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Cost Aggregation Strategy with Bilateral Filter Based on Multi-scale Nonlinear Structure Tensor

Author(s): Li Li | Hua Yan

Journal: Journal of Networks
ISSN 1796-2056

Volume: 6;
Issue: 7;
Start page: 958;
Date: 2011;
Original page

Keywords: stereo matching | cost aggregation | multi-scale nonlinear structure tensor | Log-Euclidean tensor distance | bilateral filter

This paper proposed a novel cost aggregation method for stereo matching with modified bilateral filter. In original bilateral filter, only spatial and range weights are used to compute the similarity of two considering pixels and a new weight based on structure tensor is added in our method. By smoothing each element of the structure tensor considering both the spatial and gradient distances of neighboring pixels, the nonlinear structure tensor for each pixel is constructed. We adopt the Log-Euclidean calculus as tensor dissimilarity function to compute the structure tensor distance of two considering pixels. Then the multi-scale value is computed by summing of the tensor distances in each scale. So a new weight based on multi-scale structure tensor distance is set up and included in bilateral filter for cost aggregation. By constructing the multi-scale nonlinear structure tensor and adding the new corresponding weight in cost aggregation, more pixels similar with central pixel could be aggregated in a support window and the final disparity map could be more accurate. Experimental results have confirmed the effectiveness of our proposed method.
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