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共 63 条记录，以下是31-40 订阅

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Mathematical modeling for corner saliency

Mathematical modeling for corner saliency

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International Conference on Software Technology and Engineering (ICSTE)

作者： Shao-hua Jiang Xue-jun Xu Hong-bo Zhu Xue-song Chen Institute of Image Recognition & Computer Vision Hunan Normal University Changsha China Digital Engineering & Simulation Research Center Huazhong University of Science and Technology Wuhan China

This paper proposed the definition of corner saliency firstly, which is an intrinsic characteristic of a corner and denote the contributiveness of a corner on the result of image processing. Then, we discussed the relation among the corner saliency, the out-in degree, the angle sequence and the ratio of line-length sequence in detail, and formulate a mathematic model to calculate the corner saliency. Finally, we introduced an experiment to illustrate the calculation of corner saliency. It shows that the proposed model is effective and has practical application value also.

关键词： Mathematical model Object recognition image edge detection Software image fusion Correlation

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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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Local Structure Detection with Orientation-invariant Radial Configuration

Local Structure Detection with Orientation-invariant Radial ...

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Conference on computer vision and Pattern recognition (CVPR)

作者： Lech Szumilas Rene Donner Georg Langs Allan Hanbury Pattern Recognition and Image Processing Group University of Technology Vienna Vienna Austria Institute for Computer Graphics and Vision Graz University of Technology Graz Austria

Local image descriptors have proved themselves as useful tools for many computer vision tasks such as matching points between multiple images of a scene and object recognition. Current descriptors, such as SIFT, are designed to match image features with unique local neighborhoods. However, the interest point detectors used with SIFT often fail to select perceptible local structures in the image, and the SIFT descriptor does not directly encode the local neighborhood shape. In this paper we propose a symmetry based interest point detector and radial local structure descriptor which consistently captures the majority of basic local image structures and provides a geometrical description of the structure boundaries. This approach concentrates on the extraction of shape properties in image patches, which are an intuitive way to represent local appearance for matching and classification. We explore the specificity and sensitivity of this local descriptor in the context of classification of natural patterns. The implications of the performance comparison with standard approaches like SIFT are discussed.

关键词： Detectors Shape computer vision Layout Object recognition

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A comparision of Model-based methods for knee cartilage segmentation

A comparision of Model-based methods for knee cartilage segm...

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2nd International Conference on computer vision Theory and Applications, VISAPP 2007

作者： Cheong, J. Faggian, N. Langs, G. Suter, D. Cicuttini, F. Dept. of Electrical Dept. of Computer Systems Engineering Monash University Australia Clayton School of Information Technology Monash University Australia Institute for Computer Graphics and Vision Graz University of Technology Austria Pattern Recognition and Image Processing Group Vienna University of Technology Austria Dept. of Epidemiology Dept. of Preventive Medicine Monash University Australia

Osteoarthritis is a chronic and crippling disease affecting an increasing number of people each year. With no known cure, it is expected to reach epidemic proportions in the near future. Accurate segmentation of knee cartilage from magnetic resonance imaging (MRI) scans facilitates the measurement of cartilage volume present in a patient's knee, thus enabling medical clinicians to detect the onset of osteoarthritis and also crucially, to study its effects. This paper compares four model-based segmentation methods popular for medical data segmentation, namely Active Shape Models (ASM) (Cootes et al., 1995), Active Appearance Models (AAM) (Cootes et al., 2001), Patch-based Active Appearance Models (PAAM) (Faggian et al., 2006), and Active Feature Models (AFM) (Langs et al., 2006). A comprehensive analysis of how accurately these methods segment human tibial cartilage is presented. The results obtained were benchmarked against the current "gold standard" (cartilage segmented manually by trained clinicians) and indicate that modeling local texture features around each landmark provides the best results for segmenting human tibial cartilage.

关键词： Medical imaging

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Motion Analysis of Endovascular Stent-Grafts by MDL Based Registration

Motion Analysis of Endovascular Stent-Grafts by MDL Based Re...

引用

International Conference on computer vision (ICCV)

作者： Georg Langs Nikos Paragios Rene Donner Pascal Desgranges Alain Rahmouni Hicham Kobeiter GALEN Group Laboratoire de Mathématiques Appliquées aux Systàmes Ecole Centrale de Paris France Institute for Computer Graphics and Vision Graz University of Technology Austria Pattern Recognition and Image Processing Group University of Technology Vienna Austria University of Paris XII Paris France

The endovascular repair of a traumatic rupture of the thoracic aorta - that would otherwise lead to the death of the patient - is performed by delivering a stent-graft into the vessel at the rupture location. The age range of the affected patients is large and the stent-graft will stay in the body for the remaining life. The technique is relatively new, and no experience with regard to long-term effects, and durability exists. To predict long-term complications, such as ruptures or destructive interactions with surrounding tissue during the life of the patient, it is important to understand the - rather intense and constant - movement of the stent- graft during the cardiac cycle. A computed tomography with heart gating (gated CT) acquires sequences that show the region of the stent-graft at different time points. We analyze the motion of stent-grafts with a model based approach. Stent-grafts are represented as sparse sets of axis points extracted from the gated CT, and motion patterns are captured by a minimum description length based group-wise registration of the stent-graft at different time points. No parameterization or a priori definition of the topology is necessary, and highly variable elasticity properties in the data volume can by accounted for by the sparse statistical model, that captures correlations and motion components of the stent-graft. We report results for deformation models and registration accuracy for 5 patients.

关键词： Motion analysis Computed tomography Biomedical imaging Heart Buildings computer graphics Pattern recognition image processing Hospitals Data mining

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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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3D and Infrared Face Reconstruction from RGB data using Canonical Correlation Analysis

3D and Infrared Face Reconstruction from RGB data using Cano...

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International Conference on Pattern recognition

作者： M. Reiter R. Donner G. Langs H. Bischof Pattern Recognition and Image Processing Group Vienna University of Technology Austria Graz University of Technology Institute for Computer Graphics and Vision Austria

In this paper, we apply a multiple regression method based on canonical correlation analysis (CCA) to face data modelling. CCA is a factor analysis method which exploits the correlation between two high dimensional signals. We first use CCA to perform 3D face reconstruction and in a separate application we predict near-infrared (NIR) face texture. In both cases, the input data are color (RGB) face images. Experiments show, that due to the correlation between input and output signal, only a small number of canonical factors are needed to describe the functional relation of RGB images to the respective output (NIR images and 3D depth maps) with reasonable accuracy

关键词： Shape Face detection Lighting image reconstruction image analysis image databases Pattern analysis Rendering (computer graphics) image color analysis Pattern recognition

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Car Plate Detection Using Cascaded Tree-Style Learner Based on Hybrid Object Features

Car Plate Detection Using Cascaded Tree-Style Learner Based ...

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IEEE Conference on Advanced Video and Signal Based Surveillance (AVSS)

作者： Qiang Wu Huaifeng Zhang Wenjing Jia Xiangjian He Jie Yang Tom Hintz Computer Vision Research Group University of Technology Broadway NSW Australia Institute of Image Process. & Pattern Recognition Shanghai Jiaotong University China

Car plate detection is a key component in automatic license plate recognition system. This paper adopts an enhanced cascaded tree style learner framework for car plate detection using the hybrid object features including the simple statistical features and Harr-like features. The statistical features are useful for simplifying the process on cascade classifier. The cascaded tree-style detector design will further reduce the false alarm and the false dismissal while retaining a high detection ratio. The experimental results obtained by the proposed algorithm exhibit the encouraging performance.

关键词： Object detection Licenses Detectors computer vision Pattern recognition Lighting Interference Australia image recognition Robustness

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Active Feature Models

Active Feature Models

引用

International Conference on Pattern recognition

作者： G. Langs P. Peloschek R. Donner M. Reiter H. Bischof Institute for Computer Graphics and Vision Graz University of Technology Austria Pattern Recognition and Image Processing Group University of Technology Vienna Austria Department of Radiology Medical University of Vienna Austria

ISBN: (纸本)0769525210

In this paper active feature models are proposed. They utilize local texture features and a statistical shape model for the reliable localization of landmarks in images. They are related to active appearance models, but instead of modelling the entire texture of an object they represent image texture by means of local descriptors. The approach has advantages with complex image data like anatomical structures that exhibit high texture variation with limited relevance for the recognition of the object location. Experimental results and the comparison to AAMs on different data sets indicate that active feature models can improve search speed and result accuracy, considerably

关键词： Shape Active appearance model Biomedical imaging image texture image edge detection image texture analysis computer graphics Pattern recognition image processing Radiology

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Automatic Surveying of Cutaneous Hemangiomas

Automatic Surveying of Cutaneous Hemangiomas

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International Conference on Pattern recognition

作者： S. Zambanini G. Langs R. Sablatnig P. Donath H. Maier Pattern Recognition and Image Processing Group University of Technology Vienna Austria Institute for Computer Graphics and Vision Graz University of Technology Austria Division of Special and Environmental Dermatologyy Medical University of Vienna Austria

This paper presents a method for the fully automatic surveying of cutaneous hemangiomas by means of a hemangioma segmentation and a ruler visible in the images. The algorithm computes the spatial resolution of an image. Hemangioma segmentation is accomplished by a single-layer perceptron classification by means of pixel color features. The algorithm was evaluated on a set of 120 images. It achieves satisfactory results on images with clearly visible, saturated hemangiomas

关键词： image segmentation Neoplasms Pattern recognition Spatial resolution Lesions Area measurement Pixel image processing computer graphics Biomedical imaging

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