Computing refined skeletal features from medial point clouds

Kustra, Jacek; Jalba, Andrei C.; Telea, Alexandru

doi:10.1016/j.patrec.2015.05.007

Cited by 13 publications

(18 citation statements)

References 46 publications

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“…3), Giblin and Kimia's skeletal point classification [GK04] can be elegantly written in terms of the cardinality of the feature transform [RvWT08b,KJT15]. 3), Giblin and Kimia's skeletal point classification [GK04] can be elegantly written in terms of the cardinality of the feature transform [RvWT08b,KJT15].…”

Section: Surface-skeleton Correspondencementioning

confidence: 99%

“…4.3 next) or local thickness estimation [JKT13], using them for other tasks like shape segmentation and shape classification needs complicated heuristics [ATC * 08, CLK09,KJT15]. While such point clouds directly support several tasks like garbing (Sec.…”

Section: Reconstructing Skeletal Structurementioning

confidence: 99%

“…man-made objects [RT08b,CLK09,KJT15]. Similarly, surface skeletons are used for patch-based segmentation of edge-rich shapes, e.g.…”

Section: Geometry Processingmentioning

confidence: 99%

“…the printability and mechanical resistance of 3D shapes [DK09,JKT13], anatomic tissue resistance [YP03], characterize anatomic shapes [NSK * 97], and find tubular shape parts [MPS * 04]. Separately, finding input-surface points that correspond to the boundaries of the medial surface via the feature transform allows robustly finding edges even for complex noisy shapes, which serves shape classification [RJT08,KJT15]. Separately, finding input-surface points that correspond to the boundaries of the medial surface via the feature transform allows robustly finding edges even for complex noisy shapes, which serves shape classification [RJT08,KJT15].…”

Section: Shape Metrologymentioning

confidence: 99%

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3D Skeletons: A State‐of‐the‐Art Report

Tagliasacchi

Delame

Spagnuolo

et al. 2016

Computer Graphics Forum

Self Cite

212

152

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International audienceGiven a shape, a skeleton is a thin centered structure which jointly describes the topology and the geometry of the shape. Skeletons provide an alternative to classical boundary or volumetric representations, which is especially effective for applications where one needs to reason about, and manipulate, the structure of a shape. These skeleton properties make them powerful tools for many types of shape analysis and processing tasks. For a given shape, several skeleton types can be defined, each having its own properties, advantages, and drawbacks. Similarly, a large number of methods exist to compute a given skeleton type, each having its own requirements, advantages, and limitations. While using skeletons for two-dimensional (2D) shapes is a relatively well covered area, developments in the skeletonization of three-dimensional (3D) shapes make these tasks challenging for both researchers and practitioners. This survey presents an overview of 3D shape skeletonization. We start by presenting the definition and properties of various types of 3D skeletons. We propose a taxonomy of 3D skeletons which allows us to further analyze and compare them with respect to their properties. We next overview methods and techniques used to compute all described 3D skeleton types, and discuss their assumptions, advantages, and limitations. Finally, we describe several applications of 3D skeletons, which illustrate their added value for different shape analysis and processing tasks

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Section: Surface-skeleton Correspondencementioning

confidence: 99%

Section: Reconstructing Skeletal Structurementioning

confidence: 99%

“…man-made objects [RT08b,CLK09,KJT15]. Similarly, surface skeletons are used for patch-based segmentation of edge-rich shapes, e.g.…”

Section: Geometry Processingmentioning

confidence: 99%

Section: Shape Metrologymentioning

confidence: 99%

See 2 more Smart Citations

3D Skeletons: A State‐of‐the‐Art Report

Tagliasacchi

Delame

Spagnuolo

et al. 2016

Computer Graphics Forum

Self Cite

212

152

View full text Add to dashboard Cite

show abstract

“…Chang et al compute shape medial surfaces, separate their manifolds, and back project each manifold on the shape surface to nd a segment [8]. A similar segmentation method, using high-resolution point-cloud skeletons computed on the GPU [17] along the method of Reniers et al [40], is proposed in [22]. Similar high-resolution surface and curve skeletons can be computed in voxel space using an advection model [18].…”

Section: Related Workmentioning

confidence: 99%

Improved Part-Based Segmentation of Voxel Shapes by Skeleton Cut Spaces

Feng

Jalba

Telea

2016

Mathematical Morphology - Theory and Applications

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Abstract:We present a re ned method for part-based segmentation of voxel shapes by constructing partitioning cuts from every voxel of the shape's medial surface. Our cuts have several desirable propertiessmoothness, tightness, and orientation with respect to the shape's local symmetry axis, making it a good segmentation tool. We analyze the space of all cuts created for a given shape and detect cuts which are good segment borders. We present a detailed analysis of the parameter space of our method, which yields good preset values for all its parameters. Our method is robust to noise, pose invariant, independent on the shape geometry and genus, and is simple to implement. We demonstrate our method for both automatic and interactive segmentation on a wide selection of 3D shapes.

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