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• W2560334015 abstract "In this paper, a novel dense stereo matching algorithm is proposed based on edge-aware truncated minimum spanning tree (T-MST). Instead of employing non-local cost aggregation on traditional MST which is only generated from color difference of neighbouring pixels, a new tree structure, Edge-Aware T-MST, is proposed to aggregate the cost according to the image texture. Specifically, cost aggregation is promoted among highly textured region. Meanwhile, the edge fatten effect is restrained by combining edge-prior and superpixel-prior together to locate the true disparity edge. Then a widely used Winner-Take-All (WTA) strategy is performed to establish initial disparity map. An adaptive non-local refinement is also performed based on the stability of initial stereo estimation. Experimental results on Middlebury data set show that our algorithm outperforms the current state-of-the-art non-local MST-based stereo matching algorithms." @default.
• W2560334015 created "2016-12-16" @default.
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• W2560334015 date "2016-12-01" @default.
• W2560334015 modified "2023-10-02" @default.
• W2560334015 title "Stereo Matching Based on Edge-Aware T-MST" @default.
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• W2560334015 doi "https://doi.org/10.1109/ithings-greencom-cpscom-smartdata.2016.168" @default.
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