• Journal of Broadcast Engineering
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• v.23 no.1
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• pp.3-10
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• 2018

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.

• Journal of Digital Convergence
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• v.12 no.12
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• pp.521-528
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• 2014

In this paper, we propose the method of converting 2D image to 3D image using linear curves in HTML5. We use only one image without any other information about depth map for creating 3D images. So we filter the original image to extract RGB colors for left and right eyes. After selecting the ready-made control point of linear curves to set up depth values, users can set up the depth values and modify them. Based on the depth values that the end users select, we reflect them. Anaglyph 3D is automatically made with the whole and partial depth information. As all of this work has been designed and implemented in Web environment using HTML5, it is very easy and convenient and end users can create any 3D image that they want to make.