An indoor adaptive global motion estimation method

Author

Zhang Huiqing ; Gao Lin

Author_Institution

Beijing Univ. of Technol., Beijing, China

fYear

2014

fDate

28-30 July 2014

Firstpage

8027

Lastpage

8031

Abstract

Aiming at the problem of redundancy phenomenon in the motor process of image matching feature points, we proposed a kind of global motion which use Kalman filter algorithm to estimate the overlapping areas of matching images. This algorithm only to extract the feature points in the overlapping area, part of the extracted feature points also proposed an effective combination of SUSAN-SURF algorithm. SURF retain the high efficiency and SUSAN has the outline information. And then SURF algorithm is effectively improved by using KNN to speed up image matching, determine the matching point to realize image registration. Finally the algorithm was verified by experiment, this global motion method can under the premise in accuracy, improve the real-time performance.

Keywords

Kalman filters; feature extraction; image matching; image registration; motion estimation; redundancy; Kalman filter algorithm; SUSAN-SURF algorithm; feature point extraction; image matching; image registration; indoor adaptive global motion estimation method; k nearest neighbor method; motor process; overlapping area estimation; redundancy phenomenon; Accuracy; Algorithm design and analysis; Feature extraction; Kalman filters; Motion estimation; Noise; Prediction algorithms; KNN; Kalman filter; SUSAN-SURF; global motion;

fLanguage

English

Publisher

ieee

Conference_Titel

Control Conference (CCC), 2014 33rd Chinese

Conference_Location

Nanjing

Type

conf

DOI

10.1109/ChiCC.2014.6896342

Filename

6896342