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Rapid learning-based video stereolization using graphic processing unit acceleration

[+] Author Affiliations
Tian Sun, Cheolkon Jung, Lei Wang

Xidian University, School of Electronic Engineering, Xi’an 710071, China

Joongkyu Kim

Sungkyunkwan University, College of Information and Communication Engineering, Suwon 440-746, Republic of Korea

J. Electron. Imaging. 25(5), 053021 (Sep 30, 2016). doi:10.1117/1.JEI.25.5.053021
History: Received January 12, 2016; Accepted August 30, 2016
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Abstract.  Video stereolization has received much attention in recent years due to the lack of stereoscopic three-dimensional (3-D) contents. Although video stereolization can enrich stereoscopic 3-D contents, it is hard to achieve automatic two-dimensional-to-3-D conversion with less computational cost. We proposed rapid learning-based video stereolization using a graphic processing unit (GPU) acceleration. We first generated an initial depth map based on learning from examples. Then, we refined the depth map using saliency and cross-bilateral filtering to make object boundaries clear. Finally, we performed depth-image-based-rendering to generate stereoscopic 3-D views. To accelerate the computation of video stereolization, we provided a parallelizable hybrid GPU–central processing unit (CPU) solution to be suitable for running on GPU. Experimental results demonstrate that the proposed method is nearly 180 times faster than CPU-based processing and achieves a good performance comparable to the-state-of-the-art ones.

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Citation

Tian Sun ; Cheolkon Jung ; Lei Wang and Joongkyu Kim
"Rapid learning-based video stereolization using graphic processing unit acceleration", J. Electron. Imaging. 25(5), 053021 (Sep 30, 2016). ; http://dx.doi.org/10.1117/1.JEI.25.5.053021


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