Phase matching with multiresolution wavelet transform

An approach for corresponding points matching based on multiresolution wavelet transform and phase matching is presented in this paper. In binocular vision, disparity is an important cue to reconstruct the 3D structure of the scene from two or more images. Disparity for a stereo pair can be calculated by solving the stereo correspondence problem in image matching. For phase matching, the difference between the complex phases at corresponding points is used to find binocular disparity. In this approach, we construct a complex-valued wavelet kernel, which satisfied the requirement to the filter used in phase matching, through the Hilbert transform. The multiresolution analysis is combined with this kernel to get multiresolution phases of local spatial frequency. Then the phase matching is done based on these multiresolution phases. According to the relationship between image features and phase congruency, and also the relationship between phase congruency and local energy, we modified the iteration approaches in phase matching so as to give high confidence on image features.