Abstract: Template matching has many applications in signal processing, image processing, pattern recognition, and video compressing. It fund a desired template in the large reference image by sliding the template window in a pixel-by-pixel basis, computing the degree of similarity between them, and searching position with the largest similarity measurement.It is computationally expensive to search for every possible position of the template window within the larger reference image. When having a rotation between the template and the reference image, the conventional template matcking algorithm described above is not practical for real-time processing. In this paper, a point matching algorithm was proposed to match the rotated template, which included: firstly, the feature points were detected by Harris detector, then the facet model was used to approximate locally the image intensity function, and the rotation-invariability of the feature point was extracted, finally, the transformation(translation and rotation) was obtained by matching feature points. Resups have shown the efficacy of the proposed method.

Key words: Image matching, Point feature, Corner point, Rotation invariance