Shape and topology preserving multi-valued image pyramids for multi-resolution skeletonization
Pattern Recognition Letters
Detecting and ranking foreground regions in gray-level images
BVAI'05 Proceedings of the First international conference on Brain, Vision, and Artificial Intelligence
Using resolution pyramids for watershed image segmentation
Image and Vision Computing
Object detection in watershed partitioned gray-level images
MDA'06/07 Proceedings of the 2007 international conference on Advances in mass data analysis of signals and images in medicine biotechnology and chemistry
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We introduce a method to reduce oversegmentation in watershed partitioned images, that is based on the use of a multiresolution representation of the input image. The underlying idea is that the most significant components perceived in the highest resolution image will remain identifiable also at lower resolution. Thus, starting from the image at the highest resolution, we first obtain a multiresolution representation by building a resolution pyramid. Then, we identify the seeds for watershed segmentation on the lower resolution pyramid levels and suitably use them to identify the significant seeds in the highest resolution image. This is finally partitioned by watershed segmentation, providing a satisfactory result. Since different lower resolution levels can be used to identify the seeds, we obtain alternative segmentations of the highest resolution image, so that the user can select the preferred level of detail.