| Title |
An adaptive local descriptor embedding zernike moments for image matching / |
| Authors |
Zhou, Bin ; Duan, Xue-Mei ; Wei, Wei ; Ye, Dong-Jun ; Wozniak, Marcin ; Damaševičius, Robertas |
| DOI |
10.1109/ACCESS.2019.2960203 |
| Full Text |
|
| Is Part of |
IEEE Access.. Piscataway, NJ : IEEE. 2019, vol. 7, p. 183971-183984.. ISSN 2169-3536 |
| Keywords [eng] |
adaptive neighborhood ; difference of Gaussian ; dominant direction fitting ; Scale invariance ; Zernike moment |
| Abstract [eng] |
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\times 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. |
| Published |
Piscataway, NJ : IEEE |
| Type |
Journal article |
| Language |
English |
| Publication date |
2019 |
| CC license |
|
