Stereo matching network based on AANet+ improved attention mechanism

In this study, we construct a smoothing term structure, which is an essential part of the energy function in binocular matching. However, the existing energy models are mainly deterministic, which cannot adapt to processing low-quality images, especially when there exists a large proportion of vague areas. In order to perform better in processing these low-quality images, in this paper, we construct the smoothing term based on a fuzzy model, which includes fuzzy segmentation, the fuzzy network between the superpixels and the fuzzy relationship between the pixels. These can be compatible with the uncertainty in the image. In addition, to explain the rationality of the calculation of the degree of correlation between superpixels and further elaborate on the property of these degrees between each superpixel, we propose five corresponding theorems with proofs. After we solve the energy model combined with our proposed smoothing term, we compare our disparity results with the corresponding deterministic model and several state-of-the-art algorithms in the experiment. The results verify the effectiveness of the proposed algorithm.

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Stereo matching network based on AANet+ improved attention mechanism

Cited by 2 publications

References 3 publications

Towards resource regeneration: A focus on copper recovery from electronic waste

Towards resource regeneration: A focus on copper recovery from electronic waste

A New Fuzzy Smoothing Term Model For Stereo Matching

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