An Adaptive Local Descriptor Embedding Zernike Moments for Image Matching
Bin Zhou, Xuemei Duan, Wei Wei, Dongjun Ye, Marcin Woźniak, Robertas Damaševičius · IEEE Access · 2019
Image matching is an important problem in computer vision and many technologies based on local descriptors have been developed. In this paper, we propose a novel local features descriptor based on an adaptive neighborhood and embedding Zernike moments. Instead of a fixed-size neighborhood, a size changeable neighborhood is introduced to detect the key-points and describe the features in the frame of Gaussian scale space. The radius is determined by the scale parameter of the key-point and the dominant direction is computed based on skew distribution fitting instead of the traditional eight-direction statistics. Then a 72-dimensional features vector based on a 3 × 3 grid is presented. A 19-dimensional vector consists of Zernike moments is applied to achieve better rotation invariance and finally contributes to a 91-dimensional descriptor. The accuracy and efficiency of proposed descriptor for image matching are verified by several numerical experiments.