Radial Fourier Analysis (RFA) Descriptor with Fourier-based Keypoint Orientation

Local keypoint detection and description have been widely employed in a large number of computer vision applications, such as image registration, object recognition and robot localisation. Since currently available local keypoint descriptors are based on the uses of statistical analysis in spatial domain, a local keypoint descriptor, namely Radial Fourier Analysis (RFA) keypoint descriptor, is developed with the use of spectral analysis in frequency domain. This descriptor converts image gradients around SIFT keypoints to frequency domain in order to extract the principle components of the gradients and derive distinctive descriptions for representing the keypoints. Additionally, a keypoint orientation estimate is also introduced to improve the rotational invariance of the descriptor rather than simply adopting SIFT keypoint orientations. The introduced orientation estimate employs the starting point normalisation of Fourier coefficients, which are frequency responses, to deduce rotating angles that ensure keypoint correspondences are aligned at the same orientation. Through experiments and comparisons, RFA descriptor demonstrates its outstanding and robust performances against various image distortions. Particularly, the descriptor has extremely reliable performances in dealing with the images, which are degraded by blurring, JPG compression and illumination changes. All these indicate that spectral analysis has strong potential for local keypoint description.