A robust feature-based matching of two uncalibrated images

This paper presents a method that matches interest point features detected on two images taken from different view points. A new multi-scale Plessey corner detector (MPCD) is used to detect the interest points. The geometric constraint between two images is exploited as in [R. Deriche, et al., 1994]: the fundamental matrix is derived using least median of squares (LMedS) from an initial set of matches, and then it is used to guide new matches. However, we propose a new energy function that can approximate affine transformation in a more effective way. Then, the initial set of matches is derived by minimizing the energy function. As a result, our method can perform well even when the pose variation is large between the two images. We compare our method using the proposed MPCD against two standard corner detectors on image matching. Also we evaluate our proposed matching criterion against Zhang´s. Our method gave better results in both experiments on real face images.