The Chain Pyramid: Hierarchical Contour Processing
IEEE Transactions on Pattern Analysis and Machine Intelligence
A critical view of pyramid segmentation algorithms
Pattern Recognition Letters
Hierarchical Image Analysis Using Irregular Tessellations
IEEE Transactions on Pattern Analysis and Machine Intelligence
Watersheds in Digital Spaces: An Efficient Algorithm Based on Immersion Simulations
IEEE Transactions on Pattern Analysis and Machine Intelligence
Extracting contours by perceptual grouping
Image and Vision Computing
Detecting image primitives using feature pyramids
Information Sciences: an International Journal
Soft image segmentation by weighted linked pyramid
Pattern Recognition Letters
Shape and topology preserving multi-valued image pyramids for multi-resolution skeletonization
Pattern Recognition Letters
Pattern Recognition Letters
Multiresolution-based watersheds for efficient image segmentation
Pattern Recognition Letters
Morphological Image Analysis: Principles and Applications
Morphological Image Analysis: Principles and Applications
Pyramid segmentation algorithms revisited
Pattern Recognition
Oversegmentation reduction via multiresolution image representation
CIARP'05 Proceedings of the 10th Iberoamerican Congress conference on Progress in Pattern Recognition, Image Analysis and Applications
Dynamic Image Segmentation Method Using Hierarchical Clustering
CIARP '09 Proceedings of the 14th Iberoamerican Conference on Pattern Recognition: Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications
Adaptive multi-scale segmentation of surface data using unsupervised learning of seed positions
Engineering Applications of Artificial Intelligence
Context-aware volume modeling of skeletal muscles
EuroVis'09 Proceedings of the 11th Eurographics / IEEE - VGTC conference on Visualization
An Adaptive Thresholding algorithm of field leaf image
Computers and Electronics in Agriculture
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In this paper we build a shape preserving resolution pyramid and use it in the framework of image segmentation via watershed transformation. Our method is based on the assumption that the most significant image components perceived at high resolution will also be perceived at lower resolution. Thus, we detect the seeds for the watershed transformation at a low resolution, and use them to distinguish significant and non-significant seeds at any selected higher resolution. In this way, the watershed partition obtained at the selected pyramid level will include only the most significant components, and over-segmentation will be considerably reduced. Segmentations of the image at different scales will be available. Moreover, since the seeds can be detected at different pyramid levels, alternative segmentations of the image at a given resolution can be obtained, each characterized by a different level of detail.