Skip to main content

Depth-Based Selective Blurring in Stereo Images Using Accelerated Framework


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

This is a preview of subscription content, access via your institution.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13







  1. Lazaros, N., Sirakoulis, G., & Gasteratos, A. (2008). Review of stereo vision algorithms: from software to hardware. International Journal of Optomechatronics, 2(4), 435–462.

    Article  Google Scholar 

  2. Xiao, J. Xia, L., Lin, L., & Zhang, Z. (2010). A segment-based stereo matching method with ground control points. International Conference on Environmental Science and Information Application Technology (ESIAT), 2010 (Vol. 3, pp. 306–309). 17–18 July 2010. doi: 10.1109/ESIAT.2010.5568363.

  3. Zhang, Z., Wang, Y., & Dahnoun, N. (2010). A novel algorithm for disparity calculation based on stereo vision. 4th European Education and Research Conference (EDERC), 2010 (pp. 180–184). 1–2 Dec 2010.

  4. Sunyoto, H., Van der Mark, W., & Gavrila, D. M. (2004) A comparative study of fast dense stereo vision algorithms. IEEE Intelligent Vehicles Symposium, 2004 (pp. 319–324). 14–17 June 2004. doi: 10.1109/IVS.2004.1336402.

  5. Tippetts, B., Lee, D., Lillywhite, K., & Archibald, J. (2013). Review of stereo vision algorithms and their suitability for resource-limited systems. Journal of Real-Time Image Processing 1–21. doi:10.1007/s11554-012-0313-2.

  6. Popkin, T., Cavallaro, A. & Hands, D. (2011). Efficient depth blurring with occlusion handling. 18th IEEE International Conference on Image Processing (ICIP), 2011 (pp. 2585–2588). 11–14 Sept 2011. doi: 10.1109/ICIP.2011.6116193.

  7. Hirschmuller, H. & Scharstein, D. (2007). Evaluation of cost functions for stereo matching. IEEE Conference on Computer Vision and Pattern Recognition, 2007. CVPR ‘07. (pp. 1–8) 17–22 June 2007. doi: 10.1109/CVPR.2007.383248.

  8. Tombari, F., Mattoccia, S., Di Stefano, L., & Addimanda, E. (2008). Classification and evaluation of cost aggregation methods for stereo correspondence. IEEE Conference on Computer Vision and Pattern Recognition, 2008. CVPR 2008 (pp. 1–8). 23–28 June 2008. doi: 10.1109/CVPR.2008.4587677.

  9. Abdollahifard, M., Faez, K., & Pourfard, M. (2009). Fast stereo matching using two stage color-based segmentation and dynamic programming. 6th International Symposium on Mechatronics and its Applications, 2009. ISMA ‘09 (pp. 1–6) .23–26 March 2009. doi: 10.1109/ISMA.2009.5164848.

  10. Kim, C. (2005). Segmenting a low-depth-of-field image using morphological filters and region merging. IEEE Transactions on Image Processing, 14(10), 1503–1511. doi:10.1109/TIP.2005.846030.

    Article  Google Scholar 

  11. Wang, X., Song, Y., & Zhang, Y. (2013). Natural Scene Text Detection with Multi-channel Connected Component Segmentation. 12th International Conference on Document Analysis and Recognition (ICDAR), 2013 (pp. 1375–1379). 25–28 Aug 2013. doi: 10.1109/ICDAR.2013.278.

  12. Vishwanath, N., Somasundaram, S., Ravi, M. R. R., & Nallaperumal, N. K. (2012). Connected component analysis for Indian license plate infra-red and color image character segmentation. IEEE International Conference on Computational Intelligence & Computing Research (ICCIC), 2012 (pp. 1–4). 18–20 Dec 2012. doi: 10.1109/ICCIC.2012.6510323.

  13. Zirari, F.; Ennaji, A.; Nicolas, S.; Mammass, D. (2013) “A Document Image Segmentation System Using Analysis of Connected Components. 12th International Conference on Document Analysis and Recognition (ICDAR), 2013 (pp. 753–757) 25–28 Aug 2013. doi: 10.1109/ICDAR.2013.154.

  14. Li, M., Zheng, X., Wan, X., Luo, H., Zhang, S., & Tan, L. (2011). Segmentation of brain tissue based on connected component labeling and mathematic morphology. 4th International Conference on Biomedical Engineering and Informatics (BMEI), 2011, 1, 482–485. doi:10.1109/BMEI.2011.6098294.

    Article  Google Scholar 

  15. Moftah, H. M., ella Hassanien, A. & Shoman, M. (2010). 3D brain tumor segmentation scheme using K-mean clustering and connected component labeling algorithms. 10th International Conference on Intelligent Systems Design and Applications (ISDA), 2010 (pp. 320–324). Nov 29 2010–Dec 1 2010. doi: 10.1109/ISDA.2010.5687244.

  16. Bellala Belahbib, F. Z., & Souami, F. (2012). Color image segmentation by a genetic algorithm based clustering and Connected Component Labeling. 24th International Conference on Microelectronics (ICM), 2012 (pp. 1–4). 16–20 Dec 2012. doi: 10.1109/ICM.2012.6471432.

  17. Choi, K. -S. (2012). Hierarchical block-based disparity estimation. IEEE 1st Global Conference on Consumer Electronics (GCCE), 2012 (pp. 493–494). 2–5 Oct 2012. doi: 10.1109/GCCE.2012.6379668.

  18. Zhu, S., & Yu, Y. (2012). Virtual view rendering based on self-adaptive block matching disparity estimation. International Conference on Industrial Control and Electronics Engineering (ICICEE), 2012 (pp. 947–950). 23–25 Aug 2012. doi: 10.1109/ICICEE.2012.251.

  19. Wang, Z. -F., & Zheng, Z. -G. (2008). A region based stereo matching algorithm using cooperative optimization. IEEE Conference on Computer Vision and Pattern Recognition, 2008. CVPR 2008 (pp. 1–8). 23–28 June 2008. doi: 10.1109/CVPR.2008.4587456.

  20. Lu, D., & Du, Y. (2013). A two-step stereo correspondence algorithm based on combination of feature-matching and region-matching. 8th International Forum on Strategic Technology (IFOST), 2013, 2, 51–55. doi:10.1109/IFOST.2013.6616858.

    Google Scholar 

  21. Tkalcic, M., & Tasic, J. F. (2003). Colour spaces: perceptual, historical and applicational background. EUROCON 2003. Computer as a Tool. The IEEE Region 8 1, 304–308. 10.1109/EURCON.2003.1248032.

  22. Docampo, J., Ramos, S., Taboada, G. L., Exposito, R. R., Tourino, J. & Doallo, R. (2013). Evaluation of Java for general purpose GPU computing. 27th International Conference on Advanced Information Networking and Applications Workshops (WAINA), 2013 (pp. 1398–1404). 25–28 March 2013. doi: 10.1109/WAINA.2013.234.

  23. Kolmogorov, V., & Zabih, R. (2001). Computing visual correspondence with occlusions using graph cuts. Proceedings of the Eighth IEEE International Conference on Computer Vision ICCV 2001, 2, 508–515. doi:10.1109/ICCV.2001.937668.

    Article  Google Scholar 

  24. Miled, W.; Pesquet, J. C. (2006). Disparity map estimation using a total variation bound. The 3rd Canadian Conference on Computer and Robot Vision, 2006. (p 48) 7–9 June 2006. doi: 10.1109/CRV.2006.28.

  25. Scharstein, D., Szeliski, R., & Zabih, R. (2001). A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Proceedings of the IEEE Workshop on Stereo and Multi-Baseline Vision, 2001 (SMBV 2001) (pp. 131–140). doi: 10.1109/SMBV.2001.988771.

Download references

Author information

Authors and Affiliations


Corresponding author

Correspondence to Subhayan Mukherjee.

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Mukherjee, S., Guddeti, R.M.R. Depth-Based Selective Blurring in Stereo Images Using Accelerated Framework. 3D Res 5, 14 (2014).

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • DOI:


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