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Image registration using 2D projection transformation invariant GPT correlation
Author(s): Toru Wakahara; Shizhi Zhang; Yukihiko Yamashita
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Paper 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).

Paper Details

Date Published: 22 March 2019
PDF: 6 pages
Proc. SPIE 11049, International Workshop on Advanced Image Technology (IWAIT) 2019, 110493K (22 March 2019); doi: 10.1117/12.2517185
Show Author Affiliations
Toru Wakahara, Hosei Univ. (Japan)
Shizhi Zhang, Tokyo Institute of Technology (Japan)
Yukihiko Yamashita, Tokyo Institute of Technology (Japan)


Published in SPIE Proceedings Vol. 11049:
International Workshop on Advanced Image Technology (IWAIT) 2019
Qian Kemao; Kazuya Hayase; Phooi Yee Lau; Wen-Nung Lie; Yung-Lyul Lee; Sanun Srisuk; Lu Yu, Editor(s)

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