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2013 (Vol. 5, Issue: 20)
Article Information:

SIFT Feature Matching Algorithm with Local Shape Context

Gu Lichuan, Qiao Yulong, Cao Mengru and Guo Qingyan
Corresponding Author:  Gu Lichuan 

Key words:  Elliptical neighboring region, feature matching, local shape context, SIFT algorithm, , ,
Vol. 5 , (20): 4810-4815
Submitted Accepted Published
July 27, 2012 September 03, 2012 May 15, 2013
Abstract:

SIFT (Scale Invariant Feature Transform) is one of the most effective local feature of scale, rotation and illumination invariant, which is widely used in the field of image matching. While there will be a lot mismatches when an image has many similar regions. In this study, an improved SIFT feature matching algorithm with local shape context is put forward. The feature vectors are computed by dominant orientation assignment to each feature point based on elliptical neighboring region and with local shape context and then the feature vectors are matched by using Euclidean distance and the X2 distance. The experiment indicates that the improved algorithm can reduce mismatch probability and acquire good performance on affine invariance, improves matching results greatly.
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  Cite this Reference:
Gu Lichuan, Qiao Yulong, Cao Mengru and Guo Qingyan, 2013. SIFT Feature Matching Algorithm with Local Shape Context.  Research Journal of Applied Sciences, Engineering and Technology, 5(20): 4810-4815.
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ISSN (Online):  2040-7467
ISSN (Print):   2040-7459
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