MUSTAFA ÖZUYSAL
(Bilgisayar Mühendisliği Bölümü, Mühendislik Fakültesi, İzmir Yüksek Teknoloji Enstitüsü, İzmir, Türkiye)
Yıl: 2017Cilt: 23Sayı: 5ISSN: 2147-5881Sayfa Aralığı: 588 - 596Türkçe

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Artırılmış gerçeklik için BRIEF betimleyicileri ve yerelliğe duyarlı karma yöntemi ile nesne arama
Bu çalışmada mobil artırılmış gerçeklik için kullanılabilecek bir nesne arama yöntemi sunulmaktadır. Temel olarak yöntem anahtar nokta betimleyicilerinin eşleştirilmesine ve bu anahtar nokta eşlerinin geometrik kıstaslar ile süzülmesine dayanmaktadır. Eşlemenin hızlandırılması için gerekli iyileştirmeler detayları ile verilmektedir. Ayrıca, Yerelliğe Duyarlı Karma işleminin performansının bilgi erişim yaklaşımlarından faydalanılarak arttırılabileceği de gösterilmiştir
DergiAraştırma MakalesiErişime Açık
  • [1] Ondruska P, Kohli P, Izadi S. "Mobilefusion: Real-time volumetric surface reconstruction and dense tracking on mobile phones". IEEE Transactions on Visualization and Computer Graphics, 21(11), 1251-1258, 2015.
  • [2] Özuysal M, Lepetit V, Fleuret F, Fua P. “Feature harvesting for tracking-by-detection”. European Conference on Computer Vision, Graz, Austria, 7-13 May 2006.
  • [3] Liu H, Zhang G, Bao H. "Robust keyframe-based monocular SLAM for augmented reality". International Symposium on Mixed and Augmented Reality, Merida, Mexico, 19-23 September 2016.
  • [4] Andoni A, Indyk P. “Near optimal hashing algorithms for approximate nearest neighbor in high dimensions”. Communications of the ACM, 51(1), 117-122, 2008.
  • [5] Harris C, Stephens M. “A combined corner and edge detector”. Alvey Vision Conference, Manchester, United Kingdom, 31 August – 2 September 1988.
  • [6] Lowe D G. “Distinctive Image Features from scaleinvariant keypoints”. International Journal of Computer Vision, 20(2), 91-110, 2004.
  • [7] Lindeberg T. “Scale-Space theory: A basic tool for analyzing structures at different scales”. Journal of Applied Statistics, 21(1-2), 225-270, 1994.
  • [8] Mikolajczyk K, Schmid C. “An affine invariant interest point detector”. European Conference on Computer Vision, Copenhagen, Denmark, 28-31 May 2002.
  • [9] Matas J, Chum O, Martin U, Pajdla T. “Robust wide baseline stereo from maximally stable extremal regions”. British Machine Vision Conference, Cardiff, United Kingdom, 2-5 September 2002.
  • [10] Mikolajczyk K, Schmid C. “A Performance evaluation of local descriptors”. IEEE Transactions on Pattern Analysis and Machine Learning, 27(10), 1615-1630, 2004.
  • [11] Bay H, Ess A, Tuytelaars T, Gool L V. “SURF: Speeded up robust features”. Computer Vision and Image Understanding, 10(3), 346-359, 2008.
  • [12] Viola P, Jones J. “Robust real-time face detection”. International Journal of Computer Vision, 57(2), 137-154, 2004.
  • [13] Wagner D, Reitmayr G, Mulloni A, Drummond T, Schmalstieg D. “Pose tracking from natural features on mobile phones”. International Symposium on Mixed and Augmented Reality, Cambridge, United Kingdom, 15-18 September 2008.
  • [14] Rosten E, Porter R, Drummond T. “Faster and better: A machine learning approach to corner detection”. IEEE Transactions on Pattern Analysis and Machine Learning, 32(1), 105-119, 2010.
  • [15] Calonder M, Lepetit V, Özuysal M, Trzcinski T, Strecha C, Fua P. “BRIEF: Computing a binary local descriptor very fast”. IEEE Transactions on Pattern Analysis and Machine Learning, 34(7), 1281-1298, 2012.
  • [16] Leutenegger S, Chli M, Siegwart R Y. “BRISK: Brinary robust invariant scalable keypoints”. International Conference on Computer Vision, 2011.
  • [17] Rublee E, Rabaud V, Konolige K, Bradski G. “ORB: An efficient alternative to SIFT or SURF”. International Conference on Computer Vision, Barcelona, Spain, 6-13 November 2011.
  • [18] Alahi A, Ortiz R, Vandergheynst P. “FREAK: Fast Retina Keypoints”. Conference on Computer Vision and Pattern Recognition, Providence, Rhode Island, 16-21 June 2012.
  • [19] Trzcinski T, Christoudias M, Lepetit V. “Learning Image Descriptors with Boosting”. IEEE Transactions on Pattern Analysis and Machine Learning, 37(3), 597-610, 2015.
  • [20] Levi G, Hassner T. “LATCH: Learned Arrangements of Three Patch Codes”. http://www.openu.ac.il/home/hassner/projects/LATCH (29.02.2016).
  • [21] Muja M, Lowe DG. “Fast matching of binary features”. Computer and Robot Vision Conference, Toronto, Canada, 27-30 May 2012.
  • [22] Trzcinski T, Lepetit V, Fua P. “Thick boundaries in binary space and their influence on nearest neighbor search”. Pattern Recognition Letters, 33(16), 2173-2180, 2012.
  • [23] Muja M, Lowe DG. "Scalable nearest neighbor algorithms for high dimensional data". IEEE Transactions on Pattern Analysis and Machine Intelligence, 36(11), 2227-2240, 2014.
  • [24] Kalantidis Y, Avrithis Y. "Locally optimized product quantization for approximate nearest neighbor search". Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Columbus, Ohio, United States of America, 24-27 June 2014.
  • [25] Harwood B, Drummond T. "FANNG: Fast approximate nearest neighbour graphs". Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, United States of America, 26 June – 1 July 2016.
  • [26] Fischler M, Bolles R. “Random sample consensus: a paradigm for model fitting with applications to image analysis and automated cartography”. Communications of the ACM, 24(6), 381-395, 1981.
  • [27] Chum O, Matas J. “Matching with PROSAC-Progressive sample consensus”. Conference on Computer Vision and Pattern Recognition, San Diego, CA, 20-26 June 2005.
  • [28] Hartley R, Zisserman A. Multiple View Geometry in Computer Vision. 2nd ed. Cambridge, UK, Cambridge University Press, 2000.
  • [29] Manning C, Raghavan P, Schütze M. Introduction to Information Retrieval. 1st ed. New York, United States of America, Cambridge University Press, 2008.
  • [30] Lv Q, Josephson W, Wang Z, Charikar M, Li K. “MultiProbe LSH: Efficient indexing for high-dimensional similarity search”. International Conference on Very Large Databases, Vienna, Austria, 23-27 September 2007.
  • [31] Forster C, Pizzoli M, Scaramuzza D. "SVO: Fast semidirect monocular visual odometry". IEEE International Conference on Robotics and Automation (ICRA), Hong Kong, China, 31 May – 7 June 2014.
  • [32] Engel J, Schöps T, Cremers D. "LSD-SLAM: Large-scale direct monocular SLAM". European Conference on Computer Vision, Zurich, Switzerland, 6-12 September 2014.
  • [33] Arth C, Pirchheim C, Ventura J, Schmalstieg D, Lepetit V. "Instant outdoor localization and slam initialization from 2.5d maps". IEEE Transactions on Visualization and Computer Graphics, 21(11), 1309-1318, 2015.

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