Infrared and low-light-level image registration based on neighborhood difference chain code

The registration of the infrared (IR) image and the low-light-level (LLL) image remains a challenging problem due to poor dispersion of feature points, low correlation of structure and texture information. In this paper, we propose a method based on neighbourhood difference chain code to address the challenge. First we extracted the feature points of the images with the binary eight or sixteen-neighborhood information. And then construct the descriptor of the feature point by neighborhood difference chain code. At last we use the Euclidean distance to match the feature points. We adopt TNO and INO data sets to verify our method, and by comparing with four objective evaluation parameters obtained by other three methods. The result demonstrated that the proposed algorithm performs competitively, compared to the state-of-arts such as Harris, SIFT and SURF, in terms of accuracy of registration and speed.

Duke Scholars

Author Feng Tian DKU Faculty