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

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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

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

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

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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

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

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

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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

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

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

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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

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Deriving semantics for image clustering from accumulated user feedbacks

Deriving semantics for image clustering from accumulated use...

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15th ACM International Conference on Multimedia, MM'07

作者： Chen, Yanhua Rege, Manjeet Dong, Ming Fotouhi, Farshad Machine Vision Pattern Recognition Lab. Department of Computer Science Wayne State University Detroit MI 48202 United States Database and Multimedia Systems Group Department of Computer Science Wayne State University Detroit MI 48202 United States

ISBN: (纸本)9781595937025

Image clustering solely based on visual features without any knowledge or background information suffers from the problem of semantic gap. In this paper, we propose SS-NMF: a semi-supervised non-negative matrix factorization framework for image clustering. Accumulated relevance feedback in a CBIR system is treated as user provided supervision for guiding the image clustering. We consider the set of positive images in the feedback as constraints on the clustering specifying that the images "must" be clustered together. Similarly, negative images provide constraints specifying that they "cannot" be clustered along with the positive images. Through an iterative algorithm, we perform symmetric tri-factorization of the image-image similarity matrix to infer the clustering. Theoretically, we prove the correctness of SS-NMF by showing that the algorithm is guaranteed to converge. Through experiments conducted on general purpose image datasets, we demonstrate the superior performance of SS-NMF for clustering images effectively. Copyright 2007 ACM.

关键词： Image analysis

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Head detection and localization from sparse 3D data

Head detection and localization from sparse 3D data

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24th German Association for pattern recognition Symposium, DAGM 2002

作者： Clabian, Markus Rötzer, Harald Bischof, Horst Kropatsch, Walter Advanced Computer Vision GmbH Donau City Str.1 ViennaA-1220 Austria Institute for Computer Graphics and Vision Graz University of Technology Inffeldgasse 16 GrazA-8010 Austria Pattern Recognition and Image Processing Group Institute of Computer Aided Automation Computer Science Department Vienna University of Technology Favoritenstr. 9 ViennaA-1040 Austria

ISBN: (纸本)354044209X

Head detection is an important, but difficult task, if no restrictions such as static illumination, frontal face appearance or uniform background can be assumed. We present a system that is able to perform head detection under very general conditions by employing a 3D measurement system namely a structured light distance measurement. An algorithm of head detection from sparse 3D data (19x19 data points) is developed that reconstructs a 3D surface over the image plane and detects head hypotheses of ellipsoidal shape. We demonstrate that detection and rough localization is possible in up to 90% of the images. © Springer-Verlag Berlin Heidelberg 2002.

关键词： computers

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Sparse MRF appearance models for fast anatomical structure localisation

Sparse MRF appearance models for fast anatomical structure l...

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2007 18th British Machine vision Conference, BMVC 2007

作者： Donner, René Mičušsík, Branislav Langs, Georg Bischof, Horst Institute for Computer Graphics and Vision Graz University of Technology Austria Pattern Recognition and Image Processing Group Vienna University of Technology Austria GALEN Group Laboratoire de Mathématiques Appliquées Aux Systèmes Ecole Centrale de Paris France

ISBN: (纸本)1901725340

Image segmentation methods like active shape models, active appearance models or snakes require an initialisation that guarantees a considerable overlap with the object to be segmented. In this paper we present an approach that localises anatomical structures in a global manner by means of Markov Random Fields (MRF). It does not need initialisation, but finds the most plausible match of the query structure in the image. It provides for precise, reliable and fast detection of the structure and can serve as initialisation for more detailed segmentation steps. Sparse MRF Appearance Models (SAMs) encode a priori information about the geometric configurations of interest points, local features at these points and local features along the edges of adjacent points. This information is used to formulate a Markov Random Field and the mapping of the modeled object (e.g. a sequence of vertebrae) to the query image interest points is performed by the MAX-SUM algorithm. The local image information is captured by novel symmetry-based interest points and local descriptors derived from Gradient Vector Flow. Experimental results are reported for two data-sets showing the applicability to complex medical data.

关键词： Image segmentation

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Minimizing the topological structure of line images 7th

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7th Joint IAPR International Workshop on Structural and Syntactic pattern recognition, SSPR 1998 and 2nd International Workshop on Statistical Techniques in pattern recognition, SPR 1998

作者： Kropatsch, Walter G. Burge, Mark Vienna University of Technology Institute of Automation 183/2 Pattern Recognition and Image Processing Group Treitlstr.3 WienA-1040 Austria Johannes Kepler University Institute of Systems Science Computer Vision Laboratory LinzA-4040 Austria

ISBN: (纸本)3540648585

We present a new algorithm based on Dual Graph Contraction (DGC) to transform the Run Graph into its Minimum Line Property Preserving (MLPP) form which, when implemented in parallel, requires O(log(longestcurve)) steps. A MLPP graph of a line image compliments the structural information in geometric graph representations like the run graph. Using such a graph and its dual, line image analysis systems can efficiently detect topological features like loops and holes and make use of relations like containment. © Springer-Verlag Berlin Heidelberg 1998.

关键词： Topology

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Hierarchical top down enhancement of robust PCA 9th

引用

Joint IAPR 9th International Workshop on Structural and Syntactic pattern recognition, SSPR 2002 and 4th International Workshop on Statistical Techniques in pattern recognition, SPR 2002

作者： Langs, Georg Bischof, Horst Kropatsch, Walter G. Pattern Recognition and Image Processing Group 183/2 Institute for Computer Aided Automation Vienna University of Technology Favoritenstr. 9 ViennaA-1040 Austria Institute for Computer Graphics and Vision TU Graz Inffeldgasse 16 2.OG GrazA-8010 Austria

ISBN: (纸本)3540440119

In this paper we deal with performance improvement of robust PCA algorithms by replacing regular subsampling of images by an irregular image pyramid adapted to the expected image content. The irregular pyramid is a structure built based on knowledge gained from the training set of images. It represents different regions of the image with different level of detail, depending on their importance for reconstruction. This strategy enables us to improve reconstruction results and therefore the recognition significantly. The training algorithm works on the data necessary to perform robust PCA and therefore requires no additional input. © Springer-Verlag Berlin Heidelberg 2002.

关键词： Image enhancement

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Kernel-based recognition of Human Actions Using Spatiotemporal Salient Points

Kernel-based Recognition of Human Actions Using Spatiotempor...

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IEEE computer Society Conference on computer vision and pattern recognition Workshops (CVPRW)

作者： A. Oikonomopoulos I. Patras M. Pantic Computing Department Imperial College London UK Computer Vision and Pattern Recognition Group University of York UK

This paper addresses the problem of human action recognition by introducing a sparse representation of image sequences as a collection of spatiotemporal events that are localized at points that are salient both in space and time. We detect the spatiotemporal salient points by measuring the variations in the information content of pixel neighborhoods not only in space but also in time. We derive a suitable distance measure between the representations, which is based on the Chamfer distance, and we optimize this measure with respect to a number of temporal and scaling parameters. In this way we achieve invariance against scaling, while at the same time, we eliminate the temporal differences between the representations. We use Relevance Vector Machines (RVM) in order to address the classification problem. We propose new kernels for use by the RVM, which are specifically tailored to the proposed spatiotemporal salient point representation. The basis of these kernels is the optimized Chamfer distance of the previous step. We present results on real image sequences from a small database depicting people performing 19 aerobic exercises.

关键词： Humans Spatiotemporal phenomena pattern recognition computer vision Image recognition Image sequences Detectors Entropy Image retrieval Educational institutions

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