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作者： Xu, Wei Ha, Sungsoo Mueller, Klaus Center of Visual Computing Computer Science Department Stony Brook University Stony Brook NY 11794-4400 United States

Purpose: Acquiring data for CT at low radiation doses has become a pressing goal. Unfortunately, the reduced data quality adversely affects the quality of the reconstructions, impeding their readability. In previous work, the authors showed how a prior regular-dose scan of the same patient can efficiently be used to mitigate low-dose artifacts. However, since a prior is not always available, the authors now extend the authors' method to use a database of images of other patients. Methods: The authors' framework first matches the low-dose (target) scan with the images in the database and then selects a set of images that contain anatomical content similar to the target. These "priors" are then registered to the target and form the set of regular-dose priors for restoration via an extended nonlocal means (NLM) filtering framework. To accommodate the larger spatial variability of the patient scans, the authors subdivide the image area into blocks and perform the filtering locally. The database itself is first preprocessed to map each image from its 2D image space to a corresponding high-D image feature space. From this encoding a visual vocabulary is learned that assists in the query of the database. Results: The authors demonstrate the authors' framework via a lung scan example, for both streak artifacts (resulting from smaller projection sets) as well as noise artifacts (resulting from lower mA settings). The authors find that in the authors' particular example case three priors were sufficient to restore all features faithfully. The authors also observe that the authors' method is quite robust in that it generates good results even when the noise conditions significantly worsen (here by 20%). Finally, the authors find that the restoration quality is significantly better than with conventional NLM filtering. Conclusions: The authors image restoration algorithm successfully restores images to high quality when the registration is well performed and also when the prior

关键词： Image reconstruction

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A pilot study for Augmented Reality supported procedure guidance to operate payload racks on-board the International Space Station

A pilot study for Augmented Reality supported procedure guid...

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International Symposium on Mixed and Augmented Reality (ISMAR)

作者： Daniela Markov-Vetter Oliver Staadt Biomedical Science Support Center Institute of Aerospace Medicine German Aerospace Center (DLR) Cologne Germany Visual Computing Laboratory Institute of Computer Science University of Rostock Germany

We present our current state in developing and testing of Augmented Reality supported spaceflight procedures for intra-vehicular payload activities. Our vision is to support the ground team and the flight crew to author and operate easily AR guidelines without programming and AR knowledge. For visualization of the procedural instructions using an HMD, 3D registered visual aids are overlaid onto the payload model operated by additional voice control. Embedded informational resources (e.g., images and videos) are provided through a mobile tangible user interface. In a pilot study that was performed at the ESA European Astronaut Centre by application domain experts, we evaluated the performance, workload and acceptance by comparing our AR system with the conventional method of displaying PDF documents of the procedure.

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FMRI analysis of cocaine addiction using k-support sparsity

FMRI analysis of cocaine addiction using k-support sparsity

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IEEE International Symposium on Biomedical Imaging

作者： Katerina Gkirtzou Jean Honorio Dimitris Samaras Rita Goldstein Matthew B. Blaschko Center for Visual Computing École Centrale Paris France Computer Science Departement Stony Brook University Stony Brook NY USA Icahn School of Medicine Mount Sinai New York NY USA LIGM (UMR CNRS) École des Ponts ParisTech Université Paris-Est Paris France

ISBN: (纸本)9781467364560

In this paper, we explore various sparse regularization techniques for analyzing fMRI data, such as LASSO, elastic net and the recently introduced k-support norm. Employing sparsity regularization allow us to handle the curse of dimensionality, a problem commonly found in fMRI analysis. We test these methods on real data of both healthy subjects as well as cocaine addicted ones and we show that although LASSO has good prediction, it lacks interpretability since the resulting model is too sparse, and results are highly sensitive to the regularization parameter. We find that we can improve prediction performance over the LASSO using elastic net or the k-support norm, which is a convex relaxation to sparsity with an ℓ 2 penalty that is tighter than the elastic net. Elastic net and k-support norm overcome the problem of overly sparse solutions, resulting in both good prediction and interpretable solutions, while the k-support norm gave better prediction performance. Our experimental results support the general applicability of the k-support norm in fMRI analysis, both for prediction performance and interpretability.

关键词： Correlation Image color analysis Kernel Vectors Drugs Laplace equations Biomedical imaging

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Structure-preserving retargeting of irregular 3D architecture

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Jisuanji Fuzhu Sheji Yu Tuxingxue Xuebao/Journal of computer-Aided Design and computer Graphics 2012年第2期24卷 146-148页

作者： Lin, Jinjie Cohen-Or, Daniel Zhang, Hao Liang, Cheng Sharf, Andrei Deussen, Oliver Chen, Baoquan Visual Computing Center Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences Shenzhen 518055 China Department of Computer Science Tel Aviv University Tel Aviv Israel Computer Science Simon Fraser University Vancouver Canada Department of Computer Science Ben-Gurion University Beer-Sheva Israel Department of Computer and Information Science University of Konstanz Konstanz Germany

We present an algorithm for interactive structure-preserving retargeting of irregular 3D architecture models, offering the modeler an easy-to-use tool to quickly generate a variety of 3D models that resemble an input piece in its structural style. Working on a more global and structural level of the input, our technique allows and even encourages replication of its structural elements, while taking into account their semantics and expected geometric interrelations such as alignments and adjacency. The algorithm performs automatic replication and scaling of these elements while preserving their structures. Instead of formulating and solving a complex constrained optimization, we decompose the input model into a set of sequences, each of which is a 1D structure that is relatively straightforward to retarget. As the sequences are retargeted in turn, they progressively constrain the retargeting of the remaining sequences. We demonstrate interactivity and variability of results from our retargeting algorithm using many examples modeled after real-world architectures exhibiting various forms of irregularity.

关键词： Constrained optimization

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Conjoining Gestalt rules for abstraction of architectural drawings

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Jisuanji Fuzhu Sheji Yu Tuxingxue Xuebao/Journal of computer-Aided Design and computer Graphics 2012年第2期24卷 152-154页

作者： Nan, Liangliang Sharf, Andrei Xie, Ke Wong, Tien-Tsin Deussen, Oliver Cohen-Or, Daniel Chen, Baoquan Visual Computing Center Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences Shenzhen 518055 China Department of Computer Science Ben-Gurion University Beer-Sheva Israel Department of Computer Science and Engineering Chinese University of Hong Kong Hong Kong Hong Kong Department of Computer and Information Science University of Konstanz Konstanz Germany Department of Computer Science Tel Aviv University Tel Aviv Israel

We present a method for structural summarization and abstraction of complex spatial arrangements found in architectural drawings. The method is based on the well-known Gestalt rules, which summarize how forms, patterns, and semantics are perceived by humans from bits and pieces of geometric information. Although defining a computational model for each rule alone has been extensively studied, modeling a conjoint of Gestalt rules remains a challenge. In this work, we develop a computational framework which models Gestalt rules and more importantly, their complex interactions. We apply conjoining rules to line drawings, to detect groups of objects and repetitions that conform to Gestalt principles. We summarize and abstract such groups in ways that maintain structural semantics by displaying only a reduced number of repeated elements, or by replacing them with simpler shapes. We show an application of our method to line drawings of architectural models of various styles, and the potential of extending the technique to other computer-generated illustrations, and three-dimensional models.

关键词： Semantics

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IEEE Symposium on Nuclear science (NSS/MIC)

作者： Wei Xu Klaus Mueller Center of Visual Computing Computer Science Department Stony Brook University NY USA

Iterative CT algorithms are becoming increasingly popular in recent years, and have been found useful when the projections are limited in number, irregularly spaced, or noisy, which are imaging scenarios often encountered in low-dose imaging and compressed sensing. One way to cope with the associated streak and noise artifacts in these settings is either to incorporate or to interleave a regularization objective into the iterative reconstruction framework. In this paper we explore possible techniques for the latter. We investigate a number of non-linear filters popular in the image processing literature for their suitability in iterative CT application, here OS-SIRT.

关键词： Filtering algorithms Pixel Image reconstruction Smoothing methods Computed tomography Graphics processing unit Iterative methods

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Cache-aware GPU memory scheduling scheme for CT back-projection

Cache-aware GPU memory scheduling scheme for CT back-project...

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IEEE Symposium on Nuclear science (NSS/MIC)

作者： Ziyi Zheng Klaus Mueller Center of Visual Computing Computer Science Department Stony Brook University Stony Brook NY USA

Graphic process units (GPUs) are well suited to computing-intensive tasks and are among the fastest solutions to perform Computed Tomography (CT) reconstruction. As previous research shows, the bottleneck of GPU-implementation is not the computational power, but the memory bandwidth. We propose a cache-aware memory-scheduling scheme for the back-projection, which can ensure a better load-balancing between GPU processors and the GPU memory. The proposed reshuffling method can be directly applied on existing GPU-accelerated CT reconstruction pipelines. The experimental results show that our optimization can achieve speedup ranging from 1.18-1.48. Our cache-optimization method is particular effective for low-resolution volumes with high resolution projections.

关键词： Graphics processing unit Computed tomography Three dimensional displays Instruction sets Memory management Writing

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Two examples of GPGPU acceleration of memory-intensive algorithms

Two examples of GPGPU acceleration of memory-intensive algor...

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8th Eurographics Italian Chapter Conference 2010

作者： Marras, Stefano Mura, Claudio Gobbetti, Enrico Scateni, Riccardo Scopigno, Roberto University of Cagliari Dept. of Mathematics and Computer Science Italy Sardegna Ricerche DistrICT Lab. Italy CRS4 - Center for Advanced Studies Research and Development Sardinia Italy ISTI-CNR Visual Computing Lab. Italy

ISBN: (纸本)9783905673807

The advent of GPGPU technologies has allowed for sensible speed-ups in many high-dimension, memory-intensive computational problems. In this paper we demonstrate the effectiveness of such techniques by describing two applications of GPGPU computing to two different subfields of computer graphics, namely computer vision and mesh processing. In the first case, CUDA technology is employed to accelerate the computation of approximation of motion between two images, known also as optical flow. As for mesh processing, we exploit the massively-parallel architecture of CUDA devices to accelerate the face clustering procedure that is employed in many recent mesh segmentation algorithms. In both cases, the results obtained so far are presented and thoroughly discussed, along with the expected future development of the work. © The Eurographics Association 2010.

关键词： Parallel architectures

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Efficiently GPU-accelerating long kernel convolutions in 3-D DIRECT TOF PET reconstruction via a kernel decomposition scheme

Efficiently GPU-accelerating long kernel convolutions in 3-D...

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IEEE Symposium on Nuclear science (NSS/MIC)

作者： Sungsoo Ha Zhiyuan Zhang Klaus Mueller Samuel Matej Center of Visual Computing Computer Science Department Stony Brook University NY USA Department of Radiology University of Pennsylvania Philadelphia PA USA

The DIRECT approach for 3-D Time-of-Flight (TOF) PET reconstruction performs all iterative predictor-corrector operations directly in image space. A computational bottleneck here is the convolution with the long TOF (resolution) kernels. Accelerating this convolution operation using GPUs is very important especially for spatially variant resolution kernels, which cannot be efficiently implemented in the Fourier domain. The main challenge here is the memory cache performance at non-axis aligned directions. We devised a scheme that first re-samples the image into an axis-aligned orientation offering good memory coherence for the convolution operations. In order to maintain good accuracy, we carefully design the resampling and new convolution kernels to combine into the original TOF kernel. This paper demonstrates the validity, accuracy, and high speed-performance of our scheme for a comprehensive set of orientation angles. Future work will apply these cascaded kernels within a GPU-accelerated version of DIRECT.

关键词： Kernel Image reconstruction Graphics processing unit Acceleration Strontium Positron emission tomography Filtering theory

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Constructing 3D elliptical gaussians for irregular data

Constructing 3D elliptical gaussians for irregular data

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Workshop on Mathematical Foundations of Scientific visualization, computer Graphics, and Massive Data Exploration, 2004

作者： Hong, Wei Neophytou, Neophytos Mueller, Klaus Kaufman, Arie Center for Visual Computing Department of Computer Science Stony Brook University United States

ISBN: (纸本)9783642543005

Volumetric datasets obtained from scientific simulation and partial differential equation solvers are typically given in the form of non-rectilinear grids. The splatting technique is a popular direct volume rendering algorithm, which can provide high quality rendering results, but has been mainly described for rectilinear grids. In splatting, each voxel is represented by a 3D kernel weighted by the discrete voxel value. While the 3D reconstruction kernels for rectilinear grids can be easily constructed based on the distance among the aligned voxels, for irregular grids the kernel construction is significantly more complicated. In this paper, we propose a novel method based on a 3D Delaunay triangulation to create 3D elliptical Gaussian kernels, which then can be used by a splatting algorithm for the rendering of irregular grids. Our method does not require a resampling of the irregular grid. Instead, we use a weighted least squares method to fit a 3D elliptical Gaussian centered at each grid point, approximating its Voronoi cell. The resulting 3D elliptical Gaussians are represented using a convenient matrix representation, which allows them to be seamlessly incorporated into our elliptical splatting rendering system. © 2009 Springer-Verlag Berlin Heidelberg.

关键词： Least squares approximations

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