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A Hardware Architecture for Calculating LBP-Based Image Region Descriptors

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Proceedings of the 9th International Conference on Computer Recognition Systems CORES 2015

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 403))

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Abstract

In this paper, an efficient hardware architecture, enabling the computation of LBP-based image region descriptors is presented. The complete region descriptor is formed by combining individual local descriptors and arranging them into a grid, as typically used in object detection and recognition. The proposed solution performs massively parallel, pipelined computations, facilitating the processing of over two hundred VGA frames per second, and can easily be adopted to different window and grid sizes for the use of other descriptors.

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References

  1. Ahonen, T., Hadid, A., Pietikainen, M.: Face description with local binary patterns: Application to face recognition. IEEE Trans. Pattern Anal. Mach. Intell. 28(12), 2037–2041 (2006)

    Article  MATH  Google Scholar 

  2. Bay, H., Ess, A., Tuytelaars, T., Gool, L.V.: Speeded-up robust features (SURF). Comput. Vis. Image Underst. 110(3), 346–359 (2008). Similarity Matching in Computer Vision and Multimedia

    Article  Google Scholar 

  3. Bordallo López, M., Nieto, A., Boutellier, J., Hannuksela, J., Silvén, O.: Evaluation of real-time lbp computing in multiple architectures. J. Real-Time Image Process. pp. 1–22 (2014). http://dx.doi.org/10.1007/s11554-014-0410-5

  4. Breiman, L.: Random forests. Mach. Learn. 45(1), 5–32 (2001)

    Article  MathSciNet  MATH  Google Scholar 

  5. Burges, C.J.: A tutorial on support vector machines for pattern recognition. Data Min. Knowl. Discov. 2(2), 121–167 (1998)

    Article  Google Scholar 

  6. Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR 2005, vol. 1, pp. 886–893 (2005)

    Google Scholar 

  7. Fowers, J., Brown, G., Cooke, P., Stitt, G.: A performance and energy comparison of fpgas, gpus, and multicores for sliding-window applications. In: Proceedings of the ACM/SIGDA International Symposium on Field Programmable Gate Arrays, FPGA ’12, pp. 47–56. ACM, New York (2012). http://doi.acm.org/10.1145/2145694.2145704

  8. Fularz, M., Kraft, M., Kasinski, A., Acasandrei, L.: A hybrid system on chip solution for the detection and labeling of moving objects in video streams. In: Signal Processing: Algorithms, Architectures, Arrangements, and Applications (SPA), pp. 94–99 (2013)

    Google Scholar 

  9. Fularz, M., Kraft, M., Schmidt, A., Kasiński, A.: The architecture of an embedded smart camera for intelligent inspection and surveillance. In: Szewczyk, R., Zieliński, C., Kaliczyńska, M. (eds.) Progress in Automation, Robotics and Measuring Techniques, Advances in Intelligent Systems and Computing, vol. 350, pp. 43–52. Springer International Publishing (2015)

    Google Scholar 

  10. Hamdoun, O., Moutarde, F., Stanciulescu, B., Steux, B.: Person re-identification in multi-camera system by signature based on interest point descriptors collected on short video sequences. In: Second ACM/IEEE International Conference on Distributed Smart Cameras, ICDSC 2008, pp. 1–6 (2008)

    Google Scholar 

  11. Kellokumpu, V., Zhao, G., Pietikäinen, M.: Recognition of human actions using texture descriptors. Mach. Vis. Appl. 22(5), 767–780 (2011)

    Article  Google Scholar 

  12. Kryjak, T., Komorkiewicz, M., Gorgon, M.: Fpga implementation of real-time head-shoulder detection using local binary patterns, svm and foreground object detection. In: 2012 Conference on Design and Architectures for Signal and Image Processing (DASIP), pp. 1–8 (2012)

    Google Scholar 

  13. Lienhart, R., Maydt, J.: An extended set of haar-like features for rapid object detection. In: Proceedings of International Conference on Image Processing, vol. 1, pp. I–900–I–903 (2002)

    Google Scholar 

  14. Nguyen, D.T., Zong, Z., Ogunbona, P., Li, W.: Object detection using non-redundant local binary patterns. In: 17th IEEE International Conference on Image Processing (ICIP), pp. 4609–4612 (2010)

    Google Scholar 

  15. Nguyen, D.T., Ogunbona, P.O., Li, W.: A novel shape-based non-redundant local binary pattern descriptor for object detection. Pattern Recognit. 46(5), 1485–1500 (2013)

    Article  Google Scholar 

  16. Ojala, T., Pietikainen, M., Maenpaa, T.: Multiresolution gray-scale and rotation invariant texture classification with local binary patterns. IEEE Trans. Pattern Anal. Mach. Intell. 24(7), 971–987 (2002)

    Article  MATH  Google Scholar 

  17. Pavani, S.K., Delgado, D., Frangi, A.F.: Haar-like features with optimally weighted rectangles for rapid object detection. Pattern Recognit. 43(1), 160–172 (2010)

    Article  MATH  Google Scholar 

  18. Pedersoli, M., Vedaldi, A., Gonzàlez, J., Roca, X.: A coarse-to-fine approach for fast deformable object detection. Pattern Recognit. 48(5), 1844–1853 (2015)

    Article  Google Scholar 

  19. Schapire, R.: The boosting approach to machine learning: An overview. In: Denison, D., Hansen, M., Holmes, C., Mallick, B., Yu, B. (eds.) Nonlinear Estimation and Classification, Lecture Notes in Statistics, vol. 171, pp. 149–171. Springer New York (2003)

    Google Scholar 

  20. Sun, H., Wang, C., Wang, B., El-Sheimy, N.: Pyramid binary pattern features for real-time pedestrian detection from infrared videos. Neurocomputing 74(5), 797–804 (2011)

    Article  Google Scholar 

  21. Zhang, H., Gao, W., Chen, X., Zhao, D.: Object detection using spatial histogram features. Image Vis. Comput. 24(4), 327–341 (2006)

    Article  Google Scholar 

  22. Zheng, Y., Shen, C., Hartley, R., Huang, X.: Pyramid center-symmetric local binary/trinary patterns for effective pedestrian detection. In: Kimmel, R., Klette, R., Sugimoto, A. (eds.) Computer Vision - ACCV 2010, Lecture Notes in Computer Science, vol. 6495, pp. 281–292. Springer, Berlin (2011)

    Google Scholar 

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Acknowledgments

This research was financed by the Polish National Science Centre grant funded according to the decision DEC-2011/03/N/ST6/03022, which is gratefully acknowledged.

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Authors and Affiliations

  1. Institute of Control and Information Engineering, Poznan University of Technology, Piotrowo 3A, 60-965, Poznan, Poland

    Marek Kraft & Michał Fularz

Authors
  1. Marek Kraft
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  2. Michał Fularz
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Corresponding author

Correspondence to Marek Kraft .

Editor information

Editors and Affiliations

  1. Department of Systems, Wrocław University of Technology, Wroclaw, Poland

    Robert Burduk

  2. Department of Systems and Computer, Wrocław University of Technology, Wroclaw, Poland

    Konrad Jackowski

  3. Department of Systems and Computer, Wrocław University of Technology, Wroclaw, Poland

    Marek Kurzyński

  4. Dept. of Systems and Computer Networks, Wrocław University of Technology, Wroclaw, Poland

    Michał Woźniak

  5. Department of Systems, Wrocław University of Technology, Wroclaw, Poland

    Andrzej Żołnierek

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Kraft, M., Fularz, M. (2016). A Hardware Architecture for Calculating LBP-Based Image Region Descriptors. In: Burduk, R., Jackowski, K., Kurzyński, M., Woźniak, M., Żołnierek, A. (eds) Proceedings of the 9th International Conference on Computer Recognition Systems CORES 2015. Advances in Intelligent Systems and Computing, vol 403. Springer, Cham. https://doi.org/10.1007/978-3-319-26227-7_63

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  • DOI: https://doi.org/10.1007/978-3-319-26227-7_63

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-26225-3

  • Online ISBN: 978-3-319-26227-7

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