Abstract
In this paper, we propose for stereoscopic images an effective object segmentation approach by incorporating saliency and depth information into graph cut. A saliency model based on color and depth is first used to generate the saliency map. Then the graph cut based on saliency and depth information as well as with the introduction of saliency weighted histogram is proposed to segment salient objects in one cut. Experimental results on a public stereoscopic image dataset with ground truths of salient objects demonstrate that the proposed approach outperforms the state-of-the-art salient object segmentation approaches.
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Fan, X., Liu, Z., Ye, L. (2015). Salient Object Segmentation from Stereoscopic Images. In: Liu, CL., Luo, B., Kropatsch, W., Cheng, J. (eds) Graph-Based Representations in Pattern Recognition. GbRPR 2015. Lecture Notes in Computer Science(), vol 9069. Springer, Cham. https://doi.org/10.1007/978-3-319-18224-7_27
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DOI: https://doi.org/10.1007/978-3-319-18224-7_27
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-18223-0
Online ISBN: 978-3-319-18224-7
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