Enhanced GPT correlation for 2D projection transformation invariant template matching

Abstract

This paper describes a newly enhanced technique of 2D projection transformation invariant template matching, GPT (Global Projection Transformation) correlation. The key ideas are threefold. First, we show that arbitrary 2D projection transformation (PT) with a total of eight parameters can be approximated by a simpler expression. Second, using the simpler PT expression we propose an efficient computational model for determining sub-optimal eight parameters of PT that maximize a normalized cross-correlation value between a PT-superimposed input image and a template. Third, we obtain optimal eight parameters of PT via the successive iteration method. Experiments using templates and their artificially distorted images with random noise as input images demonstrate that the proposed method is far superior to the former GPT correlation method. Moreover, K-NN classification of handwritten numerals by the proposed method shows a high recognition accuracy through its distortion-tolerant template matching ability.