| Popis: |
This paper describes a new method of image registration using distortion-tolerant template matching via multi-scale subwindow search. Here, we make full use of the GPT (Global Projection Transformation) correlation technique that maximizes a normalized cross-correlation value between an optimally 2D projection transformed template and a subwindow area of an input image. In particular, we propose to adaptively change the shape of the subwindow area from an original rectangle to its 2D projection transformed one through iterative matching process via the GPT correlation. We name this algorithm: adaptive subwindow control. Experiments made on the well-known datasets, Graffiti and Boat, show that the proposed method achieves a far superior ability of image registration under varying zoom, rotation, and viewpoints to the well-known feature-point based technique: a combination of ASIFT (Affine Scale-Invariant Feature Transform) and RANSAC (Random Sample Consensus). |
