data segmentation andobject rendering is required for localization super-resolution microscopy, fluorescent photoactivation localization microscopy (FPALM), anddirect stochastic optical reconstruction microscopy (dS...
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data segmentation andobject rendering is required for localization super-resolution microscopy, fluorescent photoactivation localization microscopy (FPALM), anddirect stochastic optical reconstruction microscopy (dSTORM). We developed and validated methods for segmenting objects based on delaunay triangulation in 3d space, followed by facet culling. We applied them to visualize mitochondrial nucleoids, which confine dNA in complexes with mitochondrial (mt) transcription factor A (TFAM) and gene expression machinery proteins, such as mt single-stranded-dNA-binding protein (mtSSB). Eos2-conjugated TFAM visualized nucleoids in HepG2 cells, which was compared with dSTORM 3d-immunocytochemistry of TFAM, mtSSB, or dNA. The localized fluorophores of FPALM/dSTORM data were segmented using delaunay triangulation into polyhedron models and by principal component analysis (PCA) into general PCA ellipsoids. The PCA ellipsoids were normalized to the smoothed volume of polyhedrons or by the net unsmootheddelaunay volume and remodeled into rotational ellipsoids to obtain models, termeddVRE. The most frequent size of ellipsoid nucleoid model imaged via TFAM was 35 x 45 x 95 nm;or 35 x 45 x 75 nm for mtdNA cores;and 25 x 45 x 100 nm for nucleoids imaged via mtSSB. Nucleoids encompasseddifferent point density and wide size ranges, speculatively due to different activity stemming from different TFAM/mtdNA stoichiometry/density. Considering twofold lower axial vs. lateral resolution, only bulky dVRE models with an aspect ratio > 3 and tilted toward the xy-plane were considered as two proximal nucleoids, suspicious occurring after division following mtdNA replication. The existence of proximal nucleoids in mtdNA-dSTORM 3d images of mtdNA "doubling"-supported possible direct observations of mt nucleoiddivision after mtdNA replication.
The problem of how to arrive at an appropriate 3d-segmentation of a scene remains difficult. While current state-of-the-art methods continue to gradually improve in benchmark performance, they also grow more and more ...
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ISBN:
(纸本)9781479951178
The problem of how to arrive at an appropriate 3d-segmentation of a scene remains difficult. While current state-of-the-art methods continue to gradually improve in benchmark performance, they also grow more and more complex, for example by incorporating chains of classifiers, which require training on large manually annotateddata-sets. As an alternative to this, we present a new, efficient learning-and model-free approach for the segmentation of 3d point clouds into object parts. The algorithm begins by decomposing the scene into an adjacency-graph of surface patches based on a voxel grid. Edges in the graph are then classified as either convex or concave using a novel combination of simple criteria which operate on the local geometry of these patches. This way the graph is divided into locally convex connected subgraphs, which - with high accuracy -represent object parts. Additionally, we propose a novel depth dependent voxel grid to deal with the decreasing point-density at far distances in the point clouds. This improves segmentation, allowing the use of fixed parameters for vastly different scenes. The algorithm is straightforward to implement and requires no training data, while nevertheless producing results that are comparable to state-of-the-art methods which incorporate high-level concepts involving classification, learning and model fitting.
3d object segmentation is important in computer vision such as target detection in biomedical image analysis. A new method, called B-Surface algorithm, is generated for 3d object segmentation. An improved3d external ...
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3d object segmentation is important in computer vision such as target detection in biomedical image analysis. A new method, called B-Surface algorithm, is generated for 3d object segmentation. An improved3d external force field combined with the normalized GVF is utilized. After the initialization of a surface model near the target, B-Surface starts to deform to locate the boundary of the object. First, it overcomes the difficulty that comes from analyzing 3d volume image slice by slice. And the speed of B-Surface deformation is enhanced since the internal forces are not needed to compute in every iteration deformation step. Next, the normal at every surface point can be calculated easily since B-Surface is a continuous deformable model. And it has the ability to achieve high compression ratio (ratio of data to parameters) by presenting the whole surface with only a relatively small number of control points. Experimental results and analysis are presented in this paper. We can see that the B-Surface algorithm can find the surface of the target efficiently. (C) 2005 Elsevier B.V. All rights reserved.
In this paper we present a new framework for analyzing and segmenting point-sampled3dobjects. Our method first computes for each surface point the surface curvature distribution by applying the Principal Component A...
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ISBN:
(纸本)0769521401
In this paper we present a new framework for analyzing and segmenting point-sampled3dobjects. Our method first computes for each surface point the surface curvature distribution by applying the Principal Component Analysis on local neighborhoods with different sizes. Then we model in the four dimensional space the joint distribution of surface curvature and position features as a mixture of Gaussians using the Expectation Maximization algorithm. Central to our method is the extension of the scale-space theory from the 2ddomain into the three-dimensional space to allow feature analysis and classification at different scales. Our algorithm operates directly on points requiring no vertex connectivity information. We demonstrate anddiscuss the performance of our framework on a collection of point sampled3dobjects.
In recent days, applications using 3d animation models are increasing. Since the 3d animation model contains a huge amount of information, data compression is needed for efficient storage or transmission. Although the...
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ISBN:
(纸本)0819439886
In recent days, applications using 3d animation models are increasing. Since the 3d animation model contains a huge amount of information, data compression is needed for efficient storage or transmission. Although there have been various proposals for 3d model coding, most works have considered only static connectivity and geometry information. Only a few studies have been presented for 3d animation models. This paper presents a coding scheme for 3d animation models using a new 3dsegmentation algorithm. For an accurate segmentation, we take advantage of temporal coherence in the generic animated3d model. After the motion vector of each vertex is mapped onto the surface of the unit sphere in the spherical coordinate system, we partition the surface of the sphere equally to have the same area. We then reconstruct in-between 3d models using the reconstructed key frame and an affine motion model for each segmented unit.
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