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检索条件"机构=Vienna University of Technology Pattern Recognition and Image Processing"

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Background removal in dental panoramic X-ray images by the A-Trous multiresolution transform

Background removal in dental panoramic X-ray images by the A...

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2005 European Conference on Circuit Theory and Design

作者： Goebel, Peter Michael Belbachir, Ahmed Nabil Truppe, Michael Fh-campus Wien Technical Project- and Process Management Ettenreichgasse 45a 1100 Vienna Austria Vienna University of Technology Pattern Recognition and Image Procesing Group Vienna Austria Karl Landsteiner Institute for Biotelematics Danube University Krems A-3500 Krems Austria

ISBN: (纸本)0780390660

Dental Panoramic X-ray images are images having complex content, because several layers of tissue, bone, fat, etc. are superimposed. Non-uniform illumination, stemming from the X-ray source, gives extra modulation to the image, which causes spatially varying X-ray photon density. The interaction of the X-ray photons with the density of matter causes spatially coherent varying noise contribution. Many algorithms exist to compensate background effects, by pixel based or global methods. However, if the image is contaminated by a non-negligible amount of noise, that is usually non-Gausslan, the methods cannot approximate the background efficiently. In this paper, a dedicated approach for the removal of a multiplying background is presented using polynomial scaling and the A-Trous multiresolution transform. The new method uses a background image and a diagnostic image together to estimate the density of the diagnostic content. It assumes a locally Gaussian statistic scaled by a hidden factor, where the hidden factor represents the variance of the non-Gaussian process of image generation. Because, the method also removes noise from the compound signal, a comparison to a standard denoising method is given. This approach has been tested on 50 images from a database of panoramic X-ray images where the results are cross validated by medical experts.

关键词： Medical imaging

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Noise estimation in panoramic x-ray images: an application analysis approach

Noise estimation in panoramic x-ray images: an application a...

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IEEE/SP Workshop on Statistical Signal processing (SSP)

作者： P.M. Goebel A.N. Belbachir M. Truppe Pattern Recognition and Image Processing Group University of Technology Vienna Vienna Austria Karl Landsteiner Institute for Biotelematics Danube-University of Krems Krems Austria

This paper presents an appropriate approach for the robust estimation of the noise statistics in dental panoramic X-ray images. To achieve maximum image quality after denoising, a semi-empirical scatter model is presented, leading to a local adaptive Gaussian scale mixture (GSM) model. State of the art methods use multiscale filtering of images to reduce the irrelevant part of information, based on generic estimation of noise. The usual assumption of a distribution of Gaussian and Poisson statistics only leads to overestimation of the noise variance in regions of low intensity (small photon counts), but to underestimation in regions of high intensity and therefore to non-optimal results. The analysis approach is tested on a database of 50 panoramic X-ray images and the results are cross-validated by medical experts. It is shown that the local standard deviation (SDEV) in images, stemming from homogeneous phantoms (AI, PMMA), follows a generalized Nakagami distribution (GND). The heavily tailed distribution is not covered entirely by the GND. The error density function, is hypothesized to stem from scatter-glare, degrading the image. A beam stop method, for estimation of the scatter-glare amount, verifies that hypothesis. Finally, the application of the method for a phantom image, is shown with denoising results for comparison purpose, followed by the conclusion

关键词： X-ray imaging image analysis Noise reduction Electromagnetic scattering Particle scattering X-ray scattering Imaging phantoms Noise robustness Statistics Dentistry

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Pedestrian Tracking Based on Colour and Spatial Information

Pedestrian Tracking Based on Colour and Spatial Information

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Proceedings of the Digital image Computing: Technqiues and Applications (DICTA)

作者： F.H. Seitner B.C. Lovell Pattern Recognition and Image Processing Group University of Technology Vienna Austria National ICT Australia and School of ITEE University of Queensland Australia

This paper describes a tracking with appearance modelling system for pedestrians. A cascade of boosted classifiers and Haar-like rectangular features [6, 12] are used for the pedestrian detection. Statistical modelling in the HSV colour space is used for adaptive background modelling and subtraction, where the use of circular statistics for hue is proposed. By using the background model in combination with the detector, the system extracts a feature vector based on colour statistics and the spatial information. Circular [9] and linear statistics are applied on the extracted features to robustly track the pedestrians and other moving objects through the scene. An adaptive appearance model copes with partial or full occlusions and addresses the problem of missing or wrong detections in single frames.

关键词： Statistics Data mining Feature extraction image color analysis Detectors pattern recognition image processing Australia Vectors Robustness

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Two-band Infrared Data Fusion Method Based-on Fractal Dimension

Two-band Infrared Data Fusion Method Based-on Fractal Dimens...

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2005 International Conference on Neural Networks and Brain(ICNN&B’05)

作者： Yuqiu Sun Jinwen Tian Jian Liu Image Processing and Intelligence Control Key Laboratory of State Educational Ministry Institute of Pattern Recognition and Artificial IntelligenceHuazhong University of Science and Technology School of information and mathematics Yangtze University Dept.of Electronic Information and Enginery of Huazhong University of Science and Technology

ISBN: (纸本)0780394224

＜正＞Researches indicate that the gray-scale images mapped from most nature objects accord to the fractal Brown stochastic field which foundation is self-similarity,an image is self-similarity means the image is made up of copies of itself in a reduced *** fractal dimension can quantificational depict the fractal character and the property of an image. Therefore,a new method based-on fractal dimension to fuse mid-wave infrared images and long-wave infrared images is presented in the ***,images are decomposed into different scale using wavelet ***,the fractal dimensions of sub-images of original images are computed,and then take them as weight to fuse sub-images at the same level according to different ***,reconstruct the fused images by wavelet inverse *** experimental results imply that the method can effectively preserve the information of source images with a high contrast and may be very practical.

关键词： Fractals Infrared imaging Artificial intelligence Fuses Educational technology image reconstruction Layout Infrared detectors Intelligent control Process control

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Optimal sub-shape models by minimum description length

Optimal sub-shape models by minimum description length

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Conference on Computer Vision and pattern recognition (CVPR)

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

Active shape models are powerful and widely used tool to interpret complex image data. By building models of shape variation they enable search algorithms to use a priori knowledge in an efficient and gainful way. However, due to the linearity of PCA, non-linearities like rotations or independently moving sub-parts in the data can deteriorate the resulting model considerably. Although non-linear extensions of active shape models have been proposed and application specific solutions have been used, they still need a certain amount of user interaction during model building. In this paper the task of building/choosing optimal models is tackled in a more generic information theoretic fashion. In particular, we propose an algorithm based on the minimum description length principle to find an optimal subdivision of the data into sub-parts, each adequate for linear modeling. This results in an overall more compact model configuration. Which in turn leads to a better model in terms of modes of variations. The proposed method is evaluated on synthetic data, medical images and hand contours.

关键词： Active shape model Biomedical imaging Principal component analysis Computer vision Linearity Polynomials pattern recognition Computer graphics Radiology image processing

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Error analysis and improvements of Spectral Angle Mapper (SAM) model

Error analysis and improvements of Spectral Angle Mapper (SA...

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MIPPR 2005: SAR and Multispectral image processing

作者： Du, Peijun Chen, Yunhao Fang, Tao Tang, Hong Department of RS and GISCi China University of Mining and Technology Xuzhou City Jiangsu Province 221008 China College of Resources Science Beijing Normal University Beijing 100875 Institute of Image Processing and Pattern Recognition Shanghai Jiaotong University Shanghai 200030 China

Spectral Angle Mapper (SAM) model has got wide applications in hyperspectral Remote Sensing (RS) information processing. But Spectral Angle couldn't achieve satisfied performance in some cases because of its sensitivity to noises and uncertainty. Based on the analysis to traditional SAM algorithm, four types of errors and their impacts to spectral angle are investigated. In order to reduce the impacts of above errors, some improved algorithms are proposed and experimented. The first improved algorithm is grouping spectral angle algorithm. In this new algorithm all bands are divided into two sets by odd and even bands, that means two additional sub-vectors are created in addition to the original spectral vector. So three spectral angles will be computed and the minimum of three indexes is used as final index. The second improved algorithm is normalized spectral angle. In this way spectral angle is computed to the normalized vectors of two original vectors. Two approaches are used to normalize the spectral vector, and spectral angle is computed to the normalized vectors. This algorithm is able to decrease the impacts of random errors. The third algorithm is intersected spectral angle. Spectral angle is calculated by a spectral displacement strategy in this approach. That means a given displacement to change the corresponding bands of two spectral vectors is used and a spectral angle to the displaced vectors will be got. By this displacement strategy the impacts of band offset is reduced. Finally some experiments are used to test those improved algorithms. It proves that those new approaches can reduce and control the errors and improve the precision and reliability of similarity measure.

关键词： Remote sensing

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Efficient VLSI architecture for multi-dimensional discrete wavelet transform

Efficient VLSI architecture for multi-dimensional discrete w...

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MIPPR 2005: SAR and Multispectral image processing

作者： Xiong, Cheng-Yi Tian, Jin-Wen Liu, Jian College of Electronic Information Engineering South-Center University for Nationalities Wuhan 430074 China Institute of Pattern Recognition and Artificial Intelligence Key Laboratory of Education Ministry for Image Processing and Intelligent Control Huazhong University of Science and Technology Wuhan 430074 China

Efficient VLSI architectures for multi-dimensional (m-D) discrete wavelet transform (DWT), e.g. m=2, 3, are presented, in which the lifting scheme of DWT is used to reduce efficiently hardware complexity. The parallelism of 2 m subbands transforms in lifting-based m-D DWT is explored, which increases efficiently the throughput rate of separable m-D DWT. The proposed architecture is composed of m2m-1 1-D DWT modules working in parallel and pipelined, which is designed to process 2m input samples per clock cycle, and generate 2m subbands coefficients synchronously. The total time of computing one level of decomposition for a 2-D image (3-D image sequence) of size N2 (MN2) is approximately N2/4 (MN2/8) intra- clock cycles (ccs). An efficient line-based architecture framework for both 2D+t and t+2D 3-D DWT is first proposed. Compared with the similar works reported in previous literature, the proposed architecture has good performance in terms of production of computation time and hardware cost. The proposed architecture is simple, regular, scalable and well suited for VLSI implementation.

关键词： VLSI circuits

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On the detection of individuals and vehicles on an airport's apron

On the detection of individuals and vehicles on an airport's...

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IEE International Conference on Visual Information Engineering (VIE 2005)

作者： Josep Aguilera Antequera Martin Kampel Vienna University of Technology Institute of Computer Aided Automation Pattern Recognition and Image Processing Group Favoritenstr.9 183-2 A-1 040 Vienna Austria

This paper gives a robust motion detection and tracking solution for a video surveillance application on an airport's apron. As an outdoor application, the system must be capable of adapting to a wide range of weather conditions and illumination changes, furthermore, the achromaticity of the scene and the presence of occlusions in the tracking process are issues considered in the selection of the motion detector and tracking system respectively. We propose an adapted mixture of Gaussiani model with RGB colour normalisation to detect mobile objects in the scene and a region tracking method based on significant mobile object features to track individuals and vehicles on the selected airport's apron. The performance of the proposed motion detector is evaluated using pixel-based performance metrics and compared with other existing methods. The capability of the application to handle partial occlusions is tested on the region tracker.

关键词： Tracking Airports Surveillance Adaptation models Visualization Motion detection Atmospheric modeling

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A framework for the uncertain spatial data mining

A framework for the uncertain spatial data mining

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International Conference on Space Information technology

作者： He, Binbin Fang, Tao Guo, Dazhi Institue of Geo-Spatial Information Science and Technology University of Electronic Science and Technology of China Chengdu 610054 China Institute of Image Processing and Pattern Recognition Shanghai Jiao Tong University Shanghai 200030 China School of Environment and Spatial Informatics China University of Mining and Technology Xuzhou 221008 China

ISBN: (纸本)0819460079

On the basis of analyzing the uncertainties of spatial data mining (SDM), and in view of the limits of traditional spatial data mining, the framework for the uncertain spatial data mining has been founded. For which, four key problems have been probed and analyzed, including uncertainty simulation of spatial data with Monte Carlo method, measurement of spatial autocorrelation based on uncertain spatial positional data, discretization of continuous data based on neighborhood EM algorithm and quality assessment of results. Meanwhile, the experiments concerned have been performed using the geo-spatial datum gotten from 37 typified cites in China.

关键词： Data mining

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Background suppression based-on wavelet transformation to detect infrared target

Background suppression based-on wavelet transformation to de...

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International Conference on Machine Learning and Cybernetics, ICMLC 2005

作者： Sun, Y.U.-Qiu Tian, Jin-Wen Liu, Jian School of Information and Mathematics Yangtze University Jingzhou 434023 China State Education Commission Key Laboratory for Image Processing and Intelligent Control Institute for Pattern Recognition and Artificial Intelligence Huazhong University of Science and Technology Wuhan 430074 China State Education Commission Key Laboratory for Image Processing and Intelligent Control Dept. of Electronic Information and Enginery Huazhong University of Science and Technology Wuhan China

ISBN: (纸本)078039092X

Target detection and location in infrared clutter background is very important to infrared search and track system. Especially for small target detection in infrared image in background of sea and sky, there are no geometric and structure character to use. When targets such as ship and naval vessel sailing at long distance, they always appear around the sea-level line, and it is mixed with cloud and sea-wave clutter. It is difficult to segment and locate precisely. Background suppression based-on wavelet transformation is proposed in the paper. Wavelet decomposition makes it possible to analyze a signal both in time and frequency domains. In the paper infrared sea and key background images are processed. For their SNR is low and background is complex, using wavelet transformation decomposes an original image and extract approximate feature to reconstruct an image, which mainly includes background information. A new image will be obtained using background image subtracted from original image. There are mainly target and noise points left in the new image. By setting proper threshold, the target can be detected perfectly. At last, experiment results are given and show the method is practical. © 2005 IEEE.

关键词： pattern recognition

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