Image matching using GPT correlation associated with simplified HOG patterns
Image matching using GPT correlation associated with simplified HOG patterns
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DOI：
10.1109/ipta.2017.8310122
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发表时间：
2017-11
期刊：
影响因子：
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通讯作者：
Shizhi Zhang;T. Wakahara;Yukihiko Yamashita
中科院分区：
文献类型：
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作者：
Shizhi Zhang;T. Wakahara;Yukihiko Yamashita
Wakahara和Yamashita提出的GAT（全局仿射变换）和GPT（全局投影变换）匹配分别计算最佳AT（仿射变换）和PT（2D投影变换）。这些图像匹配准则通过最大化模板和GAT/GPT叠加图像之间的归一化互相关来实现变形容忍匹配。为了缩短计算时间，Wakahara和Yamashita还提出了GAT/GPT匹配的加速算法。后来，Wakahara等人提出了增强的GPT匹配，以同时计算最佳PT参数，克服了匹配过程中的不兼容性。Zhang等人指出，这些匹配技术没有考虑L2范数的守恒性，并引入了范数归一化因子，实现了准确和稳定的匹配。这些基于相关性的匹配技术都很适合于“整体对整体”的图像匹配，但在“整体对部分”的图像匹配中，由于背景复杂，噪声较大，匹配效果较差。本研究首先提出简化的HOG模式，以增强GPT匹配与范数归一化，以获得对噪声和背景的鲁棒性。其次，本研究亦提出加速演算法，借由建立数个参考表，以加速所提出的匹配准则。实验结果表明，该方法与传统的GPT相关匹配算法以及SURF特征描述子与RANSAC算法的结合相比，具有更好的匹配性能.此外，所提出的方法的计算复杂度显着降低到两位数以下，通过加速算法。
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.