The Challenges and Advantages with a Parallel Implementation of Feature Matching

Anders Hast, Andrea Marchetti

2016

Abstract

The number of cores per cpu is predicted to double every second year. Therefore, the opportunity to parallelise currently used algorithms in computer vision and image processing needs to be addressed sooner rather than later. A parallel feature matching approach is proposed and evaluated in Matlab􏰂. The key idea is to use different interest point detectors so that each core can work on its own subset independently of the others. However, since the image pairs are the same, the homography will be essentially the same and can therefore be distributed by the process that first finds a solution. Nevertheless, the speedup is not linear and reasons why is discussed.

References

  1. Bay, H., Ess, A., Tuytelaars, T., and Gool, L. V. (2008). Speeded-up robust features (surf). Comput. Vis. Image Underst., 110(3):346-359.
  2. Brown, M. and Lowe, D. G. (2007). Automatic panoramic image stitching using invariant features. International Journal of Computer Vision, 74(1):59-73.
  3. Enqvist, O. and Kahl, F. (2008). Robust optimal pose estimation. In ECCV.
  4. Fijany, A. and Diotalevi, F. (2012). A cooperative search algorithm for highly parallel implementation of ransac for model estimation on tilera mimd architecture. In Aerospace Conference, 2012 IEEE, pages 1-14.
  5. Fischler, M. A. and Bolles, R. C. (1981). Random sample consensus: a paradigm for model fitting with applications to image analysis and automated cartography. Communications of the ACM, 24:381-395.
  6. Friedman, J. H., Bentley, J. L., and Finkel, R. A. (1977). An algorithm for finding best matches in logarithmic expected time. ACM Trans. Math. Softw., 3(3):209- 226.
  7. Fukunage, K. and Narendra, P. M. (1975). A branch and bound algorithm for computing k-nearest neighbors. IEEE Trans. Comput., 24(7):750-753.
  8. Gauglitz, S., Höllerer, T., and Turk, M. (2011). Evaluation of interest point detectors and feature descriptors for visual tracking. Int. J. Comput. Vision, 94(3):335-360.
  9. Harris, C. and Stephens, M. (1988). A combined corner and edge detection. In Alvey Vision Conference, pages 147-151.
  10. Hast, A. (2014). Robust and invariant phase based local feature matching. In ICPR 2014, pages 809-814. Poster with Paper.
  11. Hast, A. (2015). Interest point detection based on the extended structure tensor with a scale space parameter. In VISAPP, pages 1-8. Short Paper.
  12. Hast, A. and Marchetti, A. (2013). Rotation invariant feature matching - based on gaussian filtered log polar transform and phase correlation. In ISPA 2013, pages 100-105.
  13. Hast, A. and Marchetti, A. (2014). Invariant interest point detection based on variations of the spinor tensor. In WSCG 2014, pages 49-56. Short Paper.
  14. Hast, A., Nysjö, J., and Marchetti, A. (2013). Optimal ransac - towards a repeatable algorithm for finding the optimal set. In WSCG, pages 21-30.
  15. Hidalgo-Paniagua, A., Vega-Rodrguez, M., Pavn, N., and Ferruz, J. (2014). A comparative study of parallel ransac implementations in 3d space. International Journal of Parallel Programming, pages 1-18.
  16. Iser, R., Kubus, D., and Wahl, F. M. (2009). An efficient parallel approach to random sample matching (pransam). In Proceedings of the 2009 IEEE International Conference on Robotics and Automation, ICRA'09, pages 655-662, Piscataway, NJ, USA. IEEE Press.
  17. Lowe, D. G. (2004). Distinctive image features from scaleinvariant keypoints. International Journal of Computer Vision, 60(2):91-110.
  18. M. Zuliani, C. K. and Manjunath, B. (2005). The multiransac algorithm and its application to detect planar homographies. In The International Conference on Image Processing (ICIP), volume 3, pages 153-156.
  19. Moravec, H. P. (1980). Obstacle avoidance and navigation in the real world by a seeing robot rover. PhD thesis, Stanford University, Stanford, CA, USA. AAI8024717.
  20. Muja, M. and Lowe, D. G. (2009). Fast approximate nearest neighbors with automatic algorithm configuration. In In VISAPP International Conference on Computer Vision Theory and Applications, pages 331-340.
  21. Noble, J. A. (1989). Descriptions of image surfaces. PhD thesis, Oxford University, St Hugh's College, Oxford, GB. Ph. D. : Engineering sci. : April.
  22. Obdrzálek, S. and Matas, J. (2006). Object recognition using local affine frames on maximally stable extremal regions. In Ponce, J., Hebert, M., Schmid, C., and Zisserman, A., editors, Toward Category-Level Object Recognition, volume 4170 of Lecture Notes in Computer Science, pages 83-104. Springer.
  23. Rodehorst, V. and Hellwich, O. (2006). Genetic algorithm sample consensus (gasac) - a parallel strategy for robust parameter estimation. In IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshop (CVPRW), pages 1-8.
  24. Smith, S. M. and Brady, J. M. (1997). Susan - a new approach to low level image processing. Int. J. Comput. Vision, 23(1):45-78.
  25. Szeliski, R. (2006). Image alignment and stitching: a tutorial. Found. Trends. Comput. Graph. Vis., 2(1):1-104.
  26. Zitova, B. and Flusser, J. (2003). Image registration methods: A survey. Image and Vision Computing, 21:977- 1000.
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Paper Citation


in Harvard Style

Hast A. and Marchetti A. (2016). The Challenges and Advantages with a Parallel Implementation of Feature Matching . In Proceedings of the 11th Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 4: VISAPP, (VISIGRAPP 2016) ISBN 978-989-758-175-5, pages 101-106. DOI: 10.5220/0005674501010106


in Bibtex Style

@conference{visapp16,
author={Anders Hast and Andrea Marchetti},
title={The Challenges and Advantages with a Parallel Implementation of Feature Matching},
booktitle={Proceedings of the 11th Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 4: VISAPP, (VISIGRAPP 2016)},
year={2016},
pages={101-106},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0005674501010106},
isbn={978-989-758-175-5},
}


in EndNote Style

TY - CONF
JO - Proceedings of the 11th Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 4: VISAPP, (VISIGRAPP 2016)
TI - The Challenges and Advantages with a Parallel Implementation of Feature Matching
SN - 978-989-758-175-5
AU - Hast A.
AU - Marchetti A.
PY - 2016
SP - 101
EP - 106
DO - 10.5220/0005674501010106