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International Conference on Pattern Recognition

作者： G. Langs P. Radeva D. Rotger F. Carreras Insitute for Computer Graphics and Vision Graz University of Technology Graz Austria Pattern Recognition and Image Processing Group Vienna University of Technology Vienna Austria Computer Vision Center Universitat Autònoma de Barcelona Bellaterra Spain Hospital St. Pau Barcelona Spain

In this paper, we address the problem of multimodal registration of coronary vessels by developing a 3D parametrical model of vessel trees from computer tomography data and registering it to angiography images during intervention. Thus, the interventionist takes profit from 3D data otherwise only available before the intervention. This facilitates orientation in ambiguous radiographs, interactive visualization of all vessel structures to estimate their mutual position and navigation within the vessel system and ultimately reduces the radiation the patient and the physicians are exposed to. The model is build by exploring the branching vessel tree starting from a single position and successively expanding through the vessels guided by a local deformable surface. The result is a tree of cylindrical segments each adapted to the vessel walls that is registered to angiography images in a fast and robust way. Validation on 8 patients confirms the robustness of our method.

关键词： X-ray imaging Computed tomography Robustness Angiography image segmentation image reconstruction Distortion measurement Tree graphs Data visualization Radio navigation

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Features extraction based on fuzzy clustering and segmentation onto the motion region for medium field surveillance application

Features extraction based on fuzzy clustering and segmentati...

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International Conference on computer Graphics, Imaging and Visualization, CGIV

作者： M.N. Maliki S.A.R.A.B. Al-Attas Computer Vision Video and Image Processing (CVVIP) Research Group Department of Microelectronic and Computer Engineering (MiCE) Faculty of Electrical Engineering Universiti Teknologi Malaysia Skudai Johor Malaysia Computer Vision Universiti Teknologi Malaysia Skudai Johor Malaysia Universiti Teknologi Malaysia Skudai Johor MY Dept. of Microelectron. & Comput. Eng. Univ. Teknologi Malaysia Johor Malaysia

Fuzzy-based clustering algorithm is becoming popular in image processing, especially in image segmentation. Based on the degree of the membership function, each pixel can be grouped into "natural group" of pixels according to the spatial coordinate using their values. In this work we present features extraction based on fuzzy clustering and segmentation onto the motion region for medium field surveillance application. Instead of using blob analysis, we believe that fuzzy based clustering algorithm, can also be used to generate different clusters. Incorporating the motion-based segmentation the complexity of the fuzzy clustering is also reduced because only the motion region is processed to the clustering algorithm.

关键词： Feature extraction Surveillance Clustering algorithms image segmentation Equations Prototypes Application software computer vision image processing Data mining

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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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Hierarchical top down enhancement of robust PCA 9th

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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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Mobile robot localization under varying illumination

Mobile robot localization under varying illumination

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作者： Jogan, Matjaž Leonardis, Aleš Wildenauer, Horst Bischof, Horst Faculty of Computer and Information Science University of Ljubljana Tržaška 25 1001 Ljubljana Slovenia Pattern Recognition and Image Processing Group Institute for Computer Aided Automation Vienna University of Technology Favoritenstrasse 9/1832 A-1040 Vienna Austria Institute for Computer Graphics and Vision Graz University of Technology Inffeldgasse 16 2. OG A-8010 Graz Austria

Methods for mobile robot localization that use eigenspaces of panoramic snapshots of the environment are in general sensitive to changes in the illumination of the environment. Therefore, we propose an approach which achieves a reliable localization under severe illumination conditions. The method uses gradient filtering of the eigenspace. After testing the approach on images obtained by a mobile robot, we show that it outperforms the standard eigenspace-based recognition method. © 2002 IEEE.

关键词： Mobile robots

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Mobile robot localization under varying illumination

Mobile robot localization under varying illumination

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16th International Conference on Pattern Recognition, ICPR 2002

作者： Jogan, Matjaž Leonardis, Aleš Wildenauer, Horst Bischof, Horst Faculty of Computer and Information Science University of Ljubljana Tržaška 25 Ljubljana1001 Slovenia Pattern Recognition and Image Processing Group Institute for Computer Aided Automation Vienna University of Technology Favoritenstrasse 9/1832 ViennaA-1040 Austria Institute for Computer Graphics and Vision Graz University of Technology Inffeldgasse 16 2. OG GrazA-8010 Austria

ISBN: (纸本)0769516963

Methods for mobile robot localization that use eigen spaces of panoramic snapshots of the environment are in general sensitive to changes in the illumination of the environment. Therefore, we propose an approach which achieves a reliable localization under severe illumination conditions. The method uses gradient filtering of the eigen space. After testing the approach on images obtained by a mobile robot, we show that it outperforms the standard eigen space-based recognition method. © 2002 IEEE.

关键词： Mobile robots

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Mobile robot localization under varying illumination

Mobile robot localization under varying illumination

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International Conference on Pattern Recognition

作者： M. Jogan A. Leonardis H. Wildenauer H. Bischof Faculty of Computer and Information Science University of Ljubljana Ljubljana Slovenia Pattern Recognition and Image Processing Group Institute for Computer Aided Automation University of Technology Vienna Vienna Austria Institute for Computer Graphics and Vision Graz University of Technology Graz Austria

关键词： Mobile robots Lighting computer vision Filter bank image recognition Indoor environments Pattern recognition image processing computer graphics Information science

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3-D reconstruction of the human jaw using space carving

3-D reconstruction of the human jaw using space carving

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IEEE International Conference on image processing

作者： M.T. Ahmed A.H. Eid A.A. Farag Computer Vision and Image Processing (CVIP) Laboratory University of Louisville Louisville KY USA

This paper presents a model-based vision system for dentistry that will assist in diagnosis, treatment planning and surgical simulation. Dentistry requires an accurate 3D representation of the teeth and jaws for diagnostic and treatment purposes. The proposed integrated computer vision system constructs a 3D model of the patient's dental occlusion using an intra-oral video camera. The space carving algorithm is used to reconstruct the shape of the human jaw. This algorithm provides more flexibility and eliminates several constraints imposed by other approaches like stereo. The system performance is investigated, and the results show acceptable reconstruction accuracy.

关键词： Three dimensional displays Humans Dentistry Machine vision Surgery Computational modeling Teeth computer vision Cameras Shape

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Memory efficient fingerprint verification

Memory efficient fingerprint verification

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IEEE International Conference on image processing

作者： C. Beleznai H. Ramoser B. Wachmann J. Birchbauer H. Bischof W. Kropatsch Advanced Computer Vision Austrian Research Centre Seibersdorf Vienna Austria Programm-und Systementwicklung Siemens AG Österreich Graz Austria Pattern Recognition and Image Processing Group University of Technology Vienna Vienna Austria

Fingerprint recognition and verification are often based on local fingerprint features, usually ridge endings or terminations, also called minutiae. By exploiting the structural uniqueness of the image region around a minutia, the fingerprint recognition performance can be significantly enhanced. However, for most fingerprint images the number of minutia image regions (MIRs) becomes dramatically large, which imposes - especially for embedded systems - an enormous memory requirement. Therefore, we are investigating different algorithms for compression of minutia regions. The requirement for these algorithms is to achieve a high compression rate (about 20) with minimum loss in the matching performance of minutia image region matching. We investigate the matching performance for compression algorithms based on the principal component and the wavelet transformation. The matching results are presented in form of normalized ROC curves and interpreted in terms of compression rates and the MIR dimension.

关键词： Fingerprint recognition image coding computer vision image recognition image processing image matching Embedded system Performance loss Biometrics Character recognition

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Digital topology of sets of convex voxels

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Graphical Models 2000年第5期62卷 343-352页

作者： Saha, Punam K. Rosenfeld, Azriel Medical Image Processing Group University of Pennsylvania Philadelphia PA 19104-6021 United States Computer Vision Laboratory Center for Automation Research University of Maryland College Park MA 20742-3275 United States

Classical digital geometry deals with sets of cubical voxels (or square pixels) that can share faces, edges, or vertices, but basic parts of digital geometry can be generalized to sets S of convex voxels (or pixels) that can have arbitrary intersections. In particular, it can be shown that if each voxel P of S has only finitely many neighbors (voxels of S that intersect P), and if any nonempty intersection of neighbors of P intersects P, then the neighborhood N(P) of every voxel P is simply connected and without cavities, and if the topology of N(P) does not change when P is deleted (i.e., P is a 'simple' voxel), then deletion of P does not change the topology of S.

关键词： Topology

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