Image matching using GPT correlation associated with simplified HOG patterns

Abstract

GAT (Global Affine Transformation) and GPT (Global Projection Transformation) matchings proposed by Wakahara and Yamashita calculate the optimal AT (affine transformation) and PT (2D projection transformation), respectively. These image matching criteria realize deformation-tolerant matchings by maximizing the normalized cross-correlation between a template and a GAT/GPT-superimposed image. In order to shorten the calculation time, Wakahara and Yamashita also proposed the acceleration algorithms for GAT/GPT matchings. Later on, Wakahara et al. proposed the enhanced GPT matching to calculate the optimal PT parameters simultaneously which overcomes the incompatibility during the matching process. Zhang et al. figured out that these matching techniques do not take account of the conservation of the L2 norm, and introduced norm normalization factors that realize accurate and stable matchings. All these correlation-based matching techniques are well suited for “whole-to-whole” image matching, but are weak in “whole-to-part” image matching being cursed by complex backgrounds and noise. This research firstly proposes simplified HOG patterns for the enhanced GPT matching with norm normalization to obtain the robustness against noise and background. Secondly, this research also proposes the acceleration algorithm for the proposed matching criterion by creating several reference tables. Experiments using the Graffiti dataset show that the proposed method exhibits an outstanding matching ability compared with the original GPT correlation matching and the well-known combination of SURF feature descriptor and RANSAC algorithm. Furthermore, the computational complexity of the proposed method is significantly reduced below double figures via the acceleration algorithm.