A new SURF-based algorithm for robust registration of multimodal images data

In this paper, we propose an original algorithm allowing the registration of multimodal images. Indeed, the large and nonlinear intensity changes between different modalities often lead to mismatching of the interest points, which can alter the quality of the registration. Our method builds on the SURF for the identification and the description of the interest points. In order to boost the confidence on the subsequent matching operation of the registration, an applicable classification on both the distance and orientation SURF features is presented despite the low structural similarity of the multimodal acquisition. The proposed algorithm is tested and validated on a large dataset extracted from acquisitions on cultural heritage wall paintings that implement 4 imaging modalities covering UV, IR, visible and fluorescence. The comparison with conventional SIFT/SURF approaches, PIIFD, SURF-PIIFD-PRM,CFOG and HTCPR for registration highlights the effectiveness of the method and its robustness to outliers in SIFT/SURF descriptors.