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DC Field | Value | Language |
---|---|---|
dc.contributor.author | Mukherjee, S. | - |
dc.contributor.author | Ram Mohana Reddy, Guddeti | - |
dc.date.accessioned | 2020-03-31T08:23:11Z | - |
dc.date.available | 2020-03-31T08:23:11Z | - |
dc.date.issued | 2014 | - |
dc.identifier.citation | 3D Research, 2014, Vol.5, 3, pp.1-21 | en_US |
dc.identifier.uri | http://idr.nitk.ac.in/jspui/handle/123456789/10846 | - |
dc.description.abstract | Abstract: We propose a hybrid method for stereo disparity estimation by combining block and region-based stereo matching approaches. It generates dense depth maps from disparity measurements of only 18 % image pixels (left or right). The methodology involves segmenting pixel lightness values using fast K-Means implementation, refining segment boundaries using morphological filtering and connected components analysis; then determining boundaries disparities using sum of absolute differences (SAD) cost function. Complete disparity maps are reconstructed from boundaries disparities. We consider an application of our method for depth-based selective blurring of non-interest regions of stereo images, using Gaussian blur to de-focus users non-interest regions. Experiments on Middlebury dataset demonstrate that our method outperforms traditional disparity estimation approaches using SAD and normalized cross correlation by up to 33.6 % and some recent methods by up to 6.1 %. Further, our method is highly parallelizable using CPU GPU framework based on Java Thread Pool and APARAPI with speed-up of 5.8 for 250 stereo video frames (4,096 2,304). 2014, 3D Research Center, Kwangwoon University and Springer-Verlag Berlin Heidelberg. | en_US |
dc.title | Depth-Based Selective Blurring in Stereo Images Using Accelerated Framework | en_US |
dc.type | Article | en_US |
Appears in Collections: | 1. Journal Articles |
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