TY - GEN
T1 - A novel and blur-invariant local feature image matching
AU - Tong, Qiang
AU - Aoki, Terumasa
PY - 2017/7/25
Y1 - 2017/7/25
N2 - The lack of robustness to blur (especially motion blur) is one of the biggest problem in the existing local feature schemes. In this paper, we present a novel Local feature scheme to solve this problem. Our proposed method is good at image matching between (motion and Gaussian) blurred images and non-blurred images. Experimental results show that the proposed method outperforms state of the art methods for blurred image matching.
AB - The lack of robustness to blur (especially motion blur) is one of the biggest problem in the existing local feature schemes. In this paper, we present a novel Local feature scheme to solve this problem. Our proposed method is good at image matching between (motion and Gaussian) blurred images and non-blurred images. Experimental results show that the proposed method outperforms state of the art methods for blurred image matching.
UR - http://www.scopus.com/inward/record.url?scp=85028506870&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85028506870&partnerID=8YFLogxK
U2 - 10.1109/ICCE-China.2017.7990994
DO - 10.1109/ICCE-China.2017.7990994
M3 - Conference contribution
AN - SCOPUS:85028506870
T3 - 2017 IEEE International Conference on Consumer Electronics - Taiwan, ICCE-TW 2017
SP - 59
EP - 60
BT - 2017 IEEE International Conference on Consumer Electronics - Taiwan, ICCE-TW 2017
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 4th IEEE International Conference on Consumer Electronics - Taiwan, ICCE-TW 2017
Y2 - 12 June 2017 through 14 June 2017
ER -