Local Descriptor by Zernike Moments for Real-time Keypoint Matching (2008)
Type of ContentConference Contributions - Published
PublisherUniversity of Canterbury. Human Interface Technology Laboratory.
AuthorsHwang, S., Billinghurst, M., Kim, W.show all
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.
CitationHwang, S., Billinghurst, M., Kim, W. (2008) Local Descriptor by Zernike Moments for Real-time Keypoint Matching. Sanya, China: 2008 International Congress on Image and Signal Processing (CISP2008), 27-30 May 2008.
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