Abstract
This paper addresses the problem of interactive multi-label segmentation. We propose a powerful new framework using several color models and texture descriptors, Random Forest likelihood estimation as well as a multi-label Potts-model segmentation. We perform most of the calculations on the GPU and reach runtimes of less than two seconds, allowing for convenient user interaction. Due to the lack of an interactive multi-label segmentation benchmark, we also introduce a large publicly available dataset. We demonstrate the quality of our framework with many examples and experiments using this benchmark dataset.
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Santner, J., Pock, T., Bischof, H. (2011). Interactive Multi-label Segmentation. In: Kimmel, R., Klette, R., Sugimoto, A. (eds) Computer Vision – ACCV 2010. ACCV 2010. Lecture Notes in Computer Science, vol 6492. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-19315-6_31
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DOI: https://doi.org/10.1007/978-3-642-19315-6_31
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-19314-9
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