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Gabriella Sanniti Di Baja - One of the best experts on this subject based on the ideXlab platform.

  • distance driven Skeletonization in voxel images
    IEEE Transactions on Pattern Analysis and Machine Intelligence, 2011
    Co-Authors: C Arcelli, Gabriella Sanniti Di Baja, L Serino
    Abstract:

    A distance-driven method to compute the Surface and curve Skeletons of 3D objects in voxel images is described. The method is based on the use of the ; weighted distance transform, on the detection of anchor points, and on the application of topology preserving removal operations. The obtained Surface and curve Skeletons are centered within the object, have the same topology as the object, and have unit thickness. The object can be almost completely recovered from the Surface Skeleton since this includes almost all of the centers of maximal balls of the object. Hence, the Surface Skeleton is a faithful representation. In turn, though only partial recovery is possible from the curve Skeleton, this still provides an appealing representation of the object.

  • A parallel algorithm to Skeletonize the distance transform of 3D objects
    Image and Vision Computing, 2009
    Co-Authors: Carlo Arcelli, Gabriella Sanniti Di Baja, Luca Serino
    Abstract:

    A 2-subiteration parallel algorithm is suggested to compute the Surface Skeleton of a 3D digital object represented by its D^6 distance transform, without resorting to directional processes. The algorithm is based on the use of two operators, with 3x3x3 and 2x2x2 support, that are, respectively, applied during the two subiterations to mark the voxels of the D^6 distance transform to be ascribed to the Skeleton. The resulting Surface Skeleton is centered within the object, is homotopic to the object and is fully reversible since it includes all centers of the maximal balls of the object.

  • ICIAR - From 3D Discrete Surface Skeletons to Curve Skeletons
    Lecture Notes in Computer Science, 2008
    Co-Authors: Carlo Arcelli, Gabriella Sanniti Di Baja, Luca Serino
    Abstract:

    An algorithm to compute the curve Skeleton of a 3D object starting from its Surface Skeleton is presented. The voxels of the Surface Skeleton are suitably classified to compute the geodesic distance transform of the Surface Skeleton and to identify anchor points. Voxels are examined in increasing distance order and are removed, provided that they are not anchor points and are not necessary to preserve topology. The resulting curve Skeleton is topologically equivalent to the Surface Skeleton and reflects its geometry.

  • Medial Representations - Discrete Skeletons from Distance Transforms in 2D and 3D
    Computational Imaging and Vision, 2008
    Co-Authors: Gunilla Borgefors, Ingela Nyström, Gabriella Sanniti Di Baja
    Abstract:

    In this chapter we present discrete methods to compute the digital Skeleton of shapes in 2D and 3D images. In 2D, the Skeleton is a set of curves, while in 3D it is a set of Surfaces and curves, the Surface Skeleton, or a set of curves, the curve Skeleton. A general scheme could, in principle, be followed for both 2D and 3D discrete Skeletonization. However, we will describe one approach for 2D Skeletonization, mainly based on marking, in the distance transform, the shape elements that should be assigned to the Skeleton, and another approach for 3D Skeletonization, mainly based on iterated element removal. In both cases, the distance transform of the image will play a key role to obtain Skeletons reflecting important shape features such as symmetry, elongation, and width.

  • DGCI - New removal operators for Surface Skeletonization
    Discrete Geometry for Computer Imagery, 2006
    Co-Authors: Carlo Arcelli, Gabriella Sanniti Di Baja, Luca Serino
    Abstract:

    New 3×3×3 operators are introduced to compute the Surface Skeleton of a 3D object by either sequential or parallel voxel removal We show that the operators can be employed without creating disconnections, cavities, tunnels and vanishing of object components A final thinning process, aimed at obtaining a unit-thick Surface Skeleton, is also described.

Alexandru Telea - One of the best experts on this subject based on the ideXlab platform.

  • Unified part-patch segmentation of mesh shapes using Surface Skeletons
    Skeletonization, 2017
    Co-Authors: Joost Koehoorn, Andrei C Jalba, Jacek Kustra, Cong Feng, Alexandru Telea
    Abstract:

    Curve Skeletons are well known for their ability to support part-based segmentation of 3D shapes. In contrast, Surface Skeletons have been only sparsely used for this task and, to our knowledge, only for voxel representations. We present here a method to use such Surface Skeletons to segment 3D meshes. For this, we extend a recent Surface-Skeleton-based method for part-based segmentation of voxel shapes [16] to efficiently handle high-resolution mesh shapes, on the one hand, and to compute part-based, patch-based, and hybrid part-and-patch segmentations, a result we refer to as unified segmentation. Our method can handle high-resolution 3D meshes with low memory and computational costs and produces segmentations that compare favorably with those delivered by other state-of-the-art methods. We demonstrate our method on a wide collection of both natural (articulated) and man-made (faceted) shapes.

  • Eurographics (Short Papers) - Robust gap removal from binary volumes
    2016
    Co-Authors: Andre Sobiecki, Andrei C Jalba, Alexandru Telea
    Abstract:

    We present a method for the robust detection and removal of cracks and holes from binary voxel shapes based on the shapes' Surface and curve Skeletons. For this, we first classify gaps or indentations in the input shape by their position with respect to the shape's curve Skeleton, into details (which should be preserved) and defects (which should be removed). Next, we remove defects, and preserve details, by using a local reconstruction process that uses the shape's Surface Skeleton. We illustrate our method by comparing it against classical morphological solutions on a wide collection of real-world shapes.

  • 3DOR@Eurographics - A descriptor for voxel shapes based on the Skeleton cut space
    2016
    Co-Authors: Cong Feng, Andrei C Jalba, Alexandru Telea
    Abstract:

    Two-dimensional medial axes and three-dimensional curve Skeletons have been long used for shape retrieval tasks. In contrast, and despite their ability to fully capture shape geometry and topology, three-dimensional Surface Skeletons have seen much less usage in this context. We present here a framework for shape matching and retrieval based on such Surface Skeletons. To this end, we construct a space of cuts generated by the Surface Skeleton, which has desirable invariance properties with respect to shape size, rotation, translation, pose, and noise. Next, we extract a histogram-based descriptor from this cut space, and discuss three different metrics to compare such histograms for shape retrieval. We illustrate our proposal by showing our descriptor's effectiveness in shape retrieval using a known shape-database benchmark.

  • an unified multiscale framework for planar Surface and curve Skeletonization
    IEEE Transactions on Pattern Analysis and Machine Intelligence, 2016
    Co-Authors: Andrei C Jalba, Andre Sobiecki, Alexandru Telea
    Abstract:

    Computing Skeletons of 2D shapes, and medial Surface and curve Skeletons of 3D shapes, is a challenging task. In particular, there is no unified framework that detects all types of Skeletons using a single model, and also produces a multiscale representation which allows to progressively simplify, or regularize, all Skeleton types. In this paper, we present such a framework. We model Skeleton detection and regularization by a conservative mass transport process from a shape’s boundary to its Surface Skeleton, next to its curve Skeleton, and finally to the shape center. The resulting density field can be thresholded to obtain a multiscale representation of progressively simplified Surface, or curve, Skeletons. We detail a numerical implementation of our framework which is demonstrably stable and has high computational efficiency. We demonstrate our framework on several complex 2D and 3D shapes.

  • VISAPP (3) - Shape segmentation using medial point clouds with applications to dental cast analysis
    Proceedings of the 9th International Conference on Computer Vision Theory and Applications, 2014
    Co-Authors: Jacek Kustra, Andrei C Jalba, Alexandru Telea
    Abstract:

    We present an automatic Surface segmentation method for dental cast scans based on the point density properties of the Surface Skeleton of such shapes. We produce quasi-flat segments separated by soft ridges, in contrast to classical Surface segmentation methods that require sharp ridges. We compute the Surface Skeleton by a fast 3D Skeletonization technique followed by its regularization using Surface geodesics. We segment the resulting Skeleton by a mean-shift approach and transfer the segmentation results back to the Surface. We demonstrate our results on an industrial dental-cast segmentation application and several generic 3D shape models.

Ingela Nyström - One of the best experts on this subject based on the ideXlab platform.

  • Medial Representations - Discrete Skeletons from Distance Transforms in 2D and 3D
    Computational Imaging and Vision, 2008
    Co-Authors: Gunilla Borgefors, Ingela Nyström, Gabriella Sanniti Di Baja
    Abstract:

    In this chapter we present discrete methods to compute the digital Skeleton of shapes in 2D and 3D images. In 2D, the Skeleton is a set of curves, while in 3D it is a set of Surfaces and curves, the Surface Skeleton, or a set of curves, the curve Skeleton. A general scheme could, in principle, be followed for both 2D and 3D discrete Skeletonization. However, we will describe one approach for 2D Skeletonization, mainly based on marking, in the distance transform, the shape elements that should be assigned to the Skeleton, and another approach for 3D Skeletonization, mainly based on iterated element removal. In both cases, the distance transform of the image will play a key role to obtain Skeletons reflecting important shape features such as symmetry, elongation, and width.

  • Skeletonization in 3D Discrete Binary Images
    2005
    Co-Authors: Ingela Nyström, Gabriella Sanniti Di Baja
    Abstract:

    Skeletonization is a way to reduce dimensionality of digital objects. Here, we present in detail an algorithm that computes the curve Skeleton of a solid object, i.e., an object without cavities, in a 3D binary image. The algorithm consists of three main steps. During the first step, the Surface Skeleton is detected, by directly marking in the distance transform of the object the voxels that should be assigned to the Surface Skeleton. The curve Skeleton is then computed by iteratively thinning the Surface Skeleton, during the second step. Finally, the third step is performed to reduce the curve Skeleton to unit width and to prune, in a controlled manner, some of its peripheral branches.

  • IWVF - Curve Skeletonization by Junction Detection in Surface Skeletons
    Lecture Notes in Computer Science, 2001
    Co-Authors: Ingela Nyström, Gabriella Sanniti Di Baja, Stina Svensson
    Abstract:

    We present an algorithm that, starting from the Surface Skeleton of a 3D solid object, computes the curve Skeleton. The algorithm is based on the detection of curves and junctions in the Surface Skeleton. It can be applied to any Surface Skeleton, including the case in which the Surface Skeleton is two-voxel thick.

  • Skeletonization of Volumetric Vascular Images—Distance Information Utilized for Visualization
    Journal of Combinatorial Optimization, 2001
    Co-Authors: Ingela Nyström, Örjan Smedby
    Abstract:

    This paper deals with two techniques to represent relevant information from volumetric vascular images in a more compact format. The images are obtained with contrast-enhanced magnetic resonance angiography (MRA). After segmentation of the vessels, the curve Skeleton is extracted by an algorithm based on the distance transformation. The algorithm first reduces the original object to a Surface Skeleton and then to a curve Skeleton, after which “pruning” can be performed to remove irrelevant small branches. Applying this procedure to MRA data from the pelvic arteries resulted in a good description of the tree structure of the vessels with a much smaller number of voxels. To detect stenoses, 2D projections such as maximum intensity projection (MIP) are usually employed, but these often fail to demonstrate a stenosis if the projection angle is not suitably chosen. A new presentation method surrounds each voxel in the distance-labeled curve Skeleton of the segmented vascular tree with a ball whose radius represents the minimum vessel radius at that level. Experiments with synthetic data indicate that stenoses invisible in an ordinary projection may be seen with this technique. It is concluded that the distance-labelled curve Skeleton seems to be useful for visualizing variations in vessel calibre and in the future possibly also for quantification of arterial stenoses.

  • Skeletonization of volumetric vascular images - Distance information utilized for visualization
    Journal of Combinatorial Optimization, 2001
    Co-Authors: Ingela Nyström, Örjan Smedby
    Abstract:

    This paper deals with two techniques to represent relevant information from volumetric vascular im- ages in a more compact format. The images are obtained with contrast-enhanced magnetic resonance angiography (MRA). After segmentation of the vessels, the curve Skeleton is extracted by an algorithm based on the distance transformation. The algorithm first reduces the original object to a Surface Skeleton and then to a curve Skeleton, after which "pruning" can be performed to remove irrelevant small branches. Applying this procedure to MRA data from the pelvic arteries resulted in a good description of the tree structure of the vessels with a much smaller number of voxels. To detect stenoses, 2D projections such as maximum intensity projection (MIP) are usually employed, but these often fail to demonstrate a stenosis if the projection angle is not suitably chosen. A new presentation method surrounds each voxel in the distance-labeled curve Skeleton of the segmented vascular tree with a ball whose radius represents the minimum vessel radius at that level. Experiments with synthetic data indicate that stenoses invisible in an ordinary projection may be seen with this technique. It is concluded that the distance-labelled curve Skeleton seems to be useful for visualizing variations in vessel calibre and in the future possibly also for quantification of arterial stenoses.

Gunilla Borgefors - One of the best experts on this subject based on the ideXlab platform.

  • Medial Representations - Discrete Skeletons from Distance Transforms in 2D and 3D
    Computational Imaging and Vision, 2008
    Co-Authors: Gunilla Borgefors, Ingela Nyström, Gabriella Sanniti Di Baja
    Abstract:

    In this chapter we present discrete methods to compute the digital Skeleton of shapes in 2D and 3D images. In 2D, the Skeleton is a set of curves, while in 3D it is a set of Surfaces and curves, the Surface Skeleton, or a set of curves, the curve Skeleton. A general scheme could, in principle, be followed for both 2D and 3D discrete Skeletonization. However, we will describe one approach for 2D Skeletonization, mainly based on marking, in the distance transform, the shape elements that should be assigned to the Skeleton, and another approach for 3D Skeletonization, mainly based on iterated element removal. In both cases, the distance transform of the image will play a key role to obtain Skeletons reflecting important shape features such as symmetry, elongation, and width.

  • Simplification of 3D Skeletons using distance information
    Vision Geometry IX, 2000
    Co-Authors: Gunilla Borgefors, Gabriella Sanniti Di Baja, Ingela Nystroem, Stina Svensson
    Abstract:

    We present a method to simplify the structure of the Surface Skeleton of a 3D object, such that loss of information can be kept under control. Our approach is to prune Surface border jaggedness by removing peripheral curves. The Surface border is detected and all curves belonging to it are identified. Then, distance information is used to distinguish the short curves, whose voxels are possibly deleted, provided that the topology is not changed. Our method is simple, fast, and can be applied also to two-voxel thick Surface Skeletons. It prunes only curves which correspond to minor features of the object, without shortening the remaining more significant curves. The structure of the Surface Skeleton becomes significantly simplified. The simplified set can be used directly for shape representation, or as input to curve Skeleton computation. If we extract the curve Skeleton from the simplified set, its structure is more manageable than if the curve Skeleton is obtained from the non-simplified set.

  • Skeletonizing volume objects part 2 from Surface to curve Skeleton
    Lecture Notes in Computer Science, 1998
    Co-Authors: Gunilla Borgefors, Ingela Nyström, Gabriella Sanniti Di Baja
    Abstract:

    Volume imaging techniques are becoming common and Skeletonization has begun to prove valuable for shape analysis also in 3D. In this paper, a method to reduce solid volume objects to their 3D curve Skeletons is presented. The method consists of two major steps. The first step is aimed at the computation of the Surface Skeleton, and is an improvement of a previous method. In the second step, the Surface Skeleton is further reduced to the 3D curve Skeleton. Our Skeletonization method preserves topology; no disconnections, holes or tunnels are created. It also preserves the general geometry of the object, especially in the case of elongated objects. Resulting Skeletons for a number of synthetic and real images are presented.

  • SSPR/SPR - Skeletonizing Volume Objects Part 2: From Surface to Curve Skeleton
    Lecture Notes in Computer Science, 1998
    Co-Authors: Gunilla Borgefors, Ingela Nyström, Gabriella Sanniti Di Baja
    Abstract:

    Volume imaging techniques are becoming common and Skeletonization has begun to prove valuable for shape analysis also in 3D. In this paper, a method to reduce solid volume objects to their 3D curve Skeletons is presented. The method consists of two major steps. The first step is aimed at the computation of the Surface Skeleton, and is an improvement of a previous method. In the second step, the Surface Skeleton is further reduced to the 3D curve Skeleton. Our Skeletonization method preserves topology; no disconnections, holes or tunnels are created. It also preserves the general geometry of the object, especially in the case of elongated objects. Resulting Skeletons for a number of synthetic and real images are presented.

  • SSPR - Surface Skeletonization of Volume Objects
    Advances in Structural and Syntactical Pattern Recognition, 1996
    Co-Authors: Gunilla Borgefors, Ingela Nyström, Gabriella Sanniti Di Baja
    Abstract:

    Tools for quantitative analysis of volume images are becoming more important, as volume images are becoming more common in a number of application fields, but especially in biomedical tomographic images at different scales. Here we present a method for reducing a volume (3D) object to a Surface Skeleton. The original object can be recovered from its Skeleton. The method is based on the notion of “multiple voxels,” derived from that of “multiple pixels” in the 2D case. It consists of two phases. During the first phase non-multiple voxels are iteratively removed. During the second phase, the remaining set of voxels is thinned to a set of one-voxel thick Surfaces and curves. This Skeletonization method requires only a small number of local (3×3×3 neighbourhood) operations per voxel, no extra memory and no look-up tables. It is suited both for sequential and parallel implementation. We exemplify the results of the method on a number of 128×128×128 images.

Stina Svensson - One of the best experts on this subject based on the ideXlab platform.