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MSER-in-Chip: An Efficient Vision Tool for IoT Devices

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The IoT Physical Layer

Abstract

MSERs (maximally stable extremal regions) belong to the most popular local image features with a wide range of highly practical applications, e.g., (to name a few) in image search and retrieval, object detection and recognition, image stitching, tracking mobile objects, etc. Low complexity of MSER detectors (combined with a regular structure suitable for hardware implementation) makes MSER an attractive option for IoT devices which may require vision capabilities to analyze and interpret their environments. In this chapter, we briefly discuss theoretical, algorithmic, and technical aspects of using MSER for such purposes. The presented results have contributed to the system-on-chip implementation of MSER detector which is also overviewed in this chapter. Additionally, we discuss prospective hardware implementations of even more efficient feature detectors based on MSERs.

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References

  1. J. Matas, O. Chum, M. Urban, T. Pajdla, Robust wide baseline stereo from maximally stable extremal regions, in 13th British Machine Vision Conference BMVC’02 (2002), pp. 384–393

    Google Scholar 

  2. D. Nistér, H. Stewénius, Linear time maximally stable extremal regions, in 10th European Conference on Computer Vision ECCV’08, vol.2 (2008), pp. 183–196

    Google Scholar 

  3. F. Kristensen, W. MacLean, Real-time extraction of maximally stable extremal regions on an FPGA, in 2007 IEEE International Symposium on Circuits and Systems ISCAS’07 (2007), pp. 165–168

    Google Scholar 

  4. E. Salahat, H. Saleh, A. Sluzek, M. Al-Qutayri, B. Mohammed, M. Ismail, A maximally stable extremal regions system-on-chip for real-time visual surveillance, in 41st IEEE Industrial Electronics Society Conference IECON’15 (2015), pp. 2812–2815

    Google Scholar 

  5. K. Mikolajczyk, T. Tuytelaars, C. Schmid, A. Zisserman, J. Matas, F. Schaffalitzky, T. Kadir, L. Van Gool, A comparison of affine region detectors. Int. J. Comput. Vis. 65(1), 43–72 (2005)

    Google Scholar 

  6. A. Śluzek, Multi-distinctive MSER features and their Descriptors: a low-complexity tool for image matching, in 2015 International Conference on Advanced Concepts for Intelligent Vision Systems, Lecture Notes in Computer Science, vol. 9396 (2015)

    Google Scholar 

  7. R. Kimmel, C. Zhang, A.M. Bronstein, M.M. Bronstein, Are MSER features really interesting? IEEE Trans. PAMI 33(11), 2316–2320 (2011)

    Google Scholar 

  8. T. Tuytelaars, K. Mikolajczyk, Local invariant feature detector: a survey. Found. Trends Comput. Graphics Vis. 3(3), 177–280 (2007)

    Google Scholar 

  9. R. Szeliski, Image alignment and stitching: a tutorial. Found. Trends Comput. Graphics Vis. 2(1), 1–104 (2006)

    Google Scholar 

  10. M. Donoser, C. Arth, H. Bischof, Detecting, tracking and recognizing license plates, in 8th Asian Conference on Computer Vision ACCV’07, vol. 2 (2007), pp. 447–456

    Google Scholar 

  11. V. Lepetit, P. Fua, Monocular model-based 3D tracking of rigid objects. Found. Trends Comput. Graphics Vis. 1(1), 1–99 (2005)

    Google Scholar 

  12. D. Meger, P.-E. Forssén, K. Lai, S. Helmer, S. McCann, T. Southey, M. Baumann, J.J. Little, D.G. Love, Curious George: an attentive semantic robot. Robot. Auton. Syst. 56(1), 43–72 (2005)

    Google Scholar 

  13. J. Sivic, A. Zisserman, Video data mining using configurations of viewpoint invariant regions, in IEEE Conference on Computer Vision and Pattern Recognition CVPR’04 (2004), pp. I.488–I.495

    Google Scholar 

  14. W. Zhao, Y.-G. Jiang, C.-W. Ngo, Keyframe retrieval by keypoints: can point-to-point matching help? in 5th International Conference CIVR’06 (2006), pp. 72–81

    Google Scholar 

  15. M. Donoser, H. Bischof, M. Wiltsche, Color blob segmentation by MSER analysis, in IEEE International Conference on Image Processing ICIP’06 (2006)

    Google Scholar 

  16. A. Śluzek, Zastosowanie metod momentowych do identyfikacji obiektow w cyfrowych systemach wizyjnych (in Polish) (WPW, Warszawa, 1990)

    Google Scholar 

  17. A. Meijster, M.H. Wilkinson, A comparison of algorithms for connected set openings and closings. IEEE Trans. PAMI 24(4), 484–492 (2002)

    Google Scholar 

  18. E. Salahat, Real-time extraction of maximally stable extremal regions on SoC for visual surveillance, MSc thesis, Khalifa University, ECE Department (2014)

    Google Scholar 

  19. E. Salahat, H. Saleh, A. Sluzek, M. Al-Qutayri, B. Mohammed, M. Ismail, Architecture and method for real-time parallel detection and extraction of maximally stable extremal regions (MSERs). United States Patent no. 9,311,555 (2016)

    Google Scholar 

  20. S. Alyammahi, E. Salahat, H. Saleh, A. Sluzek, A hardware accelerator for real-time extraction of the linear-time MSER algorithm, in 41st IEEE Industrial Electronics Society Conference IECON’15 (2015), pp. 65–69

    Google Scholar 

  21. A. Śluzek, Improving performances of MSER features in matching and retrieval tasks, in 14th European Conference on Computer Vision ECCV’16, Lecture Notes in Computer Science, vol. 9915 (2016)

    Google Scholar 

  22. T. Lindeberg, Scale-space theory: a basic tool for analyzing structures at different scales. J. Appl. Stat. 21(2), 224–270 (1994)

    Google Scholar 

  23. A. Sluzek, H. Saleh, Algorithmic foundations for hardware implementation of scale-insensitive MSER features, in 59th IEEE Interantional Midwest Symposium on Circuits and Systems (MWSCAS’16) (2016)

    Google Scholar 

  24. A. Smeulders, D. Chu, R. Cucchiara, S. Calderara, A. Dehghan, M. Shah, Visual tracking: an experimental survey. IEEE Trans. PAMI 36(7), 1442–1468 (2014)

    Google Scholar 

  25. A. Śluzek, A. Alali, A. Alzaabi, A. Aljasmi, Visual target tracking using a low-cost methodology based on visual words, in 2015 International Conference on Computer Vision and Graphics ICCVG, Lecture Notes in Computer Science, vol. 9971 (2016)

    Google Scholar 

  26. M.A. Fischler, R.C. Bolles, Random sample consensus: a paradigm for model fitting with applications to image analysis and automated cartography, in 4th European Conference on Computer Vision ECCV’06 (1996), pp. 683–695

    Google Scholar 

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Acknowledgements

This work has been supported by the Semiconductor Research Corporation (SRC) under the Abu Dhabi SRC Center of Excellence on Energy-Efficient Electronic Systems (\(ACE^{4}S\)), Contract 2013 HJ2440, with funding from the Mubadala Development Company, Abu Dhabi, UAE.

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Authors and Affiliations

  1. Khalifa University, Abu Dhabi, UAE

    Andrzej Sluzek, Hani Saleh, Baker Mohammad, Mahmoud Al-Qutayri & Mohammed Ismail

  2. Warsaw University of Life Sciences-SGGW, Warsaw, Poland

    Andrzej Sluzek

Authors
  1. Andrzej Sluzek
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  2. Hani Saleh
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  3. Baker Mohammad
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  4. Mahmoud Al-Qutayri
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  5. Mohammed Ismail
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Corresponding author

Correspondence to Andrzej Sluzek .

Editor information

Editors and Affiliations

  1. Masdar Institute at Khalifa University, Abu Dhabi, United Arab Emirates

    Ibrahim (Abe) M. Elfadel

  2. Wayne State University, Detroit, MI, USA

    Mohammed Ismail

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Sluzek, A., Saleh, H., Mohammad, B., Al-Qutayri, M., Ismail, M. (2019). MSER-in-Chip: An Efficient Vision Tool for IoT Devices. In: Elfadel, I., Ismail, M. (eds) The IoT Physical Layer. Springer, Cham. https://doi.org/10.1007/978-3-319-93100-5_14

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  • DOI: https://doi.org/10.1007/978-3-319-93100-5_14

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-93099-2

  • Online ISBN: 978-3-319-93100-5

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