Advanced SearchSearch Tips
Performance Optimization of LLAH for Tracking Random Dots under Gaussian Noise
facebook(new window)  Pirnt(new window) E-mail(new window) Excel Download
  • Journal title : Journal of Broadcast Engineering
  • Volume 20, Issue 6,  2015, pp.912-920
  • Publisher : The Korean Institute of Broadcast and Media Engineers
  • DOI : 10.5909/JBE.2015.20.6.912
 Title & Authors
Performance Optimization of LLAH for Tracking Random Dots under Gaussian Noise
Park, Hanhoon;
  PDF(new window)
Unlike general texture-based feature description algorithms, Locally Likely Arrangement Hashing (LLAH) algorithm describes a feature based on the geometric relationship between its neighbors. Thus, even in poor-textured scenes or large camera pose changes, it can successfully describe and track features and enables to implement augmented reality. This paper aims to optimize the performance of LLAH algorithm for tracking random dots (
LLAH;random dot tracking;Gaussian noise;feature description;augmented reality;
 Cited by
H. Kato, M. Billinghurst, I. Poupyrev, K. Imamoto, and K. Tachibana, “Virtual object manipulation on a table-top AR environment,” Proc. of ISAR, pp. 111–119, 2000.

M. Fiala, “ARTag, a fiducial marker system using digital techniques,” Proc. of CVPR, pp. 590–596, 2005.

ALVAR, [Online; accessed 27-July-2015]

D. Wagner, G. Reitmayr, A. Mulloni, T. Drummond, and D. Schmalstieg, “Pose tracking from natural features on mobile phones,” Proc. of ISMAR, pp. 125–134, 2008.

K. Kim, V. Lepetit, W. Woo, “Scalable real-time planar targets tracking for digilog books,” Vis. Comput., vol. 26, no. 6–8, pp. 1145–1154, 2010. crossref(new window)

C. Schmid, R. Mohr, and C. Bauckhage, “Evaluation of interest point detectors," IJCV, vol. 37, no. 2, pp. 151-172, 2000. crossref(new window)

D. G. Lowe, “Distinctive image features from scale-invariant keypoints”, IJCV, vol. 60, no. 2, pp. 91-110, 2004. crossref(new window)

H. Bay, T. Tuytelaars, and L. V. Gool, “SURF: Speeded up robust features,” Proc. of ECCV, pp. 404-417, 2006.

T. Nakai, K. Kise, and M. Iwamura, “Use of affine invariants in locally likely arrangement hashing for camera-based document image retrieval,” Proc. DAS, pp. 541-552, 2006.

H. Uchiyama and H. Saito, “Random dot markers,” Proc. of VR, pp. 35-38, 2011.

H. Uchiyama and H. Saito, “Augmenting text document by on-line learning of local arrangement of keypoints,” Proc. of ISMAR, pp. 95-98, 2009.

S. Martedi, B. Thomas, and H. Saito, “Region-based tracking using sequences of relevance measures,” Proc. of ISMAR, 2013.

H. Uchiyama and E. Marchand, “Toward augmenting everything: Detecting and tracking geometrical features on planar objects,” Proc. of ISMAR, pp. 17-25, 2011.