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. 2017 May 8;17(5):1065. doi: 10.3390/s17051065

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© 2017 by the authors.

Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).

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Examples of 96 filters obtained from 1st convolution layer through training: (a) CVC-14 database; (b) DNHD-DB1; (c) KAIST database; and (d) combined database. In (a–d), the left four images show the 96 filters obtained by training with the original images, whereas the right four images represent those obtained by training with the HE images. In the left and right four images, the left-upper, right-upper, left-lower, and right lower images show the 96 filters obtained by training using 1-, 2-, 3-, and 4-fold cross validation, respectively.

Examples of 96 filters obtained from 1st convolution layer through training: (a) CVC-14 database; (b) DNHD-DB1; (c) KAIST database; and (d) combined database. In (a–d), the left four images show the 96 filters obtained by training with the original images, whereas the right four images represent those obtained by training with the HE images. In the left and right four images, the left-upper, right-upper, left-lower, and right lower images show the 96 filters obtained by training using 1-, 2-, 3-, and 4-fold cross validation, respectively.