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Depth-Based Selective Blurring in Stereo Images Using Accelerated Framework

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).

Graphical Abstract

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Notes

  1. http://vision.middlebury.edu/stereo/submit/.

  2. http://vision.middlebury.edu/stereo/eval/.

  3. http://www.cpubenchmark.net/cpu_list.php.

  4. http://store.sony.com/gsi/webstore/WFS/SNYNA-SNYUS-Site/en_US/-/USD/ViewProduct-Start?SKU=27-HDRTD10.

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Correspondence to Subhayan Mukherjee.

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Mukherjee, S., Guddeti, R.M.R. Depth-Based Selective Blurring in Stereo Images Using Accelerated Framework. 3D Res 5, 14 (2014). https://doi.org/10.1007/s13319-014-0014-7

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  • DOI: https://doi.org/10.1007/s13319-014-0014-7

Keywords

  • Stereo depth estimation
  • Sparse disparity estimates
  • Java Thread Pool
  • APARAPI
  • Morphological filter
  • Connected components analysis