Hierarchical Region-Based Image Registration in Scale Space

This paper presents a new method for image registration for real natural scenes. The method is based on the observation that most natural scenes are actually 3-D. If the assumption of weak perspective is violated, the error in image registration induced by parallax is increased, leading artifacts or blurs in image mosaic. Our method first applies the affine-invariant point detector in scale space. After clustering feature point pairs, an initial global transformation is formed based on majority correspondence. The global transformation is evaluated in each region at certain scale determined by inliers. The global model is optimized for local registration by minimizing least square error. This method is more robust than standard image registration algorithms on images subject to uncalibrated camera motion