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Table 1 Performance evaluation in terms of percentage of bad matching pixels computed over the whole image with δ = 1. Here, the optimization of energy function is carried out using different data terms E D (d) with IGMRF as prior term E P (d)

From: A learned sparseness and IGMRF-based regularization framework for dense disparity estimation using unsupervised feature learning

E D (d) Venus Teddy Cones
AD 1.90 16.49 12.14
BT 0.95 15.67 11.89
BT+gradient 0.89 14.9 11.32
E I (d)+E F (d) 0.40 11.41 9.98