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JBE, vol. 23, no. 1, pp.3-10, January, 2018

DOI: https://doi.org/10.5909/JBE.2018.23.1.3

Implementing a Depth Map Generation Algorithm by Convolutional Neural Network

Seungsoo Lee, Hong Jin Kim, and Manbae Kim

C.A E-mail: manbae@kangwon.ac.kr

Abstract:

Depth map has been utilized in a varity of fields. Recently research on generating depth map by artificial neural network (ANN) has gained much interest. This paper validates the feasibility of implementing the ready-made depth map generation by convolutional neural network (CNN). First, for a given image, a depth map is generated by the weighted average of a saliency map as well as a motion history image. Then CNN network is trained by test images and depth maps. The objective and subjective experiments are performed on the CNN and showed that the CNN can replace the ready-made depth generation method.

 



Keyword: Depth map, CNN, Saliency map, Motion Hitsory Image, Ready-made depth map

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