEEE Transactions on Pattern Analysis and Machine Intelligence
Geodesic Saliency of Watershed Contours and Hierarchical Segmentation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Mean Shift: A Robust Approach Toward Feature Space Analysis
IEEE Transactions on Pattern Analysis and Machine Intelligence
Introduction to Combinatorial Pyramids
Digital and Image Geometry, Advanced Lectures [based on a winter school held at Dagstuhl Castle, Germany in December 2000]
ICPR '02 Proceedings of the 16 th International Conference on Pattern Recognition (ICPR'02) Volume 4 - Volume 4
Multiclass Spectral Clustering
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
Learning a Classification Model for Segmentation
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
Efficient Graph-Based Image Segmentation
International Journal of Computer Vision
Spectral Segmentation with Multiscale Graph Decomposition
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 2 - Volume 02
Contains and inside relationships within combinatorial pyramids
Pattern Recognition
Hierarchical Matching Using Combinatorial Pyramid Framework
ICISP '08 Proceedings of the 3rd international conference on Image and Signal Processing
TurboPixels: Fast Superpixels Using Geometric Flows
IEEE Transactions on Pattern Analysis and Machine Intelligence
Superpixels and supervoxels in an energy optimization framework
ECCV'10 Proceedings of the 11th European conference on Computer vision: Part V
Contour detection and image segmentation
Contour detection and image segmentation
Contour Detection and Hierarchical Image Segmentation
IEEE Transactions on Pattern Analysis and Machine Intelligence
A polynomial algorithm for submap isomorphism of general maps
Pattern Recognition Letters
A new perception-based segmentation approach using combinatorial pyramids
ICIAP'11 Proceedings of the 16th international conference on Image analysis and processing: Part I
Considerations regarding the minimum spanning tree pyramid segmentation method
SSPR'06/SPR'06 Proceedings of the 2006 joint IAPR international conference on Structural, Syntactic, and Statistical Pattern Recognition
Hierarchical watersheds within the combinatorial pyramid framework
DGCI'05 Proceedings of the 12th international conference on Discrete Geometry for Computer Imagery
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Combinatorial pyramids represent the image as a stack of successively reduced combinatorial maps, which encode the whole image at different levels of abstraction. Within this framework, this paper proposes to conduct the perceptual organization of the image content in two consecutive stages. The first stage builds the lower set of levels of the hierarchy according to simple face (regions) features (colour and size). On the top of this hierarchy, the second stage will mainly employ boundary features, encoded in the darts of the combinatorial maps, to obtain a second set of levels of abstraction. The Berkeley data set BSDS300 is used to quantitatively compare the performance of the proposal to a number of perceptual grouping approaches, showing that it yields better or similar results than most of these algorithms while offering two interesting features: computation at multiple image resolutions and preservation of the image topology.