Motion‐blurred SIFT invariants based on sampling in image deformation space and univariate search

Abstract

Scale‐invariant feature transform (SIFT) operator is a widely used algorithm to detect local features in images, yet applying this algorithm for motion‐blurred image matching is difficult and inefficient. To resolve this issue, this study presents a motion‐blurred invariant SIFT algorithm that is based on sample matching in an image deformation reconstructed space. First, the motion‐blurred equation is deduced and its controlling parameters to reconstruct the deformation space are discretised. Second, the authors matched samples in the motion‐blurred space with the blurred image to identify maximal matching points and optimal parameters. In order to improve the searching efficiency, the authors used a univariate search technique combined with a variable step hill‐climbing method to determine the optimal matching. Together, the experimental results show that this improved algorithm has superior matching performance for motion‐blurred images.

Keywords

• motion-blurred SIFT invariants
• image deformation space sampling
• scale-invariant feature transform operator
• local feature detection
• motion-blurred image matching
• image deformation reconstructed space