Robust affine-invariant isomerous pyramid feature and multi-description for point feature matching

Matching for high resolution image pairs with different viewpoints and distortions is a difficult work in remote sensing, photographing and computer vision etc. Robust Affine-Invariant Isomerous Pyramid Feature and Multi-Description for Point Feature Matching algorithm was proposed. Isomerous image pyramid was constructed by sinc convolution function series, the sinc convoluted gradient, the main direction and the strength of changes were devised for determining the normalized affine-invariant area around the key point, and the rotation-invariant projective accumulated amount and the weighted histograms were given for describing the multi-changes from the isomerous image pyramid at a special position and scale, and then, the matching was implemented based on the distribution parameters and reliability calculated by the distinctive corresponding points with big scale. Experiments show that, the new algorithm is robust for scale change, rotation, noisy, a certain degree of viewpoint difference and distortion, and the match scores are better than the state of the art matching algorithms.