JOURNAL BROWSE
Search
Advanced SearchSearch Tips
Performance Analysis of Modified LLAH Algorithm under Gaussian Noise
facebook(new window)  Pirnt(new window) E-mail(new window) Excel Download
 Title & Authors
Performance Analysis of Modified LLAH Algorithm under Gaussian Noise
Ryu, Hosub; Park, Hanhoon;
  PDF(new window)
 Abstract
Methods of detecting, describing, matching image features, like corners and blobs, have been actively studied as a fundamental step for image processing and computer vision applications. As one of feature description/matching methods, LLAH(Locally Likely Arrangement Hashing) describes image features based on the geometric relationship between their neighbors, and thus is suitable for scenes with poor texture. This paper presents a modified LLAH algorithm, which includes the image features themselves for robustly describing the geometric relationship unlike the original LLAH, and employes a voting-based feature matching scheme that makes feature description much simpler. Then, this paper quantitatively analyzes its performance with synthetic images in the presence of Gaussian noise.
 Keywords
Modified LLAH;Feature Description;Area Ratio;Cross Ratio;Gaussian Noise;
 Language
Korean
 Cited by
1.
밝기 정보를 결합한 LLAH의 성능 분석,박한훈;문광석;

한국멀티미디어학회논문지, 2016. vol.19. 2, pp.138-145 crossref(new window)
1.
Performance Analysis of Brightness-Combined LLAH, Journal of Korea Multimedia Society, 2016, 19, 2, 138  crossref(new windwow)
 References
1.
Richard Szeliski, “Image Alignment and Stitching: A Tutorial,” Foundations and Trends in Computer Graphics and Computer Vision, Vol. 2, No. 1, pp. 1-104, 2006. crossref(new window)

2.
M.-K. Kim, “Finger-Knuckle-Print Verification Using Vector Similarity Matching of Keypoints,” Journal of Korea Multimedia Society, Vol. 16, No. 9, pp. 1057-1066, 2013. crossref(new window)

3.
K.-W. Choi, D.-U. Jung, S.-H. Lee, and J.-S. Choi, “Interaction Augmented Reality System Using a Hand Motion,” Journal of Korea Multimedia Society, Vol. 15, No. 4, pp. 425-438, 2012. crossref(new window)

4.
T. Tuytelaars and K. Mikolajczyk, “Local Invariant Feature Detectors: A Survey," Foundations and Trends in Computer Graphics and Vision, Vol. 3, No. 3, pp. 177-280, 2007. crossref(new window)

5.
J. Li and N.M. Allinson, “A Comprehensive Review of Current Local Features for Computer Vision," Neurocomputing, Vol. 71, No. 10-12, pp. 1771-1787, 2008. crossref(new window)

6.
T. Nakai, K. Kise, and M. Iwamura. “Use of Affine Invariants in Locally Likely Arrangement Hashing for Camera-Based Document Image Retrieval,” Proceedings of International Conference on Document Analysis Systems, pp. 541-552, 2006.

7.
H. Uchiyama and H. Saito, “Random Dot Markers,” Proceedings of IEEE Virtual Reality Conference, pp. 35-38, 2011.

8.
B.-K. Seo, H. Uchiyama, and J.-I. Park, “stAR: Visualizing Constellations with Star Retrieval,” Proceedings of ACM SIGGRAPH Conference and Exhibition on Computer Graphics and Interactive Techniques in Asia, Article No. 53, 2011.

9.
H. Uchiyama and E. Marchand, “Toward Augmenting Everything: Detecting and Tracking Geometrical Features on Planar Objects,” Proceedings of International Symposium on Mixed and Augmented Reality, pp. 17-25, 2011.

10.
C. Padgett, K. Kreutz-Delgado, and S. Udomkesmalee, “Evaluation of Star Identification Techniques,” Journal of Guidance, Control and Dynamics, Vol. 20, No. 2, pp. 259-267, 1997. crossref(new window)

11.
M. Kolomenkin, S. Pollak, I. Shimshoni, and M. lindenbaum, “Geometric Voting Algorithm for Star Trackers,” IEEE Transactions on Aerospace and Electronic Systems, Vol. 44, No. 2, pp. 441-456, 2008. crossref(new window)

12.
M. Muja and D.G. Lowe, “Fast Approximate Nearest Neighbors with Automatic Algorithm Configuration,” Proceeding of International Conference on Computer Vision Theory and Applications, pp. 331-340, 2009.