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- W4311358051 abstract "Image matching is an important research topic in computer vision and image processing. However, existing quantum algorithms mainly focus on accurate matching between template pixels, and are not robust to changes in image location and scale. In addition, the similarity calculation of the matching process is a fundamentally important issue. Therefore, this paper proposes a hybrid quantum algorithm, which uses the robustness of SIFT (scale-invariant feature transform) to extract image features, and combines the advantages of quantum exponential storage and parallel computing to represent data and calculate feature similarity. Finally, the quantum amplitude estimation is used to extract the measurement results and realize the quadratic acceleration of calculation. The experimental results show that the matching effect of this algorithm is better than the existing classical architecture. Our hybrid algorithm broadens the application scope and field of quantum computing in image processing." @default.
- W4311358051 created "2022-12-25" @default.
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- W4311358051 date "2022-12-13" @default.
- W4311358051 modified "2023-09-25" @default.
- W4311358051 title "A Hybrid Quantum Image-Matching Algorithm" @default.
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- W4311358051 doi "https://doi.org/10.3390/e24121816" @default.
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