检索结果-内蒙古大学图书馆

24th German Association for pattern recognition Symposium, DAGM 2002

作者： Bruhn, Andrés Jakob, Tobias Fischer, Markus Kohlberger, Timo Weickert, Joachim Brüning, Ulrich Schnörr, Christoph Mathematical Image Analysis Group Mathematics and Computer Science Saarland University Building 27.1 Saarbrücken66041 Germany Computer Architecture Group Department of Mathematics and Computer Science University of Mannheim Mannheim68131 Germany Computer Vision Graphics and Pattern Recognition Group Department of Mathematics and Computer Science University of Mannheim Mannheim68131 Germany

ISBN: (纸本)354044209X

This paper deals with parallelization and implementation aspects of PDE based image processing models for large cluster environments with distributed memory. As an example we focus on nonlinear isotropic diffusion filtering which we discretize by means of an additive operator splitting (AOS). We start by decomposing the algorithm into small modules that shall be parallelized separately. For this purpose image partitioning strategies are discussed and their impact on the communication pattern and volume is analyzed. Based on the results we develop an algorithmic implementation with excellent scaling properties on massively connected low latency networks. Test runs on a high–end Myrinet cluster yield almost linear speedup factors up to 209 for 256 processors. This results in typical denoising times of 0.5 seconds for five iterations on a 256 × 256 × 128 data cube. © Springer-Verlag Berlin Heidelberg 2002.

关键词： Cluster computing

来源：评论

学校读者我要写书评

暂无评论

A deep learning architecture for limited-angle computed tomography reconstruction

A deep learning architecture for limited-angle computed tomo...

引用

Workshops on Image processing for the medicine, 2017

作者： Hammernik, Kerstin Würfl, Tobias Pock, Thomas Maier, Andreas Institute of Computer Graphics and Vision Graz University of Technology Austria Pattern Recognition Lab Friedrich-Alexander-University Germany Digital Safety and Security Department AIT Austrian Institute of Technology Austria

ISBN: (纸本)9783662543443

Limited-angle computed tomography suffers from missing data in the projection domain, which results in intensity inhomogeneities and streaking artifacts in the image domain. We address both challenges by a two-step deep learning architecture: First, we learn compensation weights that account for the missing data in the projection domain and correct for intensity changes. Second, we formulate an image restoration problem as a variational network to eliminate coherent streaking artifacts. We perform our experiments on realistic data and we achieve superior results for destreaking compared to state-of-the-art non-linear filtering methods in literature. We show that our approach eliminates the need for manual tuning and enables joint optimization of both correction schemes. © Springer-Verlag GmbH Deutschland 2017.

关键词： Image reconstruction

来源：评论

学校读者我要写书评

暂无评论

SVM for semi-automatic selection of ICA components of electromyogenic artifacts in EEG data 15

引用

15th International Conference on Biomedical Engineering, ICBME 2013

作者： Gabsteiger, Florian Leutheuser, Heike Reis, Pedro Lochmann, Matthias Eskofier, Bjoern M. Digital Sports Group Pattern Recognition Lab Department of Computer Science University Erlangen-Nuremberg Germany Institute of Sport Science and Sport University Erlangen-Nuremberg Germany

ISBN: (纸本)9783319029122

Traditionally, electroencephalography (EEG) recorded during movement has been considered too noise prone to allow for sophisticated analysis. Superimposed electromyogenic activity interferes and masks the EEG signal. Presently, computational techniques such as Independent Component Analysis allow reduction of these artifacts. However, to date, it is relied on the user to select the artifact-contaminated components to reject. To automate this process and to reduce user dependent factors, we trained a support vector machine (SVM) to assist the user in choosing the independent components (ICs) most influenced by electromyogenic artifacts. We designed and conducted a study with specific neck and body movement exercises and collected data from five human participants (35 datasets total). After preprocessing, we decomposed the data by applying the Adaptive Mixture of Independent Component Analysis (AMICA) algorithm. An expert labeled the ICs found in the EEG recordings after decomposition as either ‘myogenic activity’ or ‘non-myogenic activity’. Afterwards, the classifier was evaluated on the dataset of one participant, whose data were not used in the training phase, and obtained 93% sensitivity and 96% specificity. Our study was designed to cover a diverse selection of exercises that stimulate the musculature that most interferes in EEG recordings during movement. This selection should produce similar artifact patterns as seen in most exercises or movements. Although unfamiliar exercises could result in worse classification performance, the results are expected to be equivalent to ours. Our study showed that this tool can help EEG analysis by reliably and efficiently choosing electromyogenic artifact contaminated components after AMICA decomposition, ultimately increasing the speed of data processing. © Springer International Publishing Switzerland 2014.

关键词： Support vector machines

来源：评论

学校读者我要写书评

暂无评论

Evaluation of diffusion techniques for improved vessel visualization and quantification in three-dimensional rotational angiography 4th

引用

4th International Conference on Medical Image Computing and computer-Assisted Intervention, MICCAI 2001

作者： Meijering, Erik Niessen, Wiro Weickert, Joachim Viergever, Max Image Sciences Institute University Medical Center Utrecht Heidelberglaan 100 UtrechtNL-3584 CX Netherlands Computer Vision Graphics and Pattern Recognition Group Department of Mathematics and Computer Science University of Mannheim MannheimD-68131 Germany

ISBN: (纸本)3540426973

Three-dimensional rotational angiography (3DRA) is a promising imaging technique which yields high-resolution isotropic 3D images of vascular structures. Raw 3DRA images, however, usually suffer from a high noise level and the presence of other artifacts. For accurate visualization and quantification of vascular anomalies, noise reduction is therefore highly desirable. In this paper we analyze the effects of several linear and nonlinear filtering techniques for that purpose. From the results of in vitro experiments we conclude that edge-enhancing anisotropic diffusion is very suitable for mentioned tasks. However, in view of the computational requirements of this technique, the regularized isotropic nonlinear diffusion scheme may be considered a useful alternative. © Springer-Verlag Berlin Heidelberg 2001.

关键词： Diffusion

来源：评论

学校读者我要写书评

暂无评论

Hidden Markov model-unscented Kalman filter contour tracking: A multi-cue and multi-resolution approach

Hidden Markov model-unscented Kalman filter contour tracking...

引用

Iranian Machine vision and Image Processing (MVIP)

作者： Fatemeh Moayedi Alireza Kazemi Zohreh Azimifar Computer Vision and Pattern Recognition Group School of Electrical and Computer Engineering Shiraz University Shiraz Iran

This paper present a novel attempt to introduce an HMM-based multi-resolution and multi-cue segmentation in combination with the unscented Kalman filter tracking method. It combines multiple features distribution and multiple resolutions to facilitate 2D video tracking. The advantages of this method lie in its speed and its robustness. Speed is dramatically improved by taking into account multiple resolutions which reduce number of measurement points (number of HMM states) while keeping its quality. Robustness is achieved by using multiple cues. We propose an algorithm to find an optimal operating point for a tracker in terms of the image scale. Furthermore, we propose a faster multi-scale (spatial) tracker based on a minimum acceptable performance limit. The proposed method is demonstrated on human head tracking with a non-stationary camera. Visual tests indicate that the optimized algorithms produce qualitatively better results. Results show that we are able to maintain real-time processing on quite generous video resolutions. Therefore it will be shown that our approach is faster and more efficient than conventional UKF and UKF with multi-cue.

关键词： Hidden Markov models Target tracking Pixel Visualization Kalman filters Image edge detection

来源：评论

学校读者我要写书评

暂无评论

Multiple objects segmentation based on maximum-likelihood estimation and optimum entropy-distribution(MLE-OED)

Multiple objects segmentation based on maximum-likelihood es...

引用

16th International Conference on pattern recognition, ICPR 2002

作者： Jun, Xie Tsui, H.T. Deshen, Xia Image Processing and Computer Vision Lab Department of Electronic Engineering Chinese University of Hong Kong Hong Kong Pattern Recognition Lab Department of Computer Science Nanjing University of Sci.andTech. China

ISBN: (纸本)0769516963

A new method based on MLE-OED is proposed for unsupervised image segmentation of multiple objects which have fuzzy edges. It adjusts the parameters of a mixture of Gaussian distributions via minimizing a new loss function proposed to implement image segmentation based on the image's local spatial information and global intensity distribution properties . The loss function consists of two terms: a local content fitting term, which optimizes the entropy distribution, and a global statistical fitting term, which maximizes the likelihood of the parameters for the given data. The proposed segmentation method was validated by simulated and real examples Its performance in the experiments is better than those of two popular methods. © 2002 IEEE.

关键词： Maximum likelihood estimation

来源：评论

学校读者我要写书评

暂无评论

Fully-automated analysis of muscle fiber images with combined region and edge-based active contours

Fully-automated analysis of muscle fiber images with combine...

引用

Workshops Bildverarbeitung fur die Medizin: Algorithmen Systeme Anwendungen, BVM 2006 - Workshop on Image Processing for Medicine: Algorithms - Systems - Applications, BVM 2006

作者： Brox, Thomas Kim, Yoo-Jin Weickert, Joachim Feiden, Wolfgang Computer Vision and Pattern Recognition Group University of Bonn Romerstr. 164 53117 Bonn Germany Institute of Neuropathology Saarland University 66421 Homburg/Saar Germany Math. Image Analysis Group Saarland University 66041 Saarbrücken Germany

ISBN: (纸本)9783540321361

This paper presents a new approach to automated muscle fiber analysis based on segmenting myofibers with combined region and edge-based active contours. It provides reliable and fully-automated processing, thus, enabling time-saving batch processing of the entire biopsy sample stemming from routinely HE-stained cryostat sections. The method combines color, texture, and edge cues in a level set based active contour model succeeded by a refinement with morphological filters. Falsepositive segmentations as compared to former methods are minimized. A quantitative comparison between manual and automated analysis of muscle fibers images did not reveal any significant differences. We gratefully acknowledge partial funding by the DFG.

关键词： Batch data processing

来源：评论

学校读者我要写书评

暂无评论

Diffusion-snakes: combining statistical shape knowledge and image information in a variational framework

Diffusion-snakes: combining statistical shape knowledge and ...

引用

IEEE Workshop on Variational and Level Set Methods in computer vision

作者： D. Cremers C. Schnorr J. Weickert Computer Vision Graphics and Pattern Recognition Group Department of Mathematics and Computer Science University of Mannheim Mannheim Germany

ISBN: (纸本)076951278X

We present a modification of the Mumford-Shah functional and its cartoon limit which allows the incorporation of statistical shape knowledge in a single energy functional. We show segmentation results on artificial and real-world images with and without prior shape information. In the case of occlusion and strongly cluttered background the shape prior significantly improves segmentation. Finally we compare our results to those obtained by a level-set implementation of geodesic active contours.

关键词： Image segmentation Level set Active contours Spline Shape control Active shape model Equations Statistical learning computer vision computer graphics

来源：评论

学校读者我要写书评

暂无评论

Generalized Multimodal Fusion via Poisson-Nernst-Planck Equation

arXiv

引用

arXiv 2024年

作者： Xiong, Jiayu Wang, Jing Xiang, Hengjing Xue, Jun Xu, Chen Jiang, Zhouqiang Xiamen Key Lab of Computer Vision and Pattern Recognition Huaqiao University Xiamen China

Previous studies have highlighted significant advancements in multimodal fusion. Nevertheless, such methods often encounter challenges regarding the efficacy of feature extraction, data integrity, consistency of feature dimensions, and adaptability across various downstream tasks. This paper proposes a generalized multimodal fusion method (GMF) via the Poisson-Nernst-Planck (PNP) equation, which adeptly addresses the aforementioned issues. Theoretically, the optimization objective for traditional multimodal tasks is formulated and redefined by integrating information entropy and the flow of gradient backward step. Leveraging these theoretical insights, the PNP equation is applied to feature fusion, rethinking multimodal features through the framework of charged particles in physics and controlling their movement through dissociation, concentration, and reconstruction. Building on these theoretical foundations, GMF disassociated features which extracted by the unimodal feature extractor into modality-specific and modality-invariant subspaces, thereby reducing mutual information and subsequently lowering the entropy of downstream tasks. The identifiability of the feature’s origin enables our approach to function independently as a frontend, seamlessly integrated with a simple concatenation backend, or serve as a prerequisite for other modules. Experimental results on multiple downstream tasks show that the proposed GMF achieves performance close to the state-of-the-art (SOTA) accuracy while utilizing fewer parameters and computational resources. Furthermore, by integrating GMF with advanced fusion methods, we surpass the SOTA results. © 2024, CC BY-NC-SA.

关键词： Poisson equation

来源：评论

学校读者我要写书评

暂无评论

Clustering Web images with multi-modal features

Clustering Web images with multi-modal features

引用

15th ACM International Conference on Multimedia, MM'07

作者： Rege, Manjeet Dong, Ming Hua, Jing Machine Vision Pattern Recognition Lab. Department of Computer Science Wayne State University Detroit MI 48202 United States Graphics and Imaging Lab. Department of Computer Science Wayne State University Detroit MI 48202 United States

ISBN: (纸本)9781595937025

Web image clustering has drawn significant attention in the research community recently. However, not much work has been done in using multi-modal information for clustering Web images. In this paper, we address the problem of Web image clustering by simultaneous integration of visual and textual features from a graph partitioning perspective. In particular, we modelled visual features, images, and words from the surrounding text of the images using a tripartite graph. This graph is actually considered as a fusion of two bipartite graphs that are partitioned simultaneously by the proposed Consistent Isoperimetric High-order Co-clustering(CIHC) framework. Although a similar approach has been adopted before, the main contribution of this work lies in the computational efficiency, quality in Web image clustering and scalability to large image repositories that CIHC is able to achieve. We demonstrate this through experimental results performed on real Web images. Copyright 2007 ACM.

关键词： Image analysis

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：