Multi-sensor image registration has three basic problems. (1)Detected feature points from multi-sensor images are insufficient. (2)The spatial distribution of detected feature points is unbalanced. (3)The matching result of the detected points cannot easily achieve high performance. A new CCD-IR image registration algorithm is proposed in this study to address the aforementioned problems

which includes an adaptive Harris corner detection approach and a new feature point matching measure function based on normalized mutual information and gradient orientation. In adaptive feature point detection

the quantity and spatial distribution of the feature point are as synchronous as the objective function. Different detection thresholds are then automatically assigned to the feature points according to their spatial position information. Moreover

normalized mutual information is combined with gradient orientation in the feature matching process to construct a new matching measure function. Experimental results show that the proposed adaptive feature detection approach can provide sufficient and uniform distributed feature points. The proposed mew matching measure function improved the point matching success rate by approximately 20%

whereas it decreased the registration error approximately 50%. This study proposes a novel CCD-IR image registration algorithm

which is shown have low complexity

accuracy

and practicability. Thus

it can be applied to CCD-IR image fusion application.