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Image feature matching is to seek, localize and identify the similaritiesacross the images. The matched local features between different images canindicate the similarities of their content. Resilience of image featurematching to large view point changes is challenging for a lot of applicationssuch as 3D object reconstruction, object recognition and navigation, etc, whichneed accurate and robust feature matching from quite different view points. Inthis paper we propose a novel image feature matching algorithm, integrating ourprevious proposed Affine Invariant Feature Detector (AIFD) and new proposedAffine Invariant Feature Descriptor (AIFDd). Both stages of this new proposedalgorithm can provide sufficient resilience to view point changes. Withsystematic experiments, we can prove that the proposed method of featuredetector and descriptor outperforms other state-of-the-art feature matchingalgorithms especially on view points robustness. It also performs well underother conditions such as the change of illumination, rotation and compression,etc.

A Resilient Image Matching Method with an Affine Invariant Feature Detector and Descriptor