Abstract
Due to the low signal to noise ratio, saliency detection in low contrast images has been a great challenge in computer vision. In this paper we propose a novel approach to detect salient object based on the computation of global saliencies in superpixel image blocks. This method tackles the image through a simple contrast measure, which first computes the global difference of two superpixels to obtain the resulting saliency map. Then, the map is refined by introducing the inter-superpixel similarity approach. The proposed model perfectly extracts the salient object in low contrast visibility conditions, which has been tested on three public datasets, as well as a nighttime image dataset. Experimental results demonstrate that the proposed method outperforms existing state-of-the-art saliency detection models.
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Mu, N., Xu, X. (2015). Superpixel-Based Global Contrast Driven Saliency Detection in Low Contrast Images. In: Zha, H., Chen, X., Wang, L., Miao, Q. (eds) Computer Vision. CCCV 2015. Communications in Computer and Information Science, vol 546. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-48558-3_41
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DOI: https://doi.org/10.1007/978-3-662-48558-3_41
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