A circular shifting binary descriptor for efficient rotation invariant image matching
Parastoo Soleimani, Kin Fun Li, David W. Capson · 2022 26th International Conference on Pattern Recognition (ICPR) · 2022
Image matching is a key component in computer vision and has many applications. In this work, we propose a circular shifting binary descriptor to increase the speed of rotation invariant image matching algorithms. We can compute the descriptors of the rotated image patches without rotating either the sample points or the image patch by circularly shifting the binary descriptor. Thus, operations such as multiplications and divisions from the orientation estimation step are eliminated from the image matching process which significantly reduces the number of operations for computing the descriptor. In addition, our experiments illustrate that the circular shifting binary descriptor shows limited rotation error in comparison with other descriptors such as ORB while attaining comparable mean average precision.