Watersheds in Digital Spaces: An Efficient Algorithm Based on Immersion Simulations
IEEE Transactions on Pattern Analysis and Machine Intelligence
Modeling Prior Shape and Appearance Knowledge in Watershed Segmentat
CRV '05 Proceedings of the 2nd Canadian conference on Computer and Robot Vision
A Locally Constrained Watershed Transform
IEEE Transactions on Pattern Analysis and Machine Intelligence
A comparative study on multivariate mathematical morphology
Pattern Recognition
Steerable semi-automatic segmentation of textured images
SCIA'05 Proceedings of the 14th Scandinavian conference on Image Analysis
Spatial morphological covariance applied to texture classification
MRCS'06 Proceedings of the 2006 international conference on Multimedia Content Representation, Classification and Security
Automatic image segmentation by positioning a seed
ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part II
Segmentation of complex nucleus configurations in biological images
Pattern Recognition Letters
Towards artistic minimal rendering
NPAR '10 Proceedings of the 8th International Symposium on Non-Photorealistic Animation and Rendering
Pattern Recognition Letters
Artistic minimal rendering with lines and blocks
Graphical Models
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Due to its broad impact in many image analysis applications, the problem of image segmentation has been widely studied. However, there still does not exist any automatic segmentation procedure able to deal accurately with any kind of image. Thus semi-automatic segmentation methods may be seen as an appropriate alternative to solve the segmentation problem. Among these methods, the marker-based watershed has been successfully involved in various domains. In this algorithm, the user may locate the markers, which are used only as the initial starting positions of the regions to be segmented. We propose to base the segmentation process also on the contents of the markers through a supervised pixel classification, thus resulting in a knowledge-based watershed segmentation where the knowledge is built from the markers. Our contribution has been evaluated through some comparative tests with some state-of-the-art methods on the well-known Berkeley Segmentation Dataset.