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

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Academic year: 2020

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Figure

Fig. 1 A deep deconvolutional network illustrating learning of lth layer
Fig. 2 A sparse autoencoder with n = 3 and K = 4. Here +1 represents a bias unit
Fig. 3 Filters learned at first and second layers of deep deconvolutional network.learned at second layer are 81 where 36 filters in pair are shown in color and remaining 9 filters are shown as a Number of filters learned at first layer are 9
Table 1 Performance evaluation in terms of percentage of badmatching pixels computed over the whole image with δ = 1.Here, the optimization of energy function is carried out usingdifferent data terms ED(d) with IGMRF as prior term EP(d)
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