A Variational Framework for Multiregion Pairwise-Similarity-Based Image Segmentation
| Autor: | Frederic Gibou, Luca Bertelli, Baris Sumengen, B.S. Manjunath |
|---|---|
| Rok vydání: | 2008 |
| Předmět: |
ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION
Scale-space segmentation Image processing Sensitivity and Specificity Pattern Recognition Automated Level set Minimum spanning tree-based segmentation Artificial Intelligence Image Interpretation Computer-Assisted Segmentation Mathematics business.industry Segmentation-based object categorization Applied Mathematics Binary image Reproducibility of Results Signal Processing Computer-Assisted Pattern recognition Image segmentation Image Enhancement Computational Theory and Mathematics Subtraction Technique Computer Science::Computer Vision and Pattern Recognition Computer Vision and Pattern Recognition Artificial intelligence business Algorithms Software |
| Zdroj: | IEEE Transactions on Pattern Analysis and Machine Intelligence. 30:1400-1414 |
| ISSN: | 0162-8828 |
| DOI: | 10.1109/tpami.2007.70785 |
| Popis: | Variational cost functions that are based on pairwise similarity between pixels can be minimized within level set framework resulting in a binary image segmentation. In this paper we extend such cost functions and address multi-region image segmentation problem by employing a multi-phase level set framework. For multi-modal images cost functions become more complicated and relatively difficult to minimize. We extend our previous work, proposed for background/foreground separation, to the segmentation of images in more than two regions. We also demonstrate an efficient implementation of the curve evolution, which reduces the computational time significantly. Finally, we validate the proposed method on the Berkeley Segmentation Data Set by comparing its performance with other segmentation techniques. |
| Databáze: | OpenAIRE |
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