Improved ORB Algorithm Using ThreePatch Method and Local Gray Difference This paper presents an improved Oriented Features from Accelerated Segment Test (FAST) and Rotated BRIEF (ORB) algorithm named ORB using threepatch and local gray difference (ORBTPLGD). ORB takes a breakthrough in realtime aspect. However, subtle changes of the image may greatly affect its final binary description. In this paper, the feature description generation is focused. On one hand, instead of pixel patch pairs comparison method used in present ORB algorithm, a threepixel patch group comparison method is adopted to generate the binary string. In each group, the gray value of the main patch is compared with that of the other two companion patches to determine the corresponding bit of the binary description. On the other hand, the present ORB algorithm simply uses the gray size comparison between pixel patch pairs, while ignoring the information of the gray difference value. In this paper, another binary string based on the gray difference information mentioned above is generated. Finally, the feature fusion method is adopted to combine the binary strings generated in the above two steps to generate a new feature description. Experiment results indicate that our improved ORB algorithm can achieve greater performance than ORB and some other related algorithms.

ORBPC featurebased image registration In remote sensing, intensity variation and rotational distortion are unavoidable issues for multimodal image registration. To address the intensity variation issue, RadiationInvariant Feature Transform (RIFT) featurebased image registration method was developed. The RIFT produced good results for horizontal and vertical transformation in multimodal images. However, the extracted features in RIFT produce some feature matching errors due to the rotational distortion and hence reduces the registration accuracy. Oriented FAST and Rotated BRIEF (ORB) can produce better rotation invariant features since it was basically designed to deal with the mentioned issue. However, it is not suitable for solving the intensity variation problem. Therefore, in this research work, we integrated the Phase Congruence (PC) to ORB as PC is robust to illumination and intensity variations while the ORB is robust to rotational distortion. Hence the combination of both methods has been utilized to overcome all the mentioned issues simultaneously in order to enhance the accuracy of the image registration. Experiments were conducted in order to validate the superiority of the proposed method over recently introduced RIFT method in terms of accuracy.