2021
DOI: 10.1049/ipr2.12180
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Scale space Radon transform

Abstract: An extension of Radon transform by using a measure function capturing the user need is proposed. The new transform, called scale space Radon transform, is devoted to the case where the embedded shape in the image is not filiform. A case study is brought on a straight line and an ellipse where the SSRT behaviour in the scale space and in the presence of noise is deeply analyzed. In order to show the effectiveness of the proposed transform, the experiments have been carried out, first, on linear and elliptical s… Show more

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Cited by 12 publications
(14 citation statements)
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“…6.e, the Shepp-Logan image gray level profiles, for unnoisy image, noisy image and reconstructed images, are highlighted. We remark, at first, that the effect of noise is attenuated for all the used methods, which is expected since RT and SSRT are robust to additive noise [14,1]. By examining the profiles, it appears that the smoothing effect is more pronounced for SSRT-FBP method w.r.t.…”
Section: Methodsmentioning
confidence: 65%
See 2 more Smart Citations
“…6.e, the Shepp-Logan image gray level profiles, for unnoisy image, noisy image and reconstructed images, are highlighted. We remark, at first, that the effect of noise is attenuated for all the used methods, which is expected since RT and SSRT are robust to additive noise [14,1]. By examining the profiles, it appears that the smoothing effect is more pronounced for SSRT-FBP method w.r.t.…”
Section: Methodsmentioning
confidence: 65%
“…Scale Space Radon Transform (SSRT), introduced recently in [1], is a matching of an embedded shape in an image and the Gaussian kernel. The choice of the latter should satisfy several requirements such as nice behaviour in scale space, robustness to noise, uniqueness of transform maximum, etc.…”
Section: Introductionmentioning
confidence: 99%
See 1 more Smart Citation
“…The Scale Space Radon Transform (SSRT), Šf , of an image f , is a matching of a kernel and an embedded parametric shape in this image. If the parametric shape is a line parametrized by the location parameter ρ and the angle θ and the kernel is a Gaussian one, then Šf is given by [14] Šf (ρ, θ, σ) = 1 √ 2πσ X Y f (x, y)e − (x cos θ+y sin θ−ρ) 2 2σ 2 dxdy…”
Section: Scale Space Radon Transformmentioning
confidence: 99%
“…Recently, authors in [14] have proposed a transform called the Scale Space Radon Transform (SSRT), which can be viewed as a significant generalized form of the Radon transform. They have shown that this transform can be used to detect elegantly and accurately thick lines and ellipses through an embedded kernel tuned by a scale space parameter.…”
Section: Introductionmentioning
confidence: 99%