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• W1997667760 abstract "Interpreting an image as a function on a compact subset of the Euclidean plane, we get its scale-space by diffusion, spreading the image over the entire plane. This generates a 1-parameter family of functions alternatively defined as convolutions with a progressively wider Gaussian kernel. We prove that the corresponding 1-parameter family of persistence diagrams have norms that go rapidly to zero as time goes to infinity. This result rationalizes experimental observations about scale-space. We hope this will lead to targeted improvements of related computer vision methods." @default.
• W1997667760 created "2016-06-24" @default.
• W1997667760 creator A5056248574 @default.
• W1997667760 creator A5069999967 @default.
• W1997667760 date "2011-11-01" @default.
• W1997667760 modified "2023-09-25" @default.
• W1997667760 title "Diffusion runs low on persistence fast" @default.
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• W1997667760 doi "https://doi.org/10.1109/iccv.2011.6126271" @default.
• W1997667760 hasPublicationYear "2011" @default.
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