Skeleton Graph Research Articles

A novel sketch-based 3D model retrieval method by integrating skeleton graph and contour feature

With the rapid growth of 3D models on the Web, effective methods to retrieve appropriate 3D models are becoming crucial. In this paper, we propose a novel sketch-based 3D model retrieval approach which integrates the skeleton graph and contour feature. Skeleton graph represents the topology structure of the query sketch as well as representative 2D views of 3D models, and contour feature captures their detailed information. Moreover, A hierarchical coarse-to-fine searching strategy is employed in this method. Firstly, a skeleton graph based matching method is used to eliminate those 3D models which are dissimilar to the query sketch. Then, the selected candidate models are refined by measuring the similarity of their contour feature. In addition, we adopt a graph transduction method to improve the ranking order of the retrieved 3D models. The proposed method was tested on the public sketch benchmarks and compared with other leading sketch-based 3D model retrieval methods. The experiment results demonstrate that our approach can accurately retrieve desired 3D models for users and has a superior performance to other state-of-the-art approaches.

New Feature Extraction and Processing Methods for the Advanced Knowledge Based Process Planning of Forming Operations

A new method for the automated extraction, recognition and interpretation of forming features is presented. The motivation is to apply knowledge based engineering (KBE) methods more intensively on process planning procedures for forging, tube hydroforming and sheet metal forming. With a new thinning method three-dimensional (3D) CAD data is reduced to a 1D skeleton graph. By curvature based segmentation of the surface and the neighborhood relations further information are provided. Depending on the gained classification, analyzing algorithms can be performed again with an adapted level of detail. Herewith the process planning can be supported on a much higher level than before. Video abstract [Display omitted]

Non-Planar Cluster Quivers from Surface

In this paper, any directed graph is called a quiver. We first introduce the notions of skeleton graph and reduced skeleton graph of K5 and K3,3 types. Then a complete classification of reduced skeleton graphs of K5 and K3,3 types is given. Moreover, as a specification of Kuratowski’s theorem, the precise characterization of non-planar cluster quivers from surface is obtained.

Skeleton-based abdominal aorta registration technique.

Vascular diseases are the most challenging health problems in developed countries. The vascular segmentation as well as registration techniques are the topics of past and ongoing research activities. In this work we target an abdominal aorta registration technique. The developed methodology is useful in the assessment of abdominal aortic aneurysm treatment by visualizing the correspondence between pre- and postoperative Computed Tomography (CT) data. The presented approach makes it possible to match all voxels belonging to the aorta from different CT series. It is based on aorta lumen segmentation and graph matching method. To segment the lumen area a hybrid level-set active contour approach is used. The matching step is performed based on a path similarity skeleton graph matching procedure. The registration results have been tested on the database of 8 patients, for which two different contrast-enhanced CT series were acquired. All registration results achieved with our system and verified by an expert prove the efficiency of the approach and encourage to further develop this method.

Skeleton graph expansion of critical exponents in "cultural revolution" years

Kenneth Wilson's Nobel Prize winning breakthrough in the renormalization group theory of phase transition and critical phenomena almost overlapped with the violent "cultural revolution" years (1966–1976) in China. An unexpected chance in 1972 brought the author of these lines close to the Wilson–Fisher ϵ-expansion of critical exponents and eventually led to a joint paper with Lu Yu published entirely in Chinese without any English title and abstract. Even the original acknowledgment was deleted because of mentioning foreign names like Kenneth Wilson and Kerson Huang. In this article I will tell the 40-year old story as a much belated tribute to Kenneth Wilson and to reproduce the essence of our work in English. At the end, I give an elementary derivation of the Callan–Symanzik equation without referring to field theory.

Human action recognition based on chaotic invariants

A new human action recognition approach was presented based on chaotic invariants and relevance vector machines (RVM). The trajectories of reference joints estimated by skeleton graph matching were adopted for representing the nonlinear dynamical system of human action. The C-C method was used for estimating delay time and embedding dimension of a phase space which was reconstructed by each trajectory. Then, some chaotic invariants representing action can be captured in the reconstructed phase space. Finally, RVM was used to recognize action. Experiments were performed on the KTH, Weizmann and Ballet human action datasets to test and evaluate the proposed method. The experiment results show that the average recognition accuracy is over 91.2%, which validates its effectiveness.

SCALe‐invariant Integral Surfaces

AbstractExtraction of skeletons from solid shapes has attracted quite a lot of attention, but less attention was paid so far to the reverse operation: generating smooth surfaces from skeletons and local radius information. Convolution surfaces, i.e. implicit surfaces generated by integrating a smoothing kernel along a skeleton, were developed to do so. However, they failed to reconstruct prescribed radii and were unable to model large shapes with fine details. This work introduces SCALe‐invariant Integral Surfaces (SCALIS), a new paradigm for implicit modelling from skeleton graphs. Similarly to convolution surfaces, our new surfaces still smoothly blend when field contributions from new skeleton parts are added. However, in contrast with convolution surfaces, blending properties are scale‐invariant. This brings three major benefits: the radius of the surface around a skeleton can be explicitly controlled, shapes generated in blending regions are self‐similar regardless of the scale of the model and thin shape components are not excessively smoothed out when blended into larger ones.

Symmetry discovery and retrieval of nonrigid 3D shapes using geodesic skeleton paths

In this paper, we propose a skeleton path based approach for symmetry discovery and retrieval of nonrigid 3D shapes. The main idea is to match skeleton graphs by comparing the geodesic paths between skeleton endpoints. Our approach is motivated by the fact that the path feature is stable in the presence of articulation of components. The experimental results demonstrate the performance of our proposed method in terms of robustness to symmetry, discrimination against different graph structures, and high efficiency in the retrieval of nonrigid 3D shapes.

Norm convergence of sectorial operators on varying Hilbert spaces

Convergence of operators acting on a given Hilbert space is an old and well studied topic in operator theory. The idea of introducing a related notion for operators acting on arying spaces is natural. However, it seems that the first results in this direction have been obtained only recently, to the best of our knowledge. Here we consider sectorial operators on scales of Hilbert spaces. We define a notion of convergence that generalises convergence of the resolvents in operator norm to the case when the operators act on different spaces and show that this kind of convergence is compatible with the functional calculus of the operator and moreover implies convergence of the spectrum. Finally, we present examples for which this convergence can be checked, including convergence of coefficients of parabolic problems. Convergence of a manifold (roughly speaking consisting of thin tubes) towards the manifold's skeleton graph plays a prominent role, being our main application.

Curve skeleton extraction by coupled graph contraction and surface clustering

In this paper, we present a practical algorithm to extract a curve skeleton of a 3D shape. The core of our algorithm comprises coupled processes of graph contraction and surface clustering. Given a 3D shape represented by a triangular mesh, we first construct an initial skeleton graph by directly copying the connectivity and geometry information from the input mesh. Graph contraction and surface clustering are then performed iteratively. The former merges certain graph nodes based on computation of an approximate centroidal Voronoi diagram, seeded by subsampling the graph nodes from the previous iteration. Meanwhile, a coupled surface clustering process serves to regularize the graph contraction. Constraints are used to ensure that extremities of the graph are not shortened undesirably, to ensure that skeleton has the correct topological structure, and that surface clustering leads to an approximately-centered skeleton of the input shape. These properties lead to a stable and reliable skeleton graph construction algorithm.Experiments demonstrate that our skeleton extraction algorithm satisfies various desirable criteria. Firstly, it produces a skeleton homotopic with the input (the genus of both shapes agree) which is both robust (results are stable with respect to noise and remeshing of the input shape) and reliable (every boundary point is visible from at least one curve-skeleton location). It can also handle point cloud data if we first build an initial skeleton graph based on k-nearest neighbors. In addition, a secondary output of our algorithm is a skeleton-to-surface mapping, which can e.g. be used directly for skinning animation. Highlights(1) An algorithm for curve skeleton extraction from 3D shapes based on coupled graph contraction and surface clustering. (2) The algorithm meets various desirable criteria and can be extended to work for incomplete point clouds.

Automated delineation of lung tumors from CT images using a single click ensemble segmentation approach

A single click ensemble segmentation (SCES) approach based on an existing “Click & Grow” algorithm is presented. The SCES approach requires only one operator selected seed point as compared with multiple operator inputs, which are typically needed. This facilitates processing large numbers of cases. Evaluation on a set of 129 CT lung tumor images using a similarity index (SI) was done. The average SI is above 93% using 20 different start seeds, showing stability. The average SI for 2 different readers was 79.53%. We then compared the SCES algorithm with the two readers, the level set algorithm and the skeleton graph cut algorithm obtaining an average SI of 78.29%, 77.72%, 63.77% and 63.76%, respectively. We can conclude that the newly developed automatic lung lesion segmentation algorithm is stable, accurate and automated.

Shape clustering: Common structure discovery

This paper aims to address the problem of shape clustering by discovering the common structure which captures the intrinsic structural information of shapes belonging to the same cluster. It is based on a skeleton graph, named common structure skeleton graph (CSSG), which expresses possible correspondences between nodes of the individual skeletons of the cluster. To construct the CSSG, we derive the correspondences by the optimal subsequence bijection (OSB). To cluster the shape data, we apply an agglomerative clustering scheme, in each iteration, the CSSGs are formed from each cluster and the two closest clusters are merged into one. The proposed agglomerative clustering algorithm has been evaluated on several shape data sets, including three articulated shape data sets, Torsello's data set, and a gesture data set. In all experiments, our method demonstrates effective performance compared to other algorithms.

SideKnot: Edge bundling for uncovering relation patterns in graphs

The node-link diagram is an intuitive way to depict a graph and present relationships between entities. Addressing the visual clutter induced by edge crossing and node-edge overlapping is a challenging task as the size of graph outgrows the visualization space. Many edge bundling methods are proposed to disclose high-level edge patterns. Though previous methods can successfully reveal the skeleton graph structure, the relation patterns at the individual node level can be overlooked. In addition, most edge bundling algorithms are computationally complex, which prevents them from scaling up for extremely large graphs. In this article, we extend SideKnot, an efficient edge bundling method to cluster and knot edges at the node side. Our proposed method is light, runs faster than most existing algorithms, and can reveal the relation patterns at the individual node level. Our results show that SideKnot can disclose a node's standing in the graph as well as the directional connection patterns to its peers.

A novel template‐based automatic rigging algorithm for articulated‐character animation

ABSTRACTRigging is a process for creating skeletons used to animate articulated characters. In conventional computer‐animation software, this process must be performed manually. Although several automatic rigging algorithms have been proposed, these methods still require user intervention. This paper proposes an automatic algorithm that generates an inverse kinematic skeleton for a character by locating an appropriate template skeleton on the extracted curve skeleton of the input 3D character model. After the curve skeleton is extracted, it is analyzed and classified into an appropriate category. The classification conditions are developed from the characteristics of each kind of real animal. We also develop an algorithm to extract the anatomical meaning of each skeleton segment. On the basis of the classification result, a suitable template skeleton is retrieved from the database. Each bone of the template skeleton can then be located on the appropriate skeleton segment of the input skeleton graph by using the extracted anatomical meanings. In contrast to previous methods, the algorithm does not require the input 3D character models to have certain poses or orientations. Moreover, all processes can be completed without user intervention. Copyright © 2012 John Wiley & Sons, Ltd.

Biomedical image skeletonization: a novel method applied to fibrin network structures.

To understand the rheological behavior of fibrin clots, we must obtain quantitative measurements of morphometric parameters of the networks formed under various conditions. The networks are so complex that researchers must currently manually segment the images of network samples and estimate the parameters from them. Skeletonization is a promising tool for automating this task. We here propose a method that rapidly constructs a coarse representation of a skeleton graph and, using the snake model, deforms the graph to obtain smooth skeletons. Unlike many existing approaches, our method does not involve explicit object boundary information or high order derivatives. Since our method processes a given image as a whole, the presence of multiple objects in an image is automatically detected and the skeletons of these objects are computed simultaneously.

Research on Quantitative Model of Path Planning for Mobile Robots

According to free configuration space, the skeleton graph of working environment of mobile robots was constructed. The length of link of skeleton graph was quantified according the actual length of pathway and presented with quantitative length matrix. The width of link of skeleton graph was quantified by the width rate of pathway and robot body, and presented with quantitative width matrix. For a pathway with width changing, the width was quantified according to the minimum width of the pathway. For multiply pathways between two vertexes, the pathways were merged into one pathway. In dynamic environment, the matrix value of link was set to -1 for a pathway with obstacles. In Dijkstra algorithm, the coordination path planning of multiply robots was implemented by comparing the remaining capacity of pathway width and the robot body.

3D geometry and topology of pore pathways in Opalinus clay: Implications for mass transport

Clay-rich sedimentary rocks are considered as seal lithologies for hosting radioactive waste or as caprocks for geological carbon sequestration sites. Evaluation of the rock's sealing capacity requires a comprehensive understanding of mass transport processes, which, in turn, demands knowledge of the 3D structure of pore space. Here, the use of focused ion beam nanotomography (FIB-nt) in building realistic pore space models is demonstrated along with a novel approach employed to analyze the topology of the pore space. The method was applied to three samples of the Opalinus clay of in northern Switzerland which is considered as a candidate host rock formation for the disposal of radioactive waste. Due to resolution limitations the lower limit of analyzed pore radii was about 10 nm. Pore radii > 10 nm were related to a physical porosity of about 2 vol.%. Comparing the pore size distribution determined by FIB-nt with the one obtained by N 2 adsorption analysis, FIB-nt revealed the structure of about 20% of the total pore space. The total external porosity determined by N 2 adsorption analysis was in the range of 10 to 12 vol.%. Our approach to analyze the complex 3D structure of the pore space was based on converting the voxel based 3D structure into a 3D graph of the pore skeleton. A 3D graph representation permitted determination of the spatial distribution of pore space geometrical properties such as pore path orientation, pore path tortuosity and pore path length. Pore-paths in Opalinus clay show a preferred orientation within the bedding plane in combination with a comparatively low pore path tortuosity. Pore path tortuosity perpendicular to the bedding plane is higher by a factor of as much as five. Anisotropy in pore space is caused by spatial density variations of pore path orientation (i.e. preferred orientations of pore paths) in combination with an elongated pore shape (i.e. low tortuosity).

Modeling Complex Unfoliaged Trees from a Sparse Set of Images

AbstractWe present a novel image‐based technique for modeling complex unfoliaged trees. Existing tree modeling tools either require capturing a large number of views for dense 3D reconstruction or rely on user inputs and botanic rules to synthesize natural‐looking tree geometry. In this paper, we focus on faithfully recovering real instead of realistically‐looking tree geometry from a sparse set of images. Our solution directly integrates 2D/3D tree topology as shape priors into the modeling process. For each input view, we first estimate a 2D skeleton graph from its matte image and then find a 2D skeleton tree from the graph by imposing tree topology. We develop a simple but effective technique for computing the optimal 3D skeleton tree most consistent with the 2D skeletons. For each edge in the 3D skeleton tree, we further apply volumetric reconstruction to recover its corresponding curved branch. Finally, we use piecewise cylinders to approximate each branch from the volumetric results. We demonstrate our framework on a variety of trees to illustrate the robustness and usefulness of our technique.

Connectivity-Based Skeleton Extraction in Wireless Sensor Networks

Many sensor network applications are tightly coupled with the geometric environment where the sensor nodes are deployed. The topological skeleton extraction for the topology has shown great impact on the performance of such services as location, routing, and path planning in wireless sensor networks. Nonetheless, current studies focus on using skeleton extraction for various applications in wireless sensor networks. How to achieve a better skeleton extraction has not been thoroughly investigated. There are studies on skeleton extraction from the computer vision community; their centralized algorithms for continuous space, however, are not immediately applicable for the discrete and distributed wireless sensor networks. In this paper, we present a novel Connectivity-bAsed Skeleton Extraction (CASE) algorithm to compute skeleton graph that is robust to noise, and accurate in preservation of the original topology. In addition, CASE is distributed as no centralized operation is required, and is scalable as both its time complexity and its message complexity are linearly proportional to the network size. The skeleton graph is extracted by partitioning the boundary of the sensor network to identify the skeleton points, then generating the skeleton arcs, connecting these arcs, and finally refining the coarse skeleton graph. We believe that CASE has broad applications and present a skeleton-assisted segmentation algorithm as an example. Our evaluation shows that CASE is able to extract a well-connected skeleton graph in the presence of significant noise and shape variations, and outperforms the state-of-the-art algorithms.

Path Similarity Skeleton Graph Matching

This paper presents a novel framework to for shape recognition based on object silhouettes. The main idea is to match skeleton graphs by comparing the shortest paths between skeleton endpoints. In contrast to typical tree or graph matching methods, we completely ignore the topological graph structure. Our approach is motivated by the fact that visually similar skeleton graphs may have completely different topological structures. The proposed comparison of shortest paths between endpoints of skeleton graphs yields correct matching results in such cases. The skeletons are pruned by contour partitioning with Discrete Curve Evolution, which implies that the endpoints of skeleton branches correspond to visual parts of the objects. The experimental results demonstrate that our method is able to produce correct results in the presence of articulations, stretching, and occlusion.