University of Canterbury. Human Interface Technology Laboratory.
Doi
Abstract
This paper presents a real-time keypoint matching
algorithm using a local descriptor derived by Zernike
moments. From an input image, we find a set of keypoints
by using an existing corner detection algorithm.
At each keypoint we extract a fixed size image patch
and compute a local descriptor derived by Zernike
moments. The proposed local descriptor is invariant to
rotation and illumination changes. In order to speed
up the computation of Zernike moments, we compute
the Zernike basis functions in advance and store them
in a set of lookup tables. The matching is performed
with an Approximate Nearest Neighbor (ANN) method
and refined by a RANSAC algorithm. In the
experiments we confirmed that videos of frame size
320×240 with the scale, rotation, illumination and
even 3D viewpoint changes are processed at 25~30Hz
using the proposed method. Unlike existing keypoint
matching algorithms, our approach also works in realtime
for registering a reference image