DocumentCode :
3366770
Title :
A kind of global motion estimation algorithm based on feature matching
Author :
Guo, Shuxiang ; Qiu, Chenguang ; Ye, Xiufen
Author_Institution :
Coll. of Autom., Harbin Eng. Univ., Harbin, China
fYear :
2009
fDate :
9-12 Aug. 2009
Firstpage :
107
Lastpage :
111
Abstract :
In this paper, we propose a kind of global motion estimation algorithm based on feature matching. The scale invariant feature transform (SIFT) algorithm is applied to global motion estimation. The feature extracted by SIFT algorithm is invariant to image scale and rotation. The matching accuracy is very high even under the condition of additive noise, varying illumination and affine deformation. It is advantageous to get precise estimation. But the feature of local motion is disadvantageous for global motion estimation. In order to improve the accuracy of global motion estimation, an adaptive noise reduction algorithm is presented to eliminate local motion. The parameters of the camera affine model are computed by the least square method. The proposed algorithm is tested by the standard image sequences and compared with other related methods. The experiments show that the proposed algorithm is adaptive and more accurate.
Keywords :
feature extraction; image matching; image sensors; image sequences; least squares approximations; motion estimation; transforms; adaptive noise reduction algorithm; camera affine model; feature extraction; feature matching; global motion estimation algorithm; image sequences; least square method; scale invariant feature transform algorithm; Additive noise; Cameras; Feature extraction; Image reconstruction; Image sequences; Iterative algorithms; Layout; Lighting; Motion estimation; Noise reduction; SIFT; adaptive noise reduction; global motion estimation;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Mechatronics and Automation, 2009. ICMA 2009. International Conference on
Conference_Location :
Changchun
Print_ISBN :
978-1-4244-2692-8
Electronic_ISBN :
978-1-4244-2693-5
Type :
conf
DOI :
10.1109/ICMA.2009.5246379
Filename :
5246379
Link To Document :
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