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Journal of Optimization
Volume 2013, Article ID 345287, 15 pages
http://dx.doi.org/10.1155/2013/345287
Research Article

PRO: A Novel Approach to Precision and Reliability Optimization Based Dominant Point Detection

School of Computing, National University of Singapore, Singapore 117417

Received 5 June 2013; Revised 21 July 2013; Accepted 2 August 2013

Academic Editor: Manuel Lozano

Copyright © 2013 Dilip K. Prasad. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Linked References

  1. R. Yang and Z. Zhang, “Eye gaze correction with stereovision for video-teleconferencing,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 26, no. 7, pp. 956–960, 2004. View at Publisher · View at Google Scholar · View at Scopus
  2. A. Kolesnikov and P. Fränti, “Data reduction of large vector graphics,” Pattern Recognition, vol. 38, no. 3, pp. 381–394, 2005. View at Publisher · View at Google Scholar · View at Scopus
  3. D. Brunner and P. Soille, “Iterative area filtering of multichannel images,” Image and Vision Computing, vol. 25, no. 8, pp. 1352–1364, 2007. View at Publisher · View at Google Scholar · View at Scopus
  4. S. Ozen, A. Bouganis, and M. Shanahan, “A fast evaluation criterion for the recognition of occluded shapes,” Robotics and Autonomous Systems, vol. 55, no. 9, pp. 741–749, 2007. View at Publisher · View at Google Scholar · View at Scopus
  5. A. Orzan, A. Bousseau, H. Winnemöller, P. Barla, J. Thollot, and D. Salesin, “Diffusion curves: a vector representation for smooth-shaded images,” ACM Transactions on Graphics, vol. 27, no. 3, article 92, 2008. View at Publisher · View at Google Scholar · View at Scopus
  6. J. L. G. Balboa and F. J. A. López, “Sinuosity pattern recognition of road features for segmentation purposes in cartographic generalization,” Pattern Recognition, vol. 42, no. 9, pp. 2150–2159, 2009. View at Publisher · View at Google Scholar · View at Scopus
  7. G. Erus and N. Loménie, “How to involve structural modeling for cartographic object recognition tasks in high-resolution satellite images?” Pattern Recognition Letters, vol. 31, no. 10, pp. 1109–1119, 2010. View at Publisher · View at Google Scholar · View at Scopus
  8. A. Faure, L. Buzer, and F. Feschet, “Tangential cover for thick digital curves,” Pattern Recognition, vol. 42, no. 10, pp. 2279–2287, 2009. View at Publisher · View at Google Scholar · View at Scopus
  9. C.-H. Teh and R. T. Chin, “On the detection of dominant points on digital curves,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 11, no. 8, pp. 859–872, 1989. View at Publisher · View at Google Scholar · View at Scopus
  10. N. Ansari and K. W. Huang, “Non-parametric dominant point detection,” Pattern Recognition, vol. 24, no. 9, pp. 849–862, 1991. View at Publisher · View at Google Scholar · View at Scopus
  11. T. M. Cronin, “A boundary concavity code to support dominant point detection,” Pattern Recognition Letters, vol. 20, no. 6, pp. 617–634, 1999. View at Publisher · View at Google Scholar · View at Scopus
  12. B. K. Ray and K. S. Ray, “Detection of significant points and polygonal approximation of digitized curves,” Pattern Recognition Letters, vol. 13, no. 6, pp. 443–452, 1992. View at Google Scholar · View at Scopus
  13. D. Sarkar, “A simple algorithm for detection of significant vertices for polygonal approximation of chain-coded curves,” Pattern Recognition Letters, vol. 14, no. 12, pp. 959–964, 1993. View at Google Scholar · View at Scopus
  14. A. Masood, “Dominant point detection by reverse polygonization of digital curves,” Image and Vision Computing, vol. 26, no. 5, pp. 702–715, 2008. View at Publisher · View at Google Scholar · View at Scopus
  15. D. K. Prasad, C. Quek, and M. K. H. Leung, “A non-heuristic dominant point detection based on suppression of break points,” in Image Analysis and Recognition, A. Campilho and M. Kamel, Eds., vol. 7324, pp. 269–276, Springer, Berlin, Germany, 2012. View at Google Scholar
  16. A. Carmona-Poyato, F. J. Madrid-Cuevas, R. Medina-Carnicer, and R. Muñoz-Salinas, “Polygonal approximation of digital planar curves through break point suppression,” Pattern Recognition, vol. 43, no. 1, pp. 14–25, 2010. View at Publisher · View at Google Scholar · View at Scopus
  17. T. P. Nguyen and I. Debled-Rennesson, “A discrete geometry approach for dominant point detection,” Pattern Recognition, vol. 44, no. 1, pp. 32–44, 2011. View at Publisher · View at Google Scholar · View at Scopus
  18. P. L. Rosin, “Techniques for assessing polygonal approximations of curves,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 19, no. 6, pp. 659–666, 1997. View at Publisher · View at Google Scholar · View at Scopus
  19. A. Carmona-Poyato, R. Medina-Carnicer, F. J. Madrid-Cuevas, R. Muoz-Salinas, and N. L. Fernndez-Garca, “A new measurement for assessing polygonal approximation of curves,” Pattern Recognition, vol. 44, no. 1, pp. 45–54, 2011. View at Publisher · View at Google Scholar · View at Scopus
  20. D. K. Prasad and M. K. H. Leung, “Reliability/precision uncertainity in shape fitting problems,” in Proceedings of the 17th IEEE International Conference on Image Processing (ICIP '10), pp. 4277–4280, Hong Kong, China, September 2010. View at Publisher · View at Google Scholar · View at Scopus
  21. D. K. Prasad and M. K. H. Leung, “Polygonal representation of digital curves,” in Digital Image Processing, S. G. Stanciu, Ed., pp. 71–90, InTech, Rijeka, Croatia, 2012. View at Google Scholar
  22. D. K. Prasad, Geometric primitive feature extraction-concepts, algorithms, and applications [Ph.D. thesis], School of Computer Engineering, Nanyang Technological University, Singapore, 2012.
  23. O. Strauss, “Reducing the precision/uncertainty duality in the Hough transform,” in Proceedings of the IEEE International Conference on Image Processing (ICIP '96), pp. 967–970, Lausanne, Switzerland, September 1996. View at Publisher · View at Google Scholar · View at Scopus
  24. G. McCarter and A. Storkey, Air Freight Image Sequences, 2003.
  25. L. Fei-Fei, R. Fergus, and P. Perona, “Learning generative visual models from few training examples: an incremental Bayesian approach tested on 101 object categories,” Computer Vision and Image Understanding, vol. 106, no. 1, pp. 59–70, 2007. View at Publisher · View at Google Scholar · View at Scopus
  26. California Institute of Technology, http://authors.library.caltech.edu/7694.
  27. D. Martin, C. Fowlkes, D. Tal, and J. Malik, “A database of human segmented natural images and its application to evaluating segmentation algorithms and measuring ecological statistics,” in Proceedings of the 8th International Conference on Computer Vision, pp. 416–423, July 2001. View at Scopus
  28. P. Carbonetto, G. Dorkó, C. Schmid, H. Kück, and N. de Freitas, “Learning to recognize objects with little supervision,” International Journal of Computer Vision, vol. 77, no. 1–3, pp. 219–237, 2008. View at Publisher · View at Google Scholar · View at Scopus
  29. M. Everingham, L. V. Gool, C. K. I. Williams, J. Winn, and A. Zisserman, The PASCAL Visual Object Classes Challenge 2007 (VOC2007), 2007.
  30. M. Everingham, L. V. Gool, C. K. I. Williams, J. Winn, and A. Zisserman, The PASCAL Visual Object Classes Challenge 2008 (VOC2008), 2008.
  31. M. Everingham, L. V. Gool, C. K. I. Williams, J. Winn, and A. Zisserman, The PASCAL Visual Object Classes Challenge 2009 (VOC2009), 2009.
  32. M. Everingham, L. V. Gool, C. K. I. Williams, J. Winn, and A. Zisserman, The PASCAL Visual Object Classes Challenge 2010 (VOC2010), 2010.
  33. D. K. Prasad , “Fabrication imperfection analysis and statistics generation using precision and reliability optimization method,” Optics Express, vol. 21, pp. 17602–17614, 2013. View at Google Scholar
  34. D. K. Prasad and M. S. Brown, “Online tracking of deformable objects under occlusion using dominant points,” Journal of the Optical Society of America, vol. 30, pp. 1484–1491, 2013. View at Google Scholar
  35. D. K. Prasad, M. K. H. Leung, C. Quek, and S.-Y. Cho, “A novel framework for making dominant point detection methods non-parametric,” Image and Vision Computing, vol. 30, pp. 843–859, 2012. View at Google Scholar
  36. D. K. Prasad, “Assessing error bound for dominant point detection,” International Journal of Image Processing, vol. 6, pp. 326–333, 2012. View at Google Scholar
  37. D. K. Prasad, C. Quek, M. K. H. Leung, and S.-Y. Cho, “A parameter independent line fitting method,” in Proceedings of the Asian Conference on Pattern Recognition (ACPR '11), pp. 441–445, 2011.
  38. D. G. Lowe, “Three-dimensional object recognition from single two-dimensional images,” Artificial Intelligence, vol. 31, no. 3, pp. 355–395, 1987. View at Google Scholar · View at Scopus
  39. D. H. Douglas and T. K. Peucker, “Algorithms for the reduction of the number of points required to represent a digitized line or its caricature,” Cartographica, vol. 10, pp. 112–122, 1973. View at Google Scholar
  40. U. Ramer, “An iterative procedure for the polygonal approximation of plane curves,” Computer Graphics and Image Processing, vol. 1, no. 3, pp. 244–256, 1972. View at Google Scholar · View at Scopus
  41. M. Marji and P. Siy, “Polygonal representation of digital planar curves through dominant point detection—a nonparametric algorithm,” Pattern Recognition, vol. 37, no. 11, pp. 2113–2130, 2004. View at Publisher · View at Google Scholar · View at Scopus
  42. B. K. Ray and K. S. Ray, “An algorithm for detection of dominant points and polygonal approximation of digitized curves,” Pattern Recognition Letters, vol. 13, no. 12, pp. 849–856, 1992. View at Google Scholar · View at Scopus
  43. C. Arcelli and G. Ramella, “Finding contour-based abstractions of planar patterns,” Pattern Recognition, vol. 26, no. 10, pp. 1563–1577, 1993. View at Publisher · View at Google Scholar · View at Scopus
  44. P. L. Rosin, “Assessing the behaviour of polygonal approximation algorithms,” Pattern Recognition, vol. 36, no. 2, pp. 505–518, 2003. View at Publisher · View at Google Scholar · View at Scopus
  45. D. K. Prasad and M. K. H. Leung, “A hybrid approach for ellipse detection in real images,” in 2nd International Conference on Digital Image Processing, vol. 7546 of Proceedings of SPIE, p. 75460I, Singapore, February 2010. View at Publisher · View at Google Scholar · View at Scopus
  46. D. K. Prasad, “Adaptive traffic signal control system with cloud computing based online learning,” in Proceedings of the 8th International Conference on Information, Communications and Signal Processing (ICICS '11), pp. 1–5, Singapore, December 2011. View at Publisher · View at Google Scholar · View at Scopus