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Table 2 Performance evaluation using different prior terms E P (d) with proposed E D (d). The errors are shown in terms of bad matching pixels and these are computed over the whole image with δ=1

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

E P (d)

Venus

Teddy

Cones

Truncated quadratic

1.95

15.38

11.62

Truncated linear

0.91

12.86

10.96

Potts

1.11

13.93

11.01

E IGMRF(d)

0.40

11.41

9.64

E IGMRF(d) + E sparse(d) using DCT

0.38

11.1

9.36

E IGMRF(d) + E sparse(d) using K-SVD

0.30

10.60

9.12

E IGMRF(d) + E sparse(d) using autoencoder

0.20

9.76

8.46