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Speeding-up homography estimation in mobile devices

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Abstract

A critical problem faced by computer vision on mobile devices is reducing the computational cost of algorithms and avoiding visual stalls. In this paper, we introduce a procedure for reducing the number of samples required for fitting a homography to a set of noisy correspondences using a random sampling method. This is achieved by means of a geometric constraint that detects invalid minimal sets. In the experiments conducted, we show that this constraint not only reduces the number of random samples at a negligible computational cost but also balances the processor workload over time preventing visual stalls. In extreme situations of very large outlier proportion and noise level, it reduces in about one order of magnitude the number of required random samples.

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  1. http://www.opencv.willowgarage.com.

References

  1. Bay, H., Ess, A., Tuytelaars, T., Van Gool L.: Speeded-up robust features (SURF). Comp. Vision Image Underst. 110(3), 346–359 (2008)

    Google Scholar 

  2. Bruns, E., Bimber, O.: Adaptive training of video sets for image recognition on mobile phones. Pers. Ubiquitous Comput 13:165–178 (2009)

    Google Scholar 

  3. Bruns, E., Brombach, B., Zeidler, T., Bimber, O.: Enabling mobile phones to support large-scale museum guidance. IEEE MultiMedia 14, 16–25 (2007)

    Google Scholar 

  4. Castells, M.: The Rise of the Network Society. Blackwell Publishers, Oxford (1996)

  5. Cheng, C.M., Lai, S.H.: A consensus sampling technique for fast and robust model fitting. Pattern Recognit. 42, 1318–1329 (2009)

    Google Scholar 

  6. Chum, O., Matas, J.: Randomized RANSAC with T d,d test. Proc. Br. Machine Vision Conf. 2, 448–457 (2002)

  7. Chum, O., Matas, J.: Matching with PROSAC -progressive sample consensus. Proc. Int. Conf. Comput. Vision Pattern Recognit. 1, 220–226 (2005)

  8. Chum, O., Werner, T., Matas, J.: Epipolar geometry estimation via RANSAC benefits from the oriented epipolar constraint. Proc. of the Int. Conf. Pattern Recognit., 112–115 (2004)

  9. Fishler, M.A., Bolles, R.C.: Random sample consensus: a paradigm for model fitting with applications to image analysis and automated cartography. Commun. ACM 6, 381–395 (1981)

    Google Scholar 

  10. Guerrero, J., Martínez-Cantin, R., Sagüés, C.: Visual map-less navigation based on homographies. J. Robotic Systems 22 (10):569–581 (2005)

    Google Scholar 

  11. Hartley, R.I., Zisserman, A.: Multiple View Geometry in Computer Vision, 2nd edn. Cambridge University Press, Cambridge (2004)

  12. Henze, N., Schinke, T., Boll, S.: What is that? object recognition from natural features on a mobile phone. In: Mobile Interaction with the Real World (2009)

  13. Kim, K., Lepetit, V., Woo, W.: Scalable real-time planar targets tracking for digilog books. In: Computer Graphics International (2010)

  14. Kirchhof, M.: Linear constraints in two-view multiple homography estimation of uncalibrated scenes. In: The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, vol. XXXVII, pp. B3a (2008)

  15. Klein, G., Murray, D.: Parallel tracking and mapping on a camera phone. In: Proc. IEEE and ACM International Symposium on Mixed and Augmented Reality, Orlando (2009)

  16. Laveau, S., Faugeras, O.D.: Oriented projective geometry for computer vision. In: Proc. Eur. Conf. Computer Vision, pp. 147–156 (1996)

  17. Lee, W., Park, Y., Lepetit, V., Woo, W.: Point-and-shoot for ubiquitous tagging on mobile phones. In: Proc. International Symposium on Mixed and Augmented Reality (2010)

  18. Lopez-Nicolas, G., Gans, N., Bhattacharya, S., Sagues C., Guerrero, J., Hutchinson, S.: Homography-based control scheme for mobile robots with nonholonomic and field-of-view constraints. IEEE Trans. Syst. Man Cybernetics, Part B: Cybernetics 40(4), 1115–1127 (2010a)

  19. Lopez-Nicolas, G., Guerrero, J., Sagues, C.: Multiple homographies with omnidirectional vision for robot homing. Robotics Auton. Syst. 58(6), 773–783 (2010b)

  20. Lowe, D.G.: Distinctive image features from scale-invariant keypoints. Int. J. Comp. Vision 2(60), 91–110

  21. Meer, P., Stewart, C.V., Tyler, D.E.: Robust computer vision. An interdisciplinary challenge. Comput. Vision Image Underst. 78, 1–7 (2000)

  22. Pielot, M., Henze, N., Nickel, C., Menke, C., Samadi, S., Boll, S.: Evaluation of camera phone based interaction to access information related to posters. In: Proceedings of Mobile Interaction with the Real World (2008)

  23. Roblee, E., Rabaud, V., Konolige, K., Bradski, G.: ORB: an efficient alternative to SIFT or SURF. In: Proceedings of the International Conference on Computer Vision, pp 2564–2571 (2011)

  24. Rohs, M., Gfeller, B.: Using camera-equipped mobile phones for interacting with real-world objects. In: Advances in Pervasive Computing, pp 265–271 (2004)

  25. Rousseeuw, P.J.: Least median of squares regression. J. Am. Statist. Assoc. 79, 871–880 (1984)

    Google Scholar 

  26. Takacs, G., Chandrasekhar, V., Gelfand, N., Xiong, Y., Chen, W.C., Bismpigiannis, T., Grzeszczuk, R., Pulli, K., Girod, B.: Outdoors augmented reality on mobile phone using loxel-based visual feature organization. In: Proceedings of the ACM International Conference on Multimedia Information Retrieval, MIR’08, pp. 427–434 (2008)

  27. Taylor, S., Drummond, T.: Multiple target localisation at over 100 fps. In: Proceedings of the British Machine Vision Conference (2009)

  28. Taylor, S., Rosten, E., Drummond, T.: Robust feature matching in 2.3ms. In: CVPR Workshop on Feature Detectors and Descriptors: The State Of The Art and Beyond (2009)

  29. Tell, D., Carlsson, S.: Combining appearance and topology for wide baseline matching. In: Proceedings of the European Conference on Computer Vision, pp. 68–81 (2002)

  30. Tordoff, B.J., Murray, D.W.: Guided-MLESAC faster image transform estimation by using matching priors. IEEE Trans. Pattern Anal. Machine Intel. 27(10), 1523–1535 (2005)

    Google Scholar 

  31. Torr, P., Zisserman, A.: MLESAC A new robust estimator with application to estimating image geometry. Comp. Vision Image Underst. 78, 138–156 (2000)

    Google Scholar 

  32. Torr, P.H.S., Davidson, C.: IMPSAC: Synthesis of importance sampling and random sample consensus. In: Proceedings of European Conference on Computer Vision, pp 819–83 (2000)

  33. Wagner, D., Reitmayr, G., Mulloni, R., Drummond, T., Schmalstieg, D.: Pose tracking from natural features on mobile phones. In: Proc. IEEE and ACM International Symposium on Mixed and Augmented Reality (2008)

  34. Xiong, Y., Pulli, K.: Fast image stitching and editing for panorama painting on mobile phones. In: IEEE International Workshop on Mobile Vision (2010)

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Acknowledgments

The authors gratefully acknowledge funding from Cenit project “mIO!:Tecnologías para prestar servicios en movilidad en el futuro universo inteligente” and the Spanish Ministerio de Ciencia e Innovación under contract TIN2010-19654 and the Consolider Ingenio Program under contract CSD2007-00018 . Pablo Márquez-Neila was funded by the Programa Personal Investigador de Apoyo from the Comunidad de Madrid.

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Authors and Affiliations

  1. Departamento de Inteligencia Artificial, Facultad de Informatica, Universidad Politecnica de Madrid, Campus Montegancedo s/n, 28660, Boadilla del Monte, Spain

    Pablo Márquez-Neila, Javier López-Alberca & Luis Baumela

  2. Departamento de Ciencias de la computacion, Escuela Tecnica Superior de Ingenieria Informatica, Universidad Rey Juan Carlos, C/ Tulipan, s/n, 28933, Mostoles, Spain

    José M. Buenaposada

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  1. Pablo Márquez-Neila
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  2. Javier López-Alberca
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  3. José M. Buenaposada
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  4. Luis Baumela
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Correspondence to Luis Baumela.

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Márquez-Neila, P., López-Alberca, J., Buenaposada, J.M. et al. Speeding-up homography estimation in mobile devices. J Real-Time Image Proc 11, 141–154 (2016). https://doi.org/10.1007/s11554-012-0314-1

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