Creating geometrically detailed mesh animations is an involved and resource-intensive process in digital content creation. In this work we present a method to rapidly combine available sparse motion capture data with ...
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This work presents a methodology for designing online web presentations reusing a large scale, interactive and immersive VR installation by mapping assets as well as interactions to a low capability environment. With ...
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The federated Kalman filter embodies an efficient and easy-to-implement solution for linear distributed estimation problems. Data from independent sensors can be processed locally and in parallel on different nodes wi...
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ISBN:
(纸本)9781479902842
The federated Kalman filter embodies an efficient and easy-to-implement solution for linear distributed estimation problems. Data from independent sensors can be processed locally and in parallel on different nodes without running the risk of erroneously ignoring possible dependencies. The underlying idea is to counteract the common process noise issue by inflating the joint process noise matrix. In this paper, the same trick is generalized to nonlinear models and non-Gaussian process noise. The probability density of the joint process noise is split into an exponential mixture of transition densities. By this means, the process noise is modeled to independently affect the local system models. The estimation results provided by the sensor devices can then be fused, just as if they were indeed independent.
In this paper, we show that the θ-graph with three cones is connected. We also provide an alternative proof of the connectivity of the Yao-graph with three cones.
In this paper, we show that the θ-graph with three cones is connected. We also provide an alternative proof of the connectivity of the Yao-graph with three cones.
Data below 1 mm voxel size is getting more and more common in the clinical practice but it is still hard to obtain a consistent collection of such datasets for medical image processing research. With this paper we pro...
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Data below 1 mm voxel size is getting more and more common in the clinical practice but it is still hard to obtain a consistent collection of such datasets for medical image processing research. With this paper we provide a large collection of Contrast Enhanced (CE) Computed Tomography (CT) data from porcine animal experiments and describe their acquisition procedure and peculiarities. We have acquired three CE-CT phases at the highest available scanner resolution of 57 porcine livers during induced respiratory arrest. These phases capture contrast enhanced hepatic arteries, portal venous veins and hepatic veins. Therefore, we provide scan data that allows for a highly accurate reconstruction of hepatic vessel trees. Several datasets have been acquired during Radio-Frequency Ablation (RFA) experiments. Hence, many datasets show also artificially induced hepatic lesions, which can be used for the evaluation of structure detection methods.
Given k finite point sets A1, . . . ,Ak in R2, we are interested in finding one translation for each point set such that the union of the translated point sets is in convex position. We show that if k is part of the i...
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Given k finite point sets A1, . . . ,Ak in R2, we are interested in finding one translation for each point set such that the union of the translated point sets is in convex position. We show that if k is part of the input, then it is NP-hard to determine if such translations exist, even when each point set has at most three points. The original motivation of this problem comes from the question of whether a given triangulation T of a point set is the empty shape triangulation with respect to some (strictly convex) shape S. In other words, we want to find a shape S such that the triangles of T are precisely those triangles about which we can circumscribe a homothetic copy of S that does not contain any other vertices of T. This is the Delaunay criterion with respect to S;for the usual Delaunay triangulation, S is the circle.
Statistical human body models, like SCAPE, capture static 3D human body shapes and poses and are applied to many computer Vision problems. Defined in a statistical context, their parameters do not explicitly capture s...
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Statistical human body models, like SCAPE, capture static 3D human body shapes and poses and are applied to many computer Vision problems. Defined in a statistical context, their parameters do not explicitly capture semantics of the human body shapes such as height, weight, limb length, etc. Having a set of semantic parameters would allow users and automated algorithms to sample the space of possible body shape variations in a more intuitive way. Therefore, in this paper we propose a method for re-parameterization of statistical human body models such that shapes are controlled by a small set of intuitive semantic parameters. These parameters are learned directly from the available statistical human body model. In order to apply any arbitrary animation to our human body shape model we perform retargeting. From any set of 3D scans, a semantic parametrized model can be generated and animated with the presented methods using any animation data. We quantitatively show that our semantic parameterization is more reliable than standard semantic parameterizations, and show a number of animations retargeted to our semantic body shape model.
This paper presents a discrete energy minimization approach to integrate different prior knowledge and image cues for simultaneous cell segmentation and classification. When there are multiple types of cells to segmen...
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ISBN:
(纸本)9781467364560
This paper presents a discrete energy minimization approach to integrate different prior knowledge and image cues for simultaneous cell segmentation and classification. When there are multiple types of cells to segment, the segmentation of cells and the classification of the cell types are dependent on each other. The presented approach selects the optimal segmentations from hypotheses and infers the cell types in the same process. The approach is applied to the volumetric data of Arabidopsis roots.
We present an approach to recover attenuation-free intensities of a thick sample, that is imaged by a standard confocal microscope from two views (top and bottom). A variational approach simultaneously estimates the l...
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ISBN:
(纸本)9781467364560
We present an approach to recover attenuation-free intensities of a thick sample, that is imaged by a standard confocal microscope from two views (top and bottom). A variational approach simultaneously estimates the local signal attenuation and the real attenuation-free intensity at each position. Compared to earlier work we introduce a refined image formation model, that models photo-bleaching and photon noise using Poisson image statistics. We examine the effects of different regularization methods on the absorption field (Tikhonov-Miller, Total Variation, and sparsity) and the benefit of a constrained optimization in comparison to an orthogonal subspace projection. We quantify the efficacy of the approach on synthetically generated samples, and show its general applicability on two real biological applications, namely the recordings of zebrafish embryos and Arabidopsis thaliana root tips.
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