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Decomposition of Branching Volume Data by Tip Detection
 in "2008 IEEE International Conference on Image Processing
"... We present an approach to decomposing branching volume data into subbranches. First, a metric is proposed for evaluating local convexities in volumetric data, and it is a criterion for global selection of tip points. Second, a multipath growing strategy is adopted to segment the volumes based on ..."
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We present an approach to decomposing branching volume data into subbranches. First, a metric is proposed for evaluating local convexities in volumetric data, and it is a criterion for global selection of tip points. Second, a multipath growing strategy is adopted to segment the volumes based on a DFS transformation starting from the tips. Experiments show that this approach is capable of generating desirable components and reasonable segmentation boundaries of a volume. Index Terms — volume decomposition, feature point detection, distance transformation 1.
Segmenting pointsampled surfaces
, 2010
"... Extracting features from pointbased representations of geometric surface models is becoming increasingly important for purposes such as model classification, matching, and exploration. In an earlier paper, we proposed a multiphase segmentation process to identify elongated features in pointsample ..."
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Extracting features from pointbased representations of geometric surface models is becoming increasingly important for purposes such as model classification, matching, and exploration. In an earlier paper, we proposed a multiphase segmentation process to identify elongated features in pointsampled surface models without the explicit construction of a mesh or other surface representation. The preliminary results demonstrated the strength and potential of the segmentation process, but the resulting segmentations were still of low quality, and the segmentation process could be slow. In this paper, we describe several algorithmic improvements to overcome the shortcomings of the segmentation process. To demonstrate the improved quality of the segmentation and the superior time efficiency of the new segmentation process, we present segmentation results obtained for various pointsampled surface models. We also discuss an application of our segmentation process to extract
Planning Motion in PointRepresented Contact Spaces Using Approximate StarShaped Decomposition
"... Abstract — Starshaped decomposition partitions a shape into a set of starshaped components. A shape is star shaped if and only if there exists at least one point which can see all the points in the shape. Due to this interesting property, decomposing a configuration space into starshaped componen ..."
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Abstract — Starshaped decomposition partitions a shape into a set of starshaped components. A shape is star shaped if and only if there exists at least one point which can see all the points in the shape. Due to this interesting property, decomposing a configuration space into starshaped components can be beneficial, e.g., for solving motion planning problem. In this paper, we propose a simple method to decompose the contact space, represented by point set data, into approximate starshaped components. We propose two motion planning methods, one deterministic and one probabilistic, both based on this idea. goal (a) start goal start (b) (c) (d) I.
Volume xx (200y), Number z, pp. 1–14 Hierarchical Structure Recovery of PointSampled Surfaces
"... We focus on the class of regular models defined by Várady et al. for reverse engineering purposes. Given a 3D surface M represented through a dense set of points, we present a novel algorithm that converts M to a hierarchical representation HM. In HM, the surface is encoded through patches of variou ..."
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We focus on the class of regular models defined by Várady et al. for reverse engineering purposes. Given a 3D surface M represented through a dense set of points, we present a novel algorithm that converts M to a hierarchical representation HM. In HM, the surface is encoded through patches of various shape and size, which form a hierarchical atlas. If M belongs to the class of regular models, then HM captures the most significant features of M at all the levels of detail. In this case, we show that HM can be exploited to interactively select regions of interest on M and intuitively redesign the model. Furthermore, HM intrinsically encodes a hierarchy of useful segmentations of M. We present a simple though efficient approach to extract and optimise such segmentations, and we show how they can be used to approximate the input point sets through idealised manifold meshes. Categories and Subject Descriptors (according to ACM CCS): Hierarchical clustering, segmentation, shape primitives, selection.
Approximate StarShaped Decomposition of Point Set Data
"... Simplification or decomposition is a common strategy to handle large geometric models, which otherwise require excessive computation to process. Starshaped decomposition partitions a model into a set of starshaped components. A model is star shaped if and only if there exists at least one point wh ..."
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Simplification or decomposition is a common strategy to handle large geometric models, which otherwise require excessive computation to process. Starshaped decomposition partitions a model into a set of starshaped components. A model is star shaped if and only if there exists at least one point which can see all the points of the model. Due to this interesting property, decomposing a model into starshaped components can be used for computing camera locations to guard a given environment (the artgallery problem), skeleton extraction, point data compression, as well as motion planning. In this paper, we propose a simple method to partition (or cluster) point set data (PSD) into “approximately starshaped ” components. Our method can be applied to both 2D and 3D PSD and can be naturally extended to higher dimensional spaces. Our method does not require or compute any connectivity information of the input points. The proposed method only requires the position and the outward normals of points. Our experimental results show that the size of the final decomposition is close to optimal.
Computers &
"... sim ting mp a cl framework takes a triangulated set of points, and by solving a linear leastsquare problem and iteratively refining parameter domains with newly added knots, we can finally obtain a continuous of mo ed geo rout ve spe smooth function whose zero levelset is in close proximity to the ..."
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sim ting mp a cl framework takes a triangulated set of points, and by solving a linear leastsquare problem and iteratively refining parameter domains with newly added knots, we can finally obtain a continuous of mo ed geo rout ve spe smooth function whose zero levelset is in close proximity to the angle dels. ment ision triangle mesh. Subdivisionbased representations of complex