Abstract
This paper presents a simple approach for detecting salient regions in stereo images. The approach computes saliency by considering three factors: disparity influence, central bias and spatial dissimilarity. Firstly, an image is split into equal-sized patches to be down-sampled. Next, disparity influence is estimated based on the disparity map. Besides, central bias value is assigned to every patch and spatial dissimilarity is measured between patches in reduced dimensional space. Thereafter, the product of all factors extracted from the image is computed. Finally, through a process of normalization, the saliency map is obtained. In the experiments four state-of-the-art methods are used for comparison with PSU stereo saliency benchmark dataset (SSB). The experimental results show that our method has better performance than the others for stereo salient region detection.
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Acknowledgments
This research is partially sponsored by National Natural Science Foundation of China [61370113, 61672070, 91546111, 61572004]; and Beijing Municipal Natural Science Foundation [4152005, 4152006, 4162058]; the Science and Technology Program of Tianjin [15YFXQGX0050]; the Science and technology planning project of Qinghai Province [2016-ZJ-Y04]; the Beijing Municipal Education Commission Science and Technology Program [KZ201610005009, KM201610005022, KM201510005015].
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Duan, L., Liang, F., Ma, W., Qiu, S. (2018). Stereo Saliency Analysis Based on Disparity Influence and Spatial Dissimilarity. In: Zeng, B., Huang, Q., El Saddik, A., Li, H., Jiang, S., Fan, X. (eds) Advances in Multimedia Information Processing – PCM 2017. PCM 2017. Lecture Notes in Computer Science(), vol 10736. Springer, Cham. https://doi.org/10.1007/978-3-319-77383-4_25
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