Classification of Feature Points Required for Multi-Frame Based Building Recognition 


Vol. 41,  No. 3, pp. 317-327, Mar.  2016


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  Abstract

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.

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  Cite this article

[IEEE Style]

S. Park, H. An, G. Lee, J. Yoo, "Classification of Feature Points Required for Multi-Frame Based Building Recognition," The Journal of Korean Institute of Communications and Information Sciences, vol. 41, no. 3, pp. 317-327, 2016. DOI: .

[ACM Style]

Si-young Park, Ha-eun An, Gyu-cheol Lee, and Ji-sang Yoo. 2016. Classification of Feature Points Required for Multi-Frame Based Building Recognition. The Journal of Korean Institute of Communications and Information Sciences, 41, 3, (2016), 317-327. DOI: .

[KICS Style]

Si-young Park, Ha-eun An, Gyu-cheol Lee, Ji-sang Yoo, "Classification of Feature Points Required for Multi-Frame Based Building Recognition," The Journal of Korean Institute of Communications and Information Sciences, vol. 41, no. 3, pp. 317-327, 3. 2016.

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pISSN : 1226 - 4717
eISSN : 2287 - 3880