This paper describes a robust feature descriptor called the local self-similarity frequency (LSSF) for the multispectral RGB-NIR feature matching, which uses the frequency response of the local internal layout of self-similarities. A nonlinear relationship between multi-spectral image pairs makes conventional descriptors be sensitive to spectral deformation. To alleviate this problem, the LSSF employs a weighted correlation surface reducing the discrepancy between mul-tispectral images. Furthermore, the LSSF provides a rotation invariance exploiting the frequency response of maximal values on logpolar bins based on the fact that a cyclic shift on the log-polar representation leads only a phase shift in a frequency domain. Experimental results show that LSSF outperforms state-of-the-art descriptors in terms of a recognition rate for multispectral RGB-NIR image pairs.
|Title of host publication||2014 IEEE International Conference on Image Processing, ICIP 2014|
|Publisher||Institute of Electrical and Electronics Engineers Inc.|
|Number of pages||5|
|Publication status||Published - 2014 Jan 28|
|Name||2014 IEEE International Conference on Image Processing, ICIP 2014|
Bibliographical notePublisher Copyright:
© 2014 IEEE.
All Science Journal Classification (ASJC) codes
- Computer Vision and Pattern Recognition