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Multi-camera based Images through Feature Points Algorithm for HDR Panorama
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 Title & Authors
Multi-camera based Images through Feature Points Algorithm for HDR Panorama
Yeong, Jung-Ho;
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 Abstract
With the spread of various kinds of cameras such as digital cameras and DSLR and a growing interest in high-definition and high-resolution images, a method that synthesizes multiple images is being studied among various methods. High Dynamic Range (HDR) images store light exposure with even wider range of number than normal digital images. Therefore, it can store the intensity of light inherent in specific scenes expressed by light sources in real life quite accurately. This study suggests feature points synthesis algorithm to improve the performance of HDR panorama recognition method (algorithm) at recognition and coordination level through classifying the feature points for image recognition using more than one multi frames.
 Keywords
image-based lighting;aerial image;SIFT;RANSAC;
 Language
English
 Cited by
 References
1.
D. Miorandia, S. Sicarib, F. D. Pellegrinia, and I. Chlamtaca, "Internet of things: Vision, applications and research challenges," Ad Hoc Networks, Vol. 10, No. 7, pp. 1497-1516, 2012. crossref(new window)

2.
H. Ali, G. Paar, L. Paletta, "Semantic indexing for visual recognition of buildings," in: Proc. Int'l. Symposium on Mobile Mapping Technology, pp. 28-31, 2007.

3.
J. Li, W. Huang, L. Shao, and N. Allinson, "Building recognition in urban environments: A survey of state-of-the-art and future challenges," Information Sciences, Vol. 277, No. 1, pp. 406-420, 2014. DOI: 10.1016/j.ins.2014.02.112 crossref(new window)

4.
D. Lowe, "Distinctive image features from scale-invariant keypoints," International Journal of Computer Vision, Vol. 60, No. 2, pp. 91-110, 2004. DOI: 10.1023/B:VISI.0000029664.99615.94 crossref(new window)

5.
Y. Li, and L. G. Shapiro, "Consistent line clusters for building recognition in CBIR," Pattern Recognition, 2002. Proceedings. 16th International Conference, Vol. 3, pp. 952-956, 2002.

6.
I. Jolliffe, Principal component analysis, Wiley StatsRef: Statistics Reference Online, 2002.

7.
D. Cai, X. He, J. Han, Using Graph Model for Face Analysis, Department of Computer Science, University of Illinois at Urbana Champaign, 2005.

8.
G. J. Malachlan, Discriminant analysis and statistical pattern recognition, Wiley-interscience, New York, 1992.

9.
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, June 1981. crossref(new window)

10.
S. Cho, B. Shrestha, B. Hong, and H. Y. Jeong, "Study of Generating Animated Character Using the Face Pattern Recognition," Lecture Notes in Electrical Engineering, Vol. 107, pp. 127-133, 2011. DOI: 10.1007/978-94-007-2598-0_13 crossref(new window)

11.
S. Cho, K. C. Son, J. Lee, and S. H. Lee, "FOLI Technique Algorithm for Real-Time Efficient Image Processing," Lecture Notes in Electrical Engineering, Vol. 279, pp1337-1342, 2014. DOI: 10.1007/978-3-642-41674-3_185 crossref(new window)

12.
E. Rublee, V. Rabaud, and K. Konolige, "ORB: An efficient alternative to SIFT or SURF," 2011 IEEE International Conference on Computer Vision (ICCV), pp. 2564-2571, 2011.

13.
Y. Ke and R. Sukthankar. "Pca-sift: A more distinctive representation for local image descriptors," In Computer Vision and Pattern Recognition, pp. 506-513, 2004. DOI: 10.1109/CVPR.2004.1315206 crossref(new window)

14.
W. S. Lee, J. Gu, and M. T. Nadia, "Generating Animatable 3D Virtual Humans from Photographs," Computer Graphics Forum, Vol. 19, No. 3, pp. 1-10, 2000. crossref(new window)

15.
Z. Wang, B. Fan, and F. Wu, FRIF: Fast Robust Invariant Feature, WANG et al.: Fast robust invariant feature, 1-12, 2013.

16.
D. G. Lowe, "Distinctive image features from scale-invariant keypoints," International Journal of Computer Vision, Vol. 60, pp.91-110, 2004. crossref(new window)

17.
E. Mair, G. D. Hager, D. Burschka, M. Suppa, and G. Hirzinger, "Adaptive and generic corner detection based on the accelerated segment test," In European conference on Computer vision, pp. 183-196, 2010.

18.
E. Rosten and T. Drummond, "Machine learning for high-speed corner detection," In European conference on Computer Vision, pp. 430-443, 2006. DOI: 10.1007/11744023_34 crossref(new window)

19.
E. Rosten, R. Porter, and T. Drummond, "Faster and better: A machine learning approach to corner detection," Pattern Analysis and Machine Intelligence, Vol. 32, No. 1, pp.105-119, 2010. crossref(new window)

20.
M. Calonder, V. Lepetit, C. Strecha, and P. Fua, "Brief: binary robust independent elementary features," In European conference on Computer vision, pp. 778-792, 2010.

21.
E. Rublee, V. Rabaud, K. Konolige, and G. Bradski, "Orb: An efficient alternative to sift or surf." In International Conference on Computer Vision, pp. 2564-2571, 2011.

22.
H. Bay, T. Tuytelaars, and L. V. Gool, "Surf: speeded up robust features," In European Conference on Computer Vision, pp. 404-417, 2006.