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A Multiscale Particle Filter and Winding Number Constrained for Contour Detection

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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Year of Publication: 2015
Authors:
Sonam Verma, Achint Chugh
10.5120/ijca2015907642

Sonam Verma and Achint Chugh. Article: A Multiscale Particle Filter and Winding Number Constrained for Contour Detection. International Journal of Computer Applications 131(17):17-23, December 2015. Published by Foundation of Computer Science (FCS), NY, USA. BibTeX

@article{key:article,
	author = {Sonam Verma and Achint Chugh},
	title = {Article: A Multiscale Particle Filter and Winding Number Constrained for Contour Detection},
	journal = {International Journal of Computer Applications},
	year = {2015},
	volume = {131},
	number = {17},
	pages = {17-23},
	month = {December},
	note = {Published by Foundation of Computer Science (FCS), NY, USA}
}

Abstract

This paper compares the basic contour detection algorithms. A contour detection algorithm which jointly tracks at two scales small pieces of edges called edgelets. This multiscaleedgelet structure naturally embeds semi-local information and is the basic element of the recursive Bayesian modeling. The underlying model is estimated using a sequential Monte Carlo approach, and the soft contour detection map is retrieved from the approximated trajectory distribution. The winding number constrained contour detection (WNCCD) is an energy minimization framework based on winding number constraints. In this framework, both region cues, such as color/texture homogeneity, and contour cues, such as local contrast and continuity, are represented in a joint objective function, which has both region and contour labels. This technique is based on the topological concept of winding number. Using a fast method for winding number computation, a small number of linear constraints are derived to ensure label consistency. Experiments conducted on the Berkeley Segmentation data sets show that the Multi Scale Particle Filter Contour Detector method performs a comparable result with the winding number constrained contour detection method.

References

  1. B. Wu and R. Nevatia, “Detection of Multiple, Partially Occluded Humans in a Single Image by Bayesian Combination of Edgelet Part Detectors” Proc. IEEE 10th Int’l Conf. Computer Vision (ICCV), vol. 1, pp. 90-97, 2005.
  2. N. Widynski and M. Mignotte, “A Contrario Edge Detection with Edgelets,” Proc. IEEE Int’l Conf. Signal and Image Processing Applications (ICSIPA), pp. 421-426, 2011.
  3. Nicolas Widynski and Max Mignotte “A MultiScale Particle Filter Framework for Contour Detection” IEEE Transactions on Pattern Analysis And Machine Intelligence, Vol. 36, No. 10, October 2014.
  4. P. Perez, A. Blake, and M. Gangnet, “Jetstream: Probabilistic Contour Extraction with Particles,” Proc. IEEE Eighth Int’l Conf. Computer Vision (ICCV), vol. 2, pp. 524-531, 2001.
  5. C. Florin, N. Paragios, and J. Williams, “Globally Optimal Active Contours, Sequential Monte Carlo and On-Line Learning for Vessel Segmentation,” Proc. Ninth European Conf. Computer Vision (ECCV), pp. 476-489, 2006.
  6. D. Lesage, E.D. Angelini, I. Bloch, and G. Funka-Lea, “Medial- Based Bayesian Tracking for Vascular Segmentation: Application to Coronary Arteries in 3D CT Angiography,” Proc. IEEE Fifth Int’l Symp. Biomedical Imaging: From Nano to Micro (ISBI), pp. 268-271, 2008.
  7. N. Widynski and M. Mignotte, “A Particle Filter Framework for Contour Detection,” Proc. 12th European Conf. Computer Vision (ECCV), pp. 780-794, 2012.
  8. Y. Ming, H. Li, and X. He, “Winding Number Constrained Contour Detection” IEEE Transactions on Image Processing, Vol. 24, No. 1, January 2015.
  9. P. Arbelaez, M. Maire, C. Fowlkes, and J. Malik, “Contour Detection and Hierarchical Image Segmentation,” IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 33, no. 5, pp. 898-916, May 2011.
  10. X. Ren, “Multi-Scale Improves Boundary Detection in Natural Images,” Proc. 10th European Conf. Computer Vision (ECCV), pp. 533-545, Jan. 2008.
  11. M. Maire, P. Arbel_aez, C. Fowlkes, and J. Malik, “Using Contours to Detect and Localize Junctions in Natural Images,” Proc. IEEE Conf. Computer Vision and Pattern Recognition (CVPR), pp. 1-8, 2008.
  12. N. Dalal and B. Triggs, “Histograms of Oriented Gradients for Human Detection,” Proc. IEEE Conf. Computer Vision and Pattern Recognition (CVPR), vol. 1, pp. 886-893, 2005.
  13. J. Sun, Z. Xu, and H.-Y. Shum, “Gradient Profile Prior and Its Applications in Image Super-Resolution and Enhancement,” IEEE Trans. Image Processing, vol. 20, no. 6, pp. 1529-1542, June 2011.
  14. Sequential Monte Carlo Methods in Practice, A. Doucet, N. De Freitas, and N. Gordon, eds., Springer, 2001.
  15. J. MacCormick and M. Isard, “Partitioned Sampling, Articulated Objects, and Interface-Quality Hand Tracking,” Proc. Sixth European Conf. Computer Vision (ECCV), pp. 3-19, 2000.
  16. J. MacCormick and A. Blake, “A Probabilistic Exclusion Principle for Tracking Multiple Objects,” Int’l J. Computer Vision, vol. 39, no. 1, pp. 57-71, 2000.
  17. Z. Chen, “Bayesian Filtering: From Kalman Filters to Particle Filters, and Beyond,” technical report, McMaster Univ., 2003..
  18. J. Canny, “A Computational Approach to Edge Detection,” IEEE Trans. Pattern Analysis and Machine Intelligence, vol. PAMI-8, no. 6, pp. 679-698, Nov. 1986.
  19. B. Andres, J. H. Kappes, T. Beier, U. Köthe, and F. A. Hamprecht, “Probabilistic image segmentation with closedness constraints,” in Proc. IEEE ICCV, Nov. 2011, pp. 2611–2618.
  20. J. H. Elder and S. W. Zucker, “Computing contour closure,” in Proc. 4th ECCV, 1996, pp. 399–412.
  21. J. Gallier and D. Xu, “The fundamental group, orientability,” in A Guide to the Classification Theorem for Compact Surfaces (Geometry and Computing), vol. 9. New York, NY, USA: Springer-Verlag, 2013.
  22. A. Jacobson, L. Kavan, and O. Sorkine-Hornung, “Robust insideoutside segmentation using generalized winding numbers,” in Proc. ACM SIGGRAPH, 2013, vol. 32, no. 4, p. 33.
  23. I. Kovács and B. Julesz, “A closed curve is much more than an incomplete one: Effect of closure in figure-ground segmentation,” Proc. Nat. Acad. Sci. USA, vol. 90, no. 16, pp. 7495–7497, 1993.
  24. M. McIntyre and G. Cairns, “A new formula for winding number,” GeometriaeDedicata, vol. 46, no. 2, pp. 149–159, 1993.
  25. T. Needham, Visual Complex Analysis. New York, NY, USA: Oxford Univ. Press, Feb. 1999.
  26. T. Schoenemann, F. Kahl, S. Masnou, and D. Cremers, “A linear framework for region-based image segmentation and inpainting involving curvature penalization,” Int. J. Comput. Vis., vol. 99, no. 1, pp. 53–68, 2012.
  27. H. Whitney, “On regular closed curves in the plane,” Compos. Math., vol. 4, pp. 276–284, 1937.
  28. A. Doucet, M. Briers, and S. S_en_ecal, “Efficient Block Sampling Strategies for Sequential Monte Carlo Methods,” J. Computational and Graphical Statistics, vol. 15, no. 3, pp. 693-711, 2006.

Keywords

Particle filtering, sequential Monte Carlo methods, statistical model, multiscale contour detection, BSDS