Itamar Talmi, M.Sc. Thesis Seminar
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.