Image registration using 2D projection transformation invariant GPT correlation

Toru Wakahara; Shizhi Zhang; Yukihiko Yamashita

doi:10.1117/12.2517185

22 March 2019 Image registration using 2D projection transformation invariant GPT correlation

Toru Wakahara, Shizhi Zhang, Yukihiko Yamashita

Proceedings Volume 11049, International Workshop on Advanced Image Technology (IWAIT) 2019; 110493K (2019) https://doi.org/10.1117/12.2517185
Event: 2019 Joint International Workshop on Advanced Image Technology (IWAIT) and International Forum on Medical Imaging in Asia (IFMIA), 2019, Singapore, Singapore

Abstract

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).

Citation Download Citation

Toru Wakahara, Shizhi Zhang, and Yukihiko Yamashita "Image registration using 2D projection transformation invariant GPT correlation", Proc. SPIE 11049, International Workshop on Advanced Image Technology (IWAIT) 2019, 110493K (22 March 2019); https://doi.org/10.1117/12.2517185

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