Robust Wide Baseline Feature Point Matching Based on Scale Invariant Feature Descriptor

This paper proposed a robust point matching method for wide baseline in order to achieve a large number of correct correspondences and high accuracy. To cope with large variations of scale and rotation, a feature descriptor, that is robust to scale and view-point, is added to the feature detection phase and it is included in the equations of the correspondence matrix that is central to the matching algorithm. Furthermore, the image window for normalized cross correlation is modified with adaptive scale and orientation. At the same time we remove from the matrix all the proximity information about the distance between points' locations which is the source of mismatches. Thus, the proposed algorithm is invariant to changes of scale, rotation, light and partially invariant to viewpoint. Experimental results show that the proposed algorithm can be used for large scene variations and provide evidence of better performance.