TY - GEN
T1 - Template matching with deformable diversity similarity
AU - Talmi, Itamar
AU - Mechrez, Roey
AU - Zelnik-Manor, Lihi
N1 - Publisher Copyright: © 2017 IEEE.
PY - 2017/11/6
Y1 - 2017/11/6
N2 - We propose a novel measure for template matching named Deformable Diversity Similarity - based on the diversity of feature matches between a target image window and the template. We rely on both local appearance and geometric information that jointly lead to a powerful approach for matching. Our key contribution is a similarity measure, that is robust to complex deformations, significant background clutter, and occlusions. Empirical evaluation on the most up-to-date benchmark shows that our method outperforms the current state-of-the-art in its detection accuracy while improving computational complexity.
AB - We propose a novel measure for template matching named Deformable Diversity Similarity - based on the diversity of feature matches between a target image window and the template. We rely on both local appearance and geometric information that jointly lead to a powerful approach for matching. Our key contribution is a similarity measure, that is robust to complex deformations, significant background clutter, and occlusions. Empirical evaluation on the most up-to-date benchmark shows that our method outperforms the current state-of-the-art in its detection accuracy while improving computational complexity.
UR - http://www.scopus.com/inward/record.url?scp=85044304076&partnerID=8YFLogxK
U2 - https://doi.org/10.1109/CVPR.2017.144
DO - https://doi.org/10.1109/CVPR.2017.144
M3 - منشور من مؤتمر
T3 - Proceedings - 30th IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2017
SP - 1311
EP - 1319
BT - Proceedings - 30th IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2017
T2 - 30th IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2017
Y2 - 21 July 2017 through 26 July 2017
ER -