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Mobile Image Analysis: Android vs. iOS

  • Claudiu Cobârzan
  • Marco A. Hudelist
  • Klaus Schoeffmann
  • Manfred Jürgen Primus
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8936)

Abstract

Currently, computer vision applications are becoming more common on mobile devices due to the constant increase in raw processing power coupled with extended battery life. The OpenCV framework is a popular choice when developing such applications on desktop computers as well as on mobile devices, but there are few comparative performance studies available. We know of only one such study that evaluates a set of typical OpenCV operations on iOS devices. In this paper we look at the same operations, spanning from simple image manipulation like grayscaling and blurring to keypoint detection and descriptor extraction but on flagship Android devices as well as on iOS devices and with different image resolutions. We compare the results of the same tests running on the two platforms on the same datasets and provide extended measurements on completion time and battery usage.

Keywords

mobile devices OpenCV performance evaluation Android iOS 

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References

  1. 1.
    Alahi, A., Ortiz, R., Vandergheynst, P.: Freak: Fast retina keypoint. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 510–517 (2012)Google Scholar
  2. 2.
    Bay, H., Tuytelaars, T., Van Gool, L.: SURF: Speeded up robust features. In: Leonardis, A., Bischof, H., Pinz, A. (eds.) ECCV 2006, Part I. LNCS, vol. 3951, pp. 404–417. Springer, Heidelberg (2006)CrossRefGoogle Scholar
  3. 3.
    Calonder, M., Lepetit, V., Strecha, C., Fua, P.: BRIEF: Binary robust independent elementary features. In: Daniilidis, K., Maragos, P., Paragios, N. (eds.) ECCV 2010, Part IV. LNCS, vol. 6314, pp. 778–792. Springer, Heidelberg (2010)CrossRefGoogle Scholar
  4. 4.
    Chatzilari, E., Liaros, G., Nikolopoulos, S., Kompatsiaris, Y.: A comparative study on mobile visual recognition. In: Perner, P. (ed.) MLDM 2013. LNCS, vol. 7988, pp. 442–457. Springer, Heidelberg (2013)CrossRefGoogle Scholar
  5. 5.
    Cobârzan, C., Hudelist, M.A., Del Fabro, M.: Content-based video browsing with collaborating mobile clients. In: Gurrin, C., Hopfgartner, F., Hurst, W., Johansen, H., Lee, H., O’Connor, N. (eds.) MMM 2014, Part II. LNCS, vol. 8326, pp. 402–406. Springer, Heidelberg (2014)CrossRefGoogle Scholar
  6. 6.
    Girod, B., Chandrasekhar, V., Chen, D.M., Cheung, N.-M., Grzeszczuk, R., Reznik, Y.A., Takacs, G., Tsai, S.S., Vedantham, R.: Mobile visual search. IEEE Signal Processing Magazine (2011)Google Scholar
  7. 7.
    Hudelist, M.A., Cobârzan, C., Schoeffmann, K.: Opencv performance measurements on mobile devices. In: Proc. of Int. Conf. on Multimedia Retrieval ICMR 2014, Glasgow, United Kingdom, April 01-04, pp. 479–482 (2014)Google Scholar
  8. 8.
    Leutenegger, S., Chli, M., Siegwart, R.: Brisk: Binary robust invariant scalable keypoints. In: IEEE Int. Conf. on Computer Vision (ICCV), pp. 2548–2555 (2011)Google Scholar
  9. 9.
    Lowe, D.: Distinctive image features from scale-invariant keypoints. International Journal of Computer Vision 60(2), 91–110 (2004)CrossRefGoogle Scholar
  10. 10.
    Rosten, E., Drummond, T.: Machine learning for high-speed corner detection. In: Leonardis, A., Bischof, H., Pinz, A. (eds.) ECCV 2006, Part I. LNCS, vol. 3951, pp. 430–443. Springer, Heidelberg (2006)CrossRefGoogle Scholar
  11. 11.
    Rublee, E., Rabaud, V., Konolige, K., Bradski, G.: Orb: An efficient alternative to sift or surf. In: IEEE International Conference on Computer Vision (ICCV), pp. 2564–2571 (2011)Google Scholar
  12. 12.
    Schoeffmann, K., Ahlström, D., Bailer, W., Cobârzan, C., Hopfgartner, F., McGuinness, K., Gurrin, C., Frisson, C., Le, D.-D., Del Fabro, M., Bai, H., Weiss, W.: The video browser showdown: A live evaluation of interactive video search tools. International Journal of Multimedia Information Retrieval (2014)Google Scholar
  13. 13.
    Shi, J., Tomasi, C.: Good features to track. In: IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 593–600 (1994)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Claudiu Cobârzan
    • 1
  • Marco A. Hudelist
    • 1
  • Klaus Schoeffmann
    • 1
  • Manfred Jürgen Primus
    • 1
  1. 1.Alpen-Adria-Universität KlagenfurtKlagenfurtAustria

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