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- W4281665887 abstract "As an important feature of images, the structure tensor (ST) provides properties of the local image intensities. However, it is a challenging task to analyse images directly using STs since STs are 2nd-order symmetric positive semi-definite matrices. In this paper, we define a robust intrinsic feature of images using ST. In addition, using our feature, we propose an improved image similarity measure. By converting the ST into a symmetric positive definite (called tensor) matrix, we define our feature using the extended geodesic distance of tensors calculated in a Riemannian manifold. Our feature shows the essential natural properties of ST and images as tensors are analysed on the tensor manifold. Moreover, defined by the global embedded geometry of the structure tensor, our feature provides a stable intrinsic property of images. The experiments show that our feature performs well in representing the essential attributes of images, especially the edges and important structures. It also shows that our image similarity measure can accurately detect similar images or patches." @default.
- W4281665887 created "2022-06-13" @default.
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- W4281665887 date "2022-08-01" @default.
- W4281665887 modified "2023-10-18" @default.
- W4281665887 title "A robust intrinsic feature of images derived from the tensor manifold" @default.
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- W4281665887 doi "https://doi.org/10.1016/j.patrec.2022.06.002" @default.
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