Abstract
We present a novel approach to image segmentation using iterated Graph Cuts based on multi-scale smoothing. We compute the prior probability obtained by the likelihood from a color histogram and a distance transform using the segmentation results from graph cuts in the previous process, and set the probability as the t-link of the graph for the next process. The proposed method can segment the regions of an object with a stepwise process from global to local segmentation by iterating the graph-cuts process with Gaussian smoothing using different values for the standard deviation. We demonstrate that we can obtain 4.7% better segmentation than that with the conventional approach.
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© 2007 Springer-Verlag Berlin Heidelberg
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Nagahashi, T., Fujiyoshi, H., Kanade, T. (2007). Image Segmentation Using Iterated Graph Cuts Based on Multi-scale Smoothing. In: Yagi, Y., Kang, S.B., Kweon, I.S., Zha, H. (eds) Computer Vision – ACCV 2007. ACCV 2007. Lecture Notes in Computer Science, vol 4844. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-76390-1_79
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DOI: https://doi.org/10.1007/978-3-540-76390-1_79
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-76389-5
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