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.
Deformable diversity similarity (DDIS) REF measures the diversity of feature matches between the two sets and is reported to outperform BBS by revealing the "deformation" of the NN field.
Template Matching with Deformable Diversity Similarity
2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
Computer Science