22 October 2010 Modeling of image matching accuracy with image metrics based on least squares matching algorithm
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The determination of conjugate points in a stereo image pair, i.e. image matching, is the critical step to realize automatic surveying and recognition in digital photogrammetric processing. The accuracy of image matching is closely related to specific matching algorithm as well as images. In this paper, the qualitative and quantitative relationships between the matching accuracy and the image metrics are studied at the basic of Least Squares Image Matching algorithm (LSIMA). Firstly, the algorithm is deduced mathematically, and then the main image metrics affecting the matching accuracy are presented, including total variation (TV) metric and difference of signal-to-noise ratio (DSNR) metric. Subsequently, variations of matching accuracy with TV and DSNR are analyzed, and mathematical model between them is developed. Studies show that the matching accuracy presents the natural exponential rule along with TV and DSNR of image pairs. Besides, parameters of the model are estimated and the model is verified by simulation experiments. Finally, the correctness of the model is verified using real remote sensing images. Experimental results demonstrate the robustness and accuracy of the proposed model.
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Xi-yang Zhi, Wei Zhang, Fan-jiao Tan, Qing-yu Hou, Yi-ming Cao, "Modeling of image matching accuracy with image metrics based on least squares matching algorithm", Proc. SPIE 7658, 5th International Symposium on Advanced Optical Manufacturing and Testing Technologies: Optoelectronic Materials and Devices for Detector, Imager, Display, and Energy Conversion Technology, 765857 (22 October 2010); doi: 10.1117/12.865684; https://doi.org/10.1117/12.865684

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