Euclidean skeleton via centre-of-maximal-disc extraction

Arcelli, Carlo; Baja, Gabriella Sanniti di

doi:10.1016/0262-8856(93)90055-l

Cited by 84 publications

(33 citation statements)

References 8 publications

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“…Thinning algorithms have a clear advantage in terms of simplicity. However, their performance is not invariant under Euclidean transformation unless weighted distance functions are used to approximate the Euclidean distance or the Eu-clidean distance itself are used [3]. Curve evolution methods, on the other hand, are invariant under Euclidean transformation, but require a functional description of the boundary curve.…”

Section: Introductionmentioning

confidence: 99%

Correcting Curvature-Density Effects in the Hamilton–Jacobi Skeleton

Torsello

Hancock

2006

IEEE Trans. on Image Process.

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Article:Hancock, E.R. orcid.org/0000-0003- 4496-2028 and Torsello, A. (2006) ReuseItems deposited in White Rose Research Online are protected by copyright, with all rights reserved unless indicated otherwise. They may be downloaded and/or printed for private study, or other acts as permitted by national copyright laws. The publisher or other rights holders may allow further reproduction and re-use of the full text version. This is indicated by the licence information on the White Rose Research Online record for the item. TakedownIf you consider content in White Rose Research Online to be in breach of UK law, please notify us by emailing eprints@whiterose.ac.uk including the URL of the record and the reason for the withdrawal request. Abstract-The Hamilton-Jacobi approach has proven to be a powerful and elegant method for extracting the skeleton of two-dimensional (2-D) shapes. The approach is based on the observation that the normalized flux associated with the inward evolution of the object boundary at nonskeletal points tends to zero as the size of the integration area tends to zero, while the flux is negative at the locations of skeletal points. Nonetheless, the error in calculating the flux on the image lattice is both limited by the pixel resolution and also proportional to the curvature of the boundary evolution front and, hence, unbounded near endpoints. This makes the exact location of endpoints difficult and renders the performance of the skeleton extraction algorithm dependent on a threshold parameter. This problem can be overcome by using interpolation techniques to calculate the flux with subpixel precision. However, here, we develop a method for 2-D skeleton extraction that circumvents the problem by eliminating the curvature contribution to the error. This is done by taking into account variations of density due to boundary curvature. This yields a skeletonization algorithm that gives both better localization and less susceptibility to boundary noise and parameter choice than the Hamilton-Jacobi method.

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Section: Introductionmentioning

confidence: 99%

Correcting Curvature-Density Effects in the Hamilton–Jacobi Skeleton

Torsello

Hancock

2006

IEEE Trans. on Image Process.

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“…[1]). d is the distance from the contour to the skeleton and Ψ(·) is a signed distance map from the contour C that surrounds the foreground region f. The signed distance map Ψ is a composite function, obtained by applying the signed distance transform D, (e.g.…”

Section: Shape Representation and Regularizationmentioning

confidence: 99%

“…The signed distance skeletonization process, S • Ψ, utilizes the signed distance map by first identifying the local minima in the signed distance map [1], i.e. X min = {x|Ψ(x) < Ψ(x i ) ∃x i : | x, x i | ≤ 1, Ψ(x) < 0}.…”

Section: Shape Representation and Regularizationmentioning

confidence: 99%

Tracking with Active Contours Using Dynamically Updated Shape Information

Chiverton

Xie

Mirmehdi

2008

Procedings of the British Machine Vision Conference 2008

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An active contour based tracking framework is described that generates and integrates dynamic shape information without having to learn a priori shape constraints. This dynamic shape information is combined with fixative photometric foreground model matching and background mismatching. Boundary based optical flow is also used to estimate the location of the object in each new video frame, incorporating Procrustes based shape alignment. Promising results under complex deformations of shape, varied levels of noise, and close-to-complete occlusion in the presence of complex textured backgrounds are presented.

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“…4. The skeleton obtained in one inspection of the DT computed with w e =3 and w v =4 [13,14]. Also in this case, the skeleton is nearly-thin and the same final thinning already discussed can be employed to obtain the unit-wide skeleton.…”

Section: Figmentioning

confidence: 99%