The Experts below are selected from a list of 365907 Experts worldwide ranked by ideXlab platform
Gabriella Sanniti Di Baja - One of the best experts on this subject based on the ideXlab platform.
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distance driven Skeletonization in voxel images
IEEE Transactions on Pattern Analysis and Machine Intelligence, 2011Co-Authors: C Arcelli, Gabriella Sanniti Di Baja, L SerinoAbstract: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.
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Hierarchical decomposition of multiscale Skeletons
IEEE Transactions on Pattern Analysis and Machine Intelligence, 2001Co-Authors: Gunilla Borgefors, Giuliana Ramella, Gabriella Sanniti Di BajaAbstract:The paper presents a novel procedure to hierarchically decompose a multiscale discrete Skeleton. The Skeleton is a linear pattern representation that is generally recognized as a good shape descriptor. For discrete images, the discrete Skeleton is often preferable. Multiresolution representations are convenient for many image analysis tasks. Our resulting Skeleton decomposition shows two different types of hierarchy. The first type of hierarchy is one of different scales, as the original pattern is converted into an AND-pyramid and the Skeleton is computed for each resolution level. The second type of hierarchy is established at each level of the pyramid by identifying and ranking Skeleton subsets according to their permanence, where permanence is a property intrinsically related to local pattern thickness. To achieve the decomposition, both bottom-up and top-down analysis in the sense of moving from higher to lower resolution and vice versa are used. The bottom-up analysis is used to ensure that a part of the Skeleton that is connected at a higher resolution level is also connected (if at all present) in the next, lower resolution level. The top-down analysis is used to build the permanence hierarchy ranking the Skeleton components. Our procedure is based on the use of (3/spl times/3) local operations in digital images, so it is fast and easy to implement. This Skeleton decomposition procedure is most effective on patterns having different thickness in different regions. A number of examples of decompositions of multiscale Skeletons (with and without loops) are shown. The Skeletons are, in most cases, nicely decomposed into meaningful parts. The procedure is general and not limited to any specific application.
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Skeletonizing volume objects part 2 from surface to curve Skeleton
Lecture Notes in Computer Science, 1998Co-Authors: Gunilla Borgefors, Ingela Nyström, Gabriella Sanniti Di BajaAbstract: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.
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SSPR/SPR - Skeletonizing Volume Objects Part 2: From Surface to Curve Skeleton
Lecture Notes in Computer Science, 1998Co-Authors: Gunilla Borgefors, Ingela Nyström, Gabriella Sanniti Di BajaAbstract: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.
Alexandru Telea - One of the best experts on this subject based on the ideXlab platform.
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Co-Skeletons: Consistent curve Skeletons for shape families
Computers & Graphics, 2020Co-Authors: Xingyu Chen, Alexandru Telea, Jiří KosinkaAbstract:Abstract We present co-Skeletons, a new method that computes consistent curve Skeletons for 3D shapes from a given family. We compute co-Skeletons in terms of sampling density and semantic relevance, while preserving the desired characteristics of traditional, per-shape curve Skeletonization approaches. We take the curve Skeletons extracted by traditional approaches for all shapes from a family as input, and compute semantic correlation information of individual Skeleton branches to guide an edge-pruning process via Skeleton-based descriptors, clustering, and a voting algorithm. Our approach achieves more concise and family-consistent Skeletons when compared to traditional per-shape methods. We show the utility of our method by using co-Skeletons for shape segmentation and shape blending on real-world data.
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multiscale 2d medial axes and 3d surface Skeletons by the image foresting transform
Skeletonization#R##N#Theory Methods and Applications, 2017Co-Authors: Alexandre X Falcao, Jacek Kustra, Cong Feng, Alexandru TeleaAbstract:Skeletons are simplified shape representations with many applications involving image processing, analysis, and visualization. A fundamental problem in shape Skeletonization is the sensitivity of the Skeletons to small perturbations of the input shape, which leads to the appearance of spurious branches. By assigning an importance metric to every Skeleton point, one encodes the scale of the shape details. Subsequently, simplified Skeletons can be obtained by simply thresholding at continuous values of that metric. Such a multiscale regularization procedure should ideally produce one-spel-wide Skeletons in all scales. In this chapter, we present a new method based on the image foresting transform framework, which achieves this result for medial axes of 2D shapes and for surface Skeletons of 3D shapes. Our approach relies on simple and efficient algorithms, faster than several methods based on the same importance metric, simpler than others, far less sensitive to numerical noise than a recent one, and attains similar quality of centeredness, smoothness, thinness, and ease to simplify the Skeleton. Such a conclusion is substantiated with a comparative analysis on a wide set of 2D and 3D real-word shapes against its multiscale counterparts.
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an unified multiscale framework for planar surface and curve Skeletonization
IEEE Transactions on Pattern Analysis and Machine Intelligence, 2016Co-Authors: Andrei C Jalba, Andre Sobiecki, Alexandru TeleaAbstract: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.
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3D Skeletons: A State-of-the-Art Report
Computer Graphics Forum, 2015Co-Authors: Andrea Tagliasacchi, Nina Amenta, Thomas Delame, Michela Spagnuolo, Alexandru TeleaAbstract:Given 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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surface and curve Skeletonization of large 3d models on the gpu
IEEE Transactions on Pattern Analysis and Machine Intelligence, 2013Co-Authors: Andrei C Jalba, Jacek Kustra, Alexandru TeleaAbstract:We present a GPU-based framework for extracting surface and curve Skeletons of 3D shapes represented as large polygonal meshes. We use an efficient parallel search strategy to compute point-cloud Skeletons and their distance and feature transforms (FTs) with user-defined precision. We regularize Skeletons by a new GPU-based geodesic tracing technique which is orders of magnitude faster and more accurate than comparable techniques. We reconstruct the input surface from Skeleton clouds using a fast and accurate image-based method. We also show how to reconstruct the skeletal manifold structure as a polygon mesh and the curve Skeleton as a polyline. Compared to recent Skeletonization methods, our approach offers two orders of magnitude speed-up, high-precision, and low-memory footprints. We demonstrate our framework on several complex 3D models.
Jong Seong Kang - One of the best experts on this subject based on the ideXlab platform.
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inhibitory lignans against nfat transcription factor from acanthopanax koreanum
Archives of Pharmacal Research, 2004Co-Authors: Guanghai Shen, Jong Seong KangAbstract:Three lignans isolated from the roots ofA. koreanum (Araliaceae), namely eleutheroside E (1), tortoside A (2), and hemiariensin (4), were evaluated for their ability to inhibit NFAT transcription factor. Of these compounds, compound4, possessing a diarylbutane Skeleton, exhibited potent inhibitory activity against NFAT transcription factor (IC50: 36.3 +- 2.5 μM). However, the activities of 1 (IC50: > 500 μM) and2 (IC50: 136.1 +- 9.4 μM), which possess bisaryldioxabicy-clooctane Skeletons, were lower. As the lignan derivatives of the same Skeletons, hinokinin (5) and (-)-yatein (6) with diarylbutane Skeletons and (+)-syringaresinol (3) with a bisaryldioxabicy-clooctane Skeleton were also studied for their inhibitory effects on NFAT transcription factor.
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Inhibitory lignans against NFAT transcription factor fromacanthopanax koreanum
Archives of Pharmacal Research, 2004Co-Authors: Guanghai Shen, Jong Seong KangAbstract:Three lignans isolated from the roots of A. koreanum (Araliaceae), namely eleutheroside E ( 1 ), tortoside A ( 2 ), and hemiariensin ( 4 ), were evaluated for their ability to inhibit NFAT transcription factor. Of these compounds, compound 4 , possessing a diarylbutane Skeleton, exhibited potent inhibitory activity against NFAT transcription factor (IC_50: 36.3 +- 2.5 μM). However, the activities of 1 (IC50: > 500 μM) and 2 (IC_50: 136.1 +- 9.4 μM), which possess bisaryldioxabicy-clooctane Skeletons, were lower. As the lignan derivatives of the same Skeletons, hinokinin ( 5 ) and (-)-yatein ( 6 ) with diarylbutane Skeletons and (+)-syringaresinol ( 3 ) with a bisaryldioxabicy-clooctane Skeleton were also studied for their inhibitory effects on NFAT transcription factor.
Guanghai Shen - One of the best experts on this subject based on the ideXlab platform.
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inhibitory lignans against nfat transcription factor from acanthopanax koreanum
Archives of Pharmacal Research, 2004Co-Authors: Guanghai Shen, Jong Seong KangAbstract:Three lignans isolated from the roots ofA. koreanum (Araliaceae), namely eleutheroside E (1), tortoside A (2), and hemiariensin (4), were evaluated for their ability to inhibit NFAT transcription factor. Of these compounds, compound4, possessing a diarylbutane Skeleton, exhibited potent inhibitory activity against NFAT transcription factor (IC50: 36.3 +- 2.5 μM). However, the activities of 1 (IC50: > 500 μM) and2 (IC50: 136.1 +- 9.4 μM), which possess bisaryldioxabicy-clooctane Skeletons, were lower. As the lignan derivatives of the same Skeletons, hinokinin (5) and (-)-yatein (6) with diarylbutane Skeletons and (+)-syringaresinol (3) with a bisaryldioxabicy-clooctane Skeleton were also studied for their inhibitory effects on NFAT transcription factor.
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Inhibitory lignans against NFAT transcription factor fromacanthopanax koreanum
Archives of Pharmacal Research, 2004Co-Authors: Guanghai Shen, Jong Seong KangAbstract:Three lignans isolated from the roots of A. koreanum (Araliaceae), namely eleutheroside E ( 1 ), tortoside A ( 2 ), and hemiariensin ( 4 ), were evaluated for their ability to inhibit NFAT transcription factor. Of these compounds, compound 4 , possessing a diarylbutane Skeleton, exhibited potent inhibitory activity against NFAT transcription factor (IC_50: 36.3 +- 2.5 μM). However, the activities of 1 (IC50: > 500 μM) and 2 (IC_50: 136.1 +- 9.4 μM), which possess bisaryldioxabicy-clooctane Skeletons, were lower. As the lignan derivatives of the same Skeletons, hinokinin ( 5 ) and (-)-yatein ( 6 ) with diarylbutane Skeletons and (+)-syringaresinol ( 3 ) with a bisaryldioxabicy-clooctane Skeleton were also studied for their inhibitory effects on NFAT transcription factor.
Gunilla Borgefors - One of the best experts on this subject based on the ideXlab platform.
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Hierarchical decomposition of multiscale Skeletons
IEEE Transactions on Pattern Analysis and Machine Intelligence, 2001Co-Authors: Gunilla Borgefors, Giuliana Ramella, Gabriella Sanniti Di BajaAbstract:The paper presents a novel procedure to hierarchically decompose a multiscale discrete Skeleton. The Skeleton is a linear pattern representation that is generally recognized as a good shape descriptor. For discrete images, the discrete Skeleton is often preferable. Multiresolution representations are convenient for many image analysis tasks. Our resulting Skeleton decomposition shows two different types of hierarchy. The first type of hierarchy is one of different scales, as the original pattern is converted into an AND-pyramid and the Skeleton is computed for each resolution level. The second type of hierarchy is established at each level of the pyramid by identifying and ranking Skeleton subsets according to their permanence, where permanence is a property intrinsically related to local pattern thickness. To achieve the decomposition, both bottom-up and top-down analysis in the sense of moving from higher to lower resolution and vice versa are used. The bottom-up analysis is used to ensure that a part of the Skeleton that is connected at a higher resolution level is also connected (if at all present) in the next, lower resolution level. The top-down analysis is used to build the permanence hierarchy ranking the Skeleton components. Our procedure is based on the use of (3/spl times/3) local operations in digital images, so it is fast and easy to implement. This Skeleton decomposition procedure is most effective on patterns having different thickness in different regions. A number of examples of decompositions of multiscale Skeletons (with and without loops) are shown. The Skeletons are, in most cases, nicely decomposed into meaningful parts. The procedure is general and not limited to any specific application.
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Skeletonizing volume objects part 2 from surface to curve Skeleton
Lecture Notes in Computer Science, 1998Co-Authors: Gunilla Borgefors, Ingela Nyström, Gabriella Sanniti Di BajaAbstract: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.
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SSPR/SPR - Skeletonizing Volume Objects Part 2: From Surface to Curve Skeleton
Lecture Notes in Computer Science, 1998Co-Authors: Gunilla Borgefors, Ingela Nyström, Gabriella Sanniti Di BajaAbstract: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.
