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Classification of Feature Points Required for Multi-Frame Based Building Recognition
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 Title & Authors
Classification of Feature Points Required for Multi-Frame Based Building Recognition
Park, Si-young; An, Ha-eun; Lee, Gyu-cheol; Yoo, Ji-sang;
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The extraction of significant feature points from a video is directly associated with the suggested method`s function. In particular, the occlusion regions in trees or people, or feature points extracted from the background and not from objects such as the sky or mountains are insignificant and can become the cause of undermined matching or recognition function. This paper classifies the feature points required for building recognition by using multi-frames in order to improve the recognition function(algorithm). First, through SIFT(scale invariant feature transform), the primary feature points are extracted and the mismatching feature points are removed. To categorize the feature points in occlusion regions, RANSAC(random sample consensus) is applied. Since the classified feature points were acquired through the matching method, for one feature point there are multiple descriptors and therefore a process that compiles all of them is also suggested. Experiments have verified that the suggested method is competent in its algorithm.
Occlusion region;multi-frame;feature extraction;feature matching;classification;homography;RANSAC;
 Cited by
J. Li, W. Huang, L. Shao, and N. Allinson, "Building recognition in urban environments: A survey of state-of-the-art and future challenges," Inf. Sci., vol. 277, no. 1, pp. 406-420, Sept. 2014. crossref(new window)

D. Lowe, "Distinctive image features from scale-invariant keypoints," Int. J. Computer Vision, vol. 60, no. 2, pp. 91-110, Nov. 2004. crossref(new window)

Y. Li and L. G. Shapiro, "Consistent line clusters for building recognition in CBIR," in Proc. 16th Int. Conf. Pattern Recognition, vol. 3, pp. 952-956, Aug. 2002.

I. T. Jolliffe, Principal component analysis, 2nd Ed., Springer, 2002.

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

X. He and P. Niyogi, "Locality preserving projection," in Proc. Conf. Advances in Neural Inf. Process. Syst., 2003.

D. Cai, X. He, and J. Han, Using graph model for face analysis, Department of Computer Science, University of Illinois at Urbana Champaign, Sept. 2005.

D. Cai, X. he, and J. Han, "Semi-supervised discriminant analysis," in Proc. IEEE 11th Int. Conf. Computer Vision, pp. 1-7, Oct. 2007.

J. H. Heo and M. C. Lee, "Building recognition using image segmentation and color features," J. Korea Robotics Soc., vol. 8, no. 2, pp. 82-91, Jun. 2013. crossref(new window)

W. Zahng and J. Kosecka, "Localization based on building recognition," IEEE Computer Soc. Conf., Jun. 2005.

V. Vapnik, The nature of statistical learning theory, Springer, 1995.

H. Trinh, D. N. Kim, and K. H. Jo, "Facet-based multiple building analysis for robot intelligence," Mathematics and Computation, vol. 205, no. 2, pp. 537-549, Nov. 2008. crossref(new window)

M. A. Fischler and R. C. Bolles, "Random sample consensus: a paradigm for model fitting with applications to image analysis and automated cartography," Commun. ACM, vol. 24, no. 6, pp. 381-395, Jun. 1981. crossref(new window)

H. Bay, A. Ess, T. Tuytelaars, and L. V. Gool, "Speeded-up robust feature," Computer Vision and Image Understanding, vol. 10, no. 3, pp. 346-359, Jun. 2008.

S. M. Smith and J. M. Brady, "Susan - a new approach to low level image processing," Int. J. Computer Vision, vol. 23, no. 1, pp. 45-78, May 1997. crossref(new window)

E. Rosten and T. Drummond, "Machine learning for high-speed corner detection," Eur. Conf. Computer Vision, pp. 430-443, Graz, Austria, May 2006.

L. M. J. Florack, B. M. T. H. Romeny, J. J. Koenderink, and M. A. Viergever, "General intensity transformations and differential invariants," J. Mathematical Imaging and Vision, vol. 4, no. 2, pp. 171-187, May 1994. crossref(new window)

E. Dubrofsky, Homography estimation, Univ. of British COLUMBIA, Mar. 2009.

M. M. Hossain, H. J. Lee, and J. S. Lee, "Fast image stitching for video stabilization using sift feature points," J. KICS, vol. 39, no. 10, pp. 957-966, Oct. 2014.

B. W. Chung, K. Y. Park, and S. Y. Hwang, "A fast and efficient haar-like feature selection algorithm for object detection," J. KICS, vol. 38, no. 6, pp. 486-497, Jun. 2013.

J. H. Hong, B. C. Ko, and J. Y. Nam, "Human action recognition in still image using weighted bag-of-features and ensemble decision trees," J. KICS, vol. 38, no. 1, pp. 1-9, Jan. 2013.