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Real-time, parallel segmentation of high-resolution images on multi-core platforms

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Abstract

Many high-level vision applications apply image segmentation during preprocessing to improve their overall execution efficiency. The applied segmentation technique must, therefore, be highly efficient, or otherwise would counteract any system performance improvement. Existing segmentation approaches often fail to meet real-time processing standards and exhibit extremely slow frame rates when applied to high-resolution images. This paper presents a highly optimized serial implementation of a novel image segmentation approach known as leap segmentation, which achieves frame rates exceeding that of the state-of-the art: it segments more than 80 fps on 640 × 360 images and more than 20 fps on high-resolution (1,280 × 720) images. (All images used for evaluation in this paper use a 24-bit red-green-blue color representation with 8 bits per color). We analyze leap segmentation further for areas of possible parallelization and restructure it for use on a parallel processing system to achieve additional speed-up. On a multi-core, mobile processing system with four threads, our multi-core leap segmentation implementation achieves frame rates of over 114 fps on 640 × 360 images and more than 31 fps on 1,280 × 720 images, thus easily exceeding real-time processing standards.

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References

  1. Abramov, A., Kulvicius, T., Wörgötter, F., Dellen, B.: Real-time image segmentation on a GPU. In: Proc. Facing the Multicore-Challenge, pp. 131–142 (2011)

  2. Carreira-Perpiñán, M.: Acceleration strategies for gaussian mean-shift image segmentation. In: Proc. IEEE Int. Conf. Comp. Vision Pattern Recog., vol. 1, pp. 1160–1167 (2006)

  3. Christoudias, C., Georgescu, B., Meer, P.: Synergism in low level vision. In: Proc. 16th Int. Conf. Pattern Recog., vol. 4, pp. 150–155. Quebec City, Canada (2002)

  4. Comaniciu, D., Meer, P.: Mean shift: a robust approach toward feature space analysis. IEEE Trans. Pattern Anal. Mach. Intell. 24(5), 603–619 (2002)

    Article  Google Scholar 

  5. Dempster, A., Laird, N., Rubin, D.: Maximum likelihood from incomplete data via the EM algorithm. J. Royal Stat. Soc. Ser B 39(1), 1–38 (1977)

    MATH  MathSciNet  Google Scholar 

  6. Drucker, F., MacCormick, J.: Fast superpixels for video analysis. In: Proc. IEEE Wksp Motion Video Comput., pp. 1–8 (2009)

  7. Felzenszwalb, P., Huttenlocher, D.: Efficient graph-based image segmentation. Int. J. Comp. Vision 59(2), 167–181 (2004)

    Article  Google Scholar 

  8. Forsthoefel, D., Wills, D., Wills, L.: Leap segmentation for recovering image surface layout. In: Proc. IEEE Southwest Symp. Image Anal. Interp. Santa Fe, NM (2012)

  9. Forsthoefel, D., Wills, D., Wills, L.: Unsupervised video leap segmentation for fast detection of salient segment transformations in mobile sequences. In: Proc. 15th International IEEE Intelligent Transportation Systems Conference (ITSC). Anchorage, AK (2012)

  10. Happ, P., Ferreira, R., Bentes, C., Costa, G., Feitosa, R.: Multiresolution segmentation: a parallel approach for high resolution image segmentation in multicore architectures. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences 38(4) (2010)

  11. Martin, D., Fowlkes, C.: The berkeley segmentation dataset and benchmark. Available from http://www.cs.berkeley.edu/projects/vision/grouping/segbench/ (2007)

  12. Martin, D., Fowlkes, C., Tal, D., Malik, J.: A database of human segmented natural images and its application to evaluating segmentation algorithms and measuring ecological statistics. In: Proc. 8th Int. Conf. Comp. Vision, vol. 2, pp. 416–423 (2001)

  13. Meribout, M., Nakanishi, M.: A new real time object segmentation and tracking algorithm and its parallel hardware architecture. J. VLSI Signal Process. 39(3), 249–266 (2005)

    Article  Google Scholar 

  14. OpenMP application program interface version 3.0. Available from http://www.openmp.org/mp-documents/spec30 (2008). OpenMP Architecture Review Board

  15. Pantofaru, C., Hebert, M.: A comparison of image segmentation algorithms. Technical Report CMU-RI-TR-05-40, The Robotics Institute, Carnegie Mellon University (2005)

  16. Paris, S., Durand, F.: A topological approach to hierarchical segmentation using mean shift. In: Proc. IEEE Conf. Comp. Vision Pattern Recog., pp. 1–8 (2007)

  17. Shi, J., Malik, J.: Normalized cuts and image segmentation. IEEE Trans. Pattern Anal. Mach. Intell. 22(8), 888–905 (2000)

    Article  Google Scholar 

  18. Szeliski, R.: Computer Vision: Algorithms and Applications. 1st edn. Springer, Heidelberg (2010)

    MATH  Google Scholar 

  19. Vincent, L., Soille, P.: Watersheds in digital spaces: an efficient algorithm based on immersion simulations. IEEE Trans. Pattern Anal. Mach. Intell. 13(6), 583–598 (1991)

    Article  Google Scholar 

  20. Wang, J., Bhat, P., Colburn, R., Agrawala, M., Cohen, M.: Interactive video cutout. ACM Trans. Graphics 24(3), 585–594 (2005)

    Article  Google Scholar 

  21. Yilmaz, A., Javed, O., Shah, M.: Object tracking: a survey. ACM Comput. Surv. 38(4), 1–45 (2006)

    Article  Google Scholar 

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Acknowledgments

The authors wish to thank M. Ryan Bales, Shoaib Azmat, and Qianao Ju for their helpful discussions and insights.

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Authors and Affiliations

  1. Georgia Institute of Technology, Atlanta, GA, USA

    Dana Forsthoefel Fitzgerald, D. Scott Wills & Linda M. Wills

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  1. Dana Forsthoefel Fitzgerald
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  2. D. Scott Wills
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  3. Linda M. Wills
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Correspondence to Dana Forsthoefel Fitzgerald.

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Fitzgerald, D.F., Wills, D.S. & Wills, L.M. Real-time, parallel segmentation of high-resolution images on multi-core platforms. J Real-Time Image Proc 13, 685–702 (2017). https://doi.org/10.1007/s11554-014-0432-z

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  • DOI: https://doi.org/10.1007/s11554-014-0432-z

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