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Artırılmış gerçeklik için BRIEF betimleyicileri ve yerelliğe duyarlı karma yöntemi ile nesne arama

Yıl 2017, Cilt: 23 Sayı: 5, 588 - 596, 20.10.2017

Öz

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.

Kaynakça

  • 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.
  • 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.
  • 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.
  • Andoni A, Indyk P. “Near optimal hashing algorithms for approximate nearest neighbor in high dimensions”. Communications of the ACM, 51(1), 117-122, 2008.
  • Harris C, Stephens M. “A combined corner and edge detector”. Alvey Vision Conference, Manchester, United Kingdom, 31 August – 2 September 1988.
  • Lowe D G. “Distinctive Image Features from scale-invariant keypoints”. International Journal of Computer Vision, 20(2), 91-110, 2004.
  • Lindeberg T. “Scale-Space theory: A basic tool for analyzing structures at different scales”. Journal of Applied Statistics, 21(1-2), 225-270, 1994.
  • Mikolajczyk K, Schmid C. “An affine invariant interest point detector”. European Conference on Computer Vision, Copenhagen, Denmark, 28-31 May 2002.
  • 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.
  • Mikolajczyk K, Schmid C. “A Performance evaluation of local descriptors”. IEEE Transactions on Pattern Analysis and Machine Learning, 27(10), 1615-1630, 2004.
  • Bay H, Ess A, Tuytelaars T, Gool L V. “SURF: Speeded up robust features”. Computer Vision and Image Understanding, 10(3), 346-359, 2008.
  • Viola P, Jones J. “Robust real-time face detection”. International Journal of Computer Vision, 57(2), 137-154, 2004.
  • 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.
  • 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.
  • 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.
  • Leutenegger S, Chli M, Siegwart R Y. “BRISK: Brinary robust invariant scalable keypoints”. International Conference on Computer Vision, 2011.
  • 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.
  • Alahi A, Ortiz R, Vandergheynst P. “FREAK: Fast Retina Keypoints”. Conference on Computer Vision and Pattern Recognition, Providence, Rhode Island, 16-21 June 2012.
  • Trzcinski T, Christoudias M, Lepetit V. “Learning Image Descriptors with Boosting”. IEEE Transactions on Pattern Analysis and Machine Learning, 37(3), 597-610, 2015.
  • Levi G, Hassner T. “LATCH: Learned Arrangements of Three Patch Codes”. http://www.openu.ac.il/home/hassner/projects/LATCH (29.02.2016).
  • Muja M, Lowe DG. “Fast matching of binary features”. Computer and Robot Vision Conference, Toronto, Canada, 27-30 May 2012.
  • 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.
  • 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.
  • 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.
  • 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.
  • 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.
  • Chum O, Matas J. “Matching with PROSAC-Progressive sample consensus”. Conference on Computer Vision and Pattern Recognition, San Diego, CA, 20-26 June 2005.
  • Hartley R, Zisserman A. Multiple View Geometry in Computer Vision. 2nd ed. Cambridge, UK, Cambridge University Press, 2000.
  • Manning C, Raghavan P, Schütze M. Introduction to Information Retrieval. 1st ed. New York, United States of America, Cambridge University Press, 2008.
  • Lv Q, Josephson W, Wang Z, Charikar M, Li K. “Multi-Probe LSH: Efficient indexing for high-dimensional similarity search”. International Conference on Very Large Databases, Vienna, Austria, 23-27 September 2007.
  • Forster C, Pizzoli M, Scaramuzza D. "SVO: Fast semi-direct monocular visual odometry". IEEE International Conference on Robotics and Automation (ICRA), Hong Kong, China, 31 May – 7 June 2014.
  • Engel J, Schöps T, Cremers D. "LSD-SLAM: Large-scale direct monocular SLAM". European Conference on Computer Vision, Zurich, Switzerland, 6-12 September 2014.
  • 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.

Object detection with BRIEF descriptors and locality sensitive matching for augmented reality

Yıl 2017, Cilt: 23 Sayı: 5, 588 - 596, 20.10.2017

Öz

In
this paper, an object detection approach suitable for mobile augmented reality
is presented. The baseline approach is based on matching keypoint descriptors
and verifying these matches with geometric constraints. The performance
optimizations necessary for speeding up matching are detailed. It is also
demonstrated that it is possible to increase the performance of the Locality
Sensitive Hashing by exploiting approaches from the information retrieval
field.

Kaynakça

  • 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.
  • 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.
  • 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.
  • Andoni A, Indyk P. “Near optimal hashing algorithms for approximate nearest neighbor in high dimensions”. Communications of the ACM, 51(1), 117-122, 2008.
  • Harris C, Stephens M. “A combined corner and edge detector”. Alvey Vision Conference, Manchester, United Kingdom, 31 August – 2 September 1988.
  • Lowe D G. “Distinctive Image Features from scale-invariant keypoints”. International Journal of Computer Vision, 20(2), 91-110, 2004.
  • Lindeberg T. “Scale-Space theory: A basic tool for analyzing structures at different scales”. Journal of Applied Statistics, 21(1-2), 225-270, 1994.
  • Mikolajczyk K, Schmid C. “An affine invariant interest point detector”. European Conference on Computer Vision, Copenhagen, Denmark, 28-31 May 2002.
  • 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.
  • Mikolajczyk K, Schmid C. “A Performance evaluation of local descriptors”. IEEE Transactions on Pattern Analysis and Machine Learning, 27(10), 1615-1630, 2004.
  • Bay H, Ess A, Tuytelaars T, Gool L V. “SURF: Speeded up robust features”. Computer Vision and Image Understanding, 10(3), 346-359, 2008.
  • Viola P, Jones J. “Robust real-time face detection”. International Journal of Computer Vision, 57(2), 137-154, 2004.
  • 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.
  • 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.
  • 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.
  • Leutenegger S, Chli M, Siegwart R Y. “BRISK: Brinary robust invariant scalable keypoints”. International Conference on Computer Vision, 2011.
  • 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.
  • Alahi A, Ortiz R, Vandergheynst P. “FREAK: Fast Retina Keypoints”. Conference on Computer Vision and Pattern Recognition, Providence, Rhode Island, 16-21 June 2012.
  • Trzcinski T, Christoudias M, Lepetit V. “Learning Image Descriptors with Boosting”. IEEE Transactions on Pattern Analysis and Machine Learning, 37(3), 597-610, 2015.
  • Levi G, Hassner T. “LATCH: Learned Arrangements of Three Patch Codes”. http://www.openu.ac.il/home/hassner/projects/LATCH (29.02.2016).
  • Muja M, Lowe DG. “Fast matching of binary features”. Computer and Robot Vision Conference, Toronto, Canada, 27-30 May 2012.
  • 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.
  • 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.
  • 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.
  • 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.
  • 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.
  • Chum O, Matas J. “Matching with PROSAC-Progressive sample consensus”. Conference on Computer Vision and Pattern Recognition, San Diego, CA, 20-26 June 2005.
  • Hartley R, Zisserman A. Multiple View Geometry in Computer Vision. 2nd ed. Cambridge, UK, Cambridge University Press, 2000.
  • Manning C, Raghavan P, Schütze M. Introduction to Information Retrieval. 1st ed. New York, United States of America, Cambridge University Press, 2008.
  • Lv Q, Josephson W, Wang Z, Charikar M, Li K. “Multi-Probe LSH: Efficient indexing for high-dimensional similarity search”. International Conference on Very Large Databases, Vienna, Austria, 23-27 September 2007.
  • Forster C, Pizzoli M, Scaramuzza D. "SVO: Fast semi-direct monocular visual odometry". IEEE International Conference on Robotics and Automation (ICRA), Hong Kong, China, 31 May – 7 June 2014.
  • Engel J, Schöps T, Cremers D. "LSD-SLAM: Large-scale direct monocular SLAM". European Conference on Computer Vision, Zurich, Switzerland, 6-12 September 2014.
  • 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.
Toplam 33 adet kaynakça vardır.

Ayrıntılar

Konular Mühendislik
Bölüm Makale
Yazarlar

Mustafa Özuysal

Yayımlanma Tarihi 20 Ekim 2017
Yayımlandığı Sayı Yıl 2017 Cilt: 23 Sayı: 5

Kaynak Göster

APA Özuysal, M. (2017). Artırılmış gerçeklik için BRIEF betimleyicileri ve yerelliğe duyarlı karma yöntemi ile nesne arama. Pamukkale Üniversitesi Mühendislik Bilimleri Dergisi, 23(5), 588-596.
AMA Özuysal M. Artırılmış gerçeklik için BRIEF betimleyicileri ve yerelliğe duyarlı karma yöntemi ile nesne arama. Pamukkale Üniversitesi Mühendislik Bilimleri Dergisi. Ekim 2017;23(5):588-596.
Chicago Özuysal, Mustafa. “Artırılmış gerçeklik için BRIEF Betimleyicileri Ve yerelliğe Duyarlı Karma yöntemi Ile Nesne Arama”. Pamukkale Üniversitesi Mühendislik Bilimleri Dergisi 23, sy. 5 (Ekim 2017): 588-96.
EndNote Özuysal M (01 Ekim 2017) Artırılmış gerçeklik için BRIEF betimleyicileri ve yerelliğe duyarlı karma yöntemi ile nesne arama. Pamukkale Üniversitesi Mühendislik Bilimleri Dergisi 23 5 588–596.
IEEE M. Özuysal, “Artırılmış gerçeklik için BRIEF betimleyicileri ve yerelliğe duyarlı karma yöntemi ile nesne arama”, Pamukkale Üniversitesi Mühendislik Bilimleri Dergisi, c. 23, sy. 5, ss. 588–596, 2017.
ISNAD Özuysal, Mustafa. “Artırılmış gerçeklik için BRIEF Betimleyicileri Ve yerelliğe Duyarlı Karma yöntemi Ile Nesne Arama”. Pamukkale Üniversitesi Mühendislik Bilimleri Dergisi 23/5 (Ekim 2017), 588-596.
JAMA Özuysal M. Artırılmış gerçeklik için BRIEF betimleyicileri ve yerelliğe duyarlı karma yöntemi ile nesne arama. Pamukkale Üniversitesi Mühendislik Bilimleri Dergisi. 2017;23:588–596.
MLA Özuysal, Mustafa. “Artırılmış gerçeklik için BRIEF Betimleyicileri Ve yerelliğe Duyarlı Karma yöntemi Ile Nesne Arama”. Pamukkale Üniversitesi Mühendislik Bilimleri Dergisi, c. 23, sy. 5, 2017, ss. 588-96.
Vancouver Özuysal M. Artırılmış gerçeklik için BRIEF betimleyicileri ve yerelliğe duyarlı karma yöntemi ile nesne arama. Pamukkale Üniversitesi Mühendislik Bilimleri Dergisi. 2017;23(5):588-96.





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