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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