  Image feature matching is to seek, localize and identify the similarities
across the images. The matched local features between different images can
indicate the similarities of their content. Resilience of image feature
matching to large view point changes is challenging for a lot of applications
such as 3D object reconstruction, object recognition and navigation, etc, which
need accurate and robust feature matching from quite different view points. In
this paper we propose a novel image feature matching algorithm, integrating our
previous proposed Affine Invariant Feature Detector (AIFD) and new proposed
Affine Invariant Feature Descriptor (AIFDd). Both stages of this new proposed
algorithm can provide sufficient resilience to view point changes. With
systematic experiments, we can prove that the proposed method of feature
detector and descriptor outperforms other state-of-the-art feature matching
algorithms especially on view points robustness. It also performs well under
other conditions such as the change of illumination, rotation and compression,
etc.
