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Evaluation of feature detectors for bladder fluorescence images

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PL
Ocena algorytmów ekstrakcji cech na potrzeby fluorescencyjnej endoskopii pęcherza moczowego
Języki publikacji
EN
Abstrakty
EN
Images of bladder fluorescence endoscopy are different from generic ones. Up to now there was no evaluation of feature detectors based on such images. In this paper seven popular algorithms were compared.
PL
Zdjęcia wykonane podczas endoskopii fluorescencyjnej różnią się od zwykłych zdjęć. Dotychczasowe porównania nie uwzględniały zdjęć wykonanych w niebieskim świetle. W pracy porównano siedem popularnych algorytmów ekstrakcji cech.
Rocznik
Strony
192--194
Opis fizyczny
Bibliogr. 23 poz., il.
Twórcy
autor
autor
Bibliografia
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  • [2] Olympus, “Olympus observer.” http://www. olympus-europa.com/endoscopy/images/GB_ OBSERVER_2007.pdf, 2007.
  • [3] R. I. Hartley and A. Zisserman, Multiple View Geometry in Computer Vision. Cambridge University Press, ISBN: 0521540518, second ed., 2004.
  • [4] H. Moravec, “Obstacle avoidance and navigation in the real world by a seeing robot rover,” tech. rep., CMU-RI-TR-80-03, Robotics Institute, Carnegie Mellon University & doctoral dissertation, Stanford University, September 1980.
  • [5] W. Forstner, “A feature-based correspondence algorithm for image matching,” International Archives on Photogrammetry and Remote Sensing, vol. 26, no. 3, pp. 150–166, 1986.
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  • [7] K. Mikolajczyk and C. Schmid, “Scale & affine invariant interest point detectors,” International Journal of Computer Vision, vol. 60, no. 1, pp. 63–86, 2004.
  • [8] T. Lindeberg, “Feature detection with automatic scale selection,” International Journal of Computer Vision, vol. 30, no. 2, pp. 79– 116, 1998.
  • [9] D. Lowe, “Object recognition from local scale-invariant features,” in The Proceedings of the 7th IEEE International Conference on Computer Vision, vol. 2, pp. 1150–1157, 1999.
  • [10] K. Mikolajczyk, T. Tuytelaars, C. Schmid, A. Zisserman, J. Matas, F. Schaffalitzky, T. Kadir, and L. V. Gool, “A comparison of affine region detectors,” International Journal of Computer Vision, vol. 65, no. 1-2, pp. 43–72, 2005.
  • [11] T. Tuytelaars and L. V. Gool, “Matching widely separated views based on affine invariant regions,” International Journal of Computer Vision, vol. 59, no. 1, pp. 61–85, 2004.
  • [12] J. Matas, O. Chum, U. Martin, and T. Pajdla, “Robust widebaseline stereo from maximally stable extremal regions,” Image and Vision Computing, vol. 22, pp. 761–767, September 2004.
  • [13] T. Kadir, A. Zisserman, and M. Brady, “An affine invariant salient region detector,” in European Conference on Computer Vision, 2004.
  • [14] C. Schmid, R. Mohr, and C. Bauckhage, “Comparing and evaluating interest points,” in The Proceedings of the 6th International Conference on Computer Vision, pp. 230–235, Jan 1998.
  • [15] C. Schmid, R. Mohr, and C. Bauckhage, “Evaluation of interest point detectors,” International Journal of Computer Vision, vol. 37, no. 2, pp. 151–172, 2000.
  • [16] M. Brown and D. Lowe, “Invariant features from interest point groups,” in British Machine Vision Conference, 2002.
  • [17] T. Lindeberg, “Detecting salient blob-like image structures and their scales with a scale-space primal sketch: A method for focus-of-attention,” International Journal of Computer Vision, vol. 11, pp. 283–318, December 1993.
  • [18] D. G. Lowe, “Distinctive image features from scale-invariant keypoints,” International Journal of Computer Vision, vol. 60, no. 2, pp. 91–110, 2004.
  • [19] T. Kadir and M. Brady, “Saliency, scale and image description,” International Journal of Computer Vision, vol. 45, no. 2, pp. 83– 105, 2001.
  • [20] T. Tuytelaars and L. V. Gool, “Wide baseline stereo matching based on local, affinely invariant regions,” in The Proceedings of the 11th British Machine Vision Conference, 2000.
  • [21] J. Matas, O. Chum, M. Urban, and T. Pajdla, “Robust wide baseline stereo from maximally stable extremal regions,” in The proceedings of the 13th British Machine Vision Conference, pp. 384–393, 2002.
  • [22] A. Behrens, “An image mosaicing algorithm for bladder fluorescence endoscopy,” in International Student Conference on Electrical Engineering POSTER, (Prague), 2008.
  • [23] M. A. Fischler and R. C. Bolles, “Random sample consensus: a paradigm for model fitting with applications to image analysis and automated cartography,” Communications of the ACM, vol. 24, no. 6, pp. 381–395, 1981.
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-article-BPOB-0026-0013
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