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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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VISUAL OBJECT TRACKING WITH ONLINE LEARNING ON RIEMANNIAN MANIFOLDS BY ONE-CLASS SUPPORT VECTOR MACHINES

VISUAL OBJECT TRACKING WITH ONLINE LEARNING ON RIEMANNIAN MA...

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IEEE International Conference on image processing

作者： Yixiao Yun Keren Fu Irene Yu-Hua Gu Jie Yang Dept. of Signals and Systems Chalmers Univ. of Technology Inst. of Image Processing & Pattern Recognition Shanghai Jiao Tong Univ.

ISBN: (纸本)9781479957521

This paper addresses issues in video object tracking. We propose a novel method where tracking is regarded as a one-class classification problem of domain-shift objects. The proposed tracker is inspired by the fact that the positive samples can be bounded by a closed hypersphere generated by one-class support vector machines (SVM), leading to a solution for robust learning of target model online. The main novelties of the paper include: (a) represent the target model by a set of positive samples as a cluster of points on Riemannian manifolds;(b) perform online learning of target model as a dynamic cluster of points flowing on the manifold, in an alternate manner with tracking;(c) formulate geodesic-based kernel function for one-class SVM on Riemannian manifolds under the log-Euclidean metric. Experiments are conducted on several videos, results have provided support to the proposed method.

关键词： Visual object tracking Support vector machines One-class classification Online learning Riemannian manifold Covariance matrix

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Automated multi-contrast brain pathological area extraction from 2D MR images

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Journal of Applied Research and Technology 2015年第1期13卷 58-69页

作者： Dvořák, P. Bartušek, K. Kropatsch, W.G. Smékal, Z. Institute of Scientific Instruments of the ASCR V.v.i Academy if Sciences of the Czech Republic Brno Czech Republic Dept. of Telecommunications Faculty of Electrical Engineering and Communication Brno University of Technology Brno Czech Republic Pattern Recognition and Image Processing Group Institute of Computer Graphics and Algorithms Faculty of Informatics Vienna University of Technology Vienna Austria

The aim of this work is to propose the fully automated pathological area extraction from multi-parametric 2D MR images of brain. The proposed method is based on multi-resolution symmetry analysis and automatic thresholding. The proposed algorithm first detects the presence of pathology and then starts its extraction. T2 images are used for the presence detection and the multi-contrast MRI is used for the extraction, concretely T2 and FLAIR images. The extraction is based on thresholding, where Otsu's algorithm is used for the automatic determination of the threshold. Since the method is based on symmetry, it works for both axial and coronal planes. In both these planes of healthy brain, the approximate left-right symmetry exists and it is used as the prior knowledge for searching the approximate pathology location. It is assumed that this area is not located symmetrically in both hemispheres, which is met in most cases. The detection algorithm was tested on 203 T2-weighted images and reached the true positive rate of 87.52% and true negative rate of 93.14%. The extraction algorithm was tested on 357 axial and 443 coronal real images from publicly available BRATS databases containing 3D volumes brain tumor patients. The results were evaluated by Dice Coefficient (axial: 0.85 ± 0.11, coronal 0.82 ± 0.18) and by Accuracy (axial: 0.96 ± 0.05, coronal 0.94 ± 0.09). © 2015 Universidad Nacional Autónoma de México.

关键词： Brain Pathology Brain Tumor MRI Multi-contrast MRI Symmetry Analysis

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Analysis of Complexity Based EEG Features for the Diagnosis of Alzheimer's Disease

Analysis of Complexity Based EEG Features for the Diagnosis ...

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Annual International Conference of the IEEE Engineering in Medicine and Biology Society

作者： Tyler Staudinger Robi Polikar Signal Processing and Pattern Recognition Laboratory Dept. of Electrical and Computer Eng. at Rowan University

ISBN: (纸本)9781424441211

As life expectancy increases, particularly in the developed world, so does the prevalence of Alzheimer's Disease (AD). AD is a neurodegenerative disorder characterized by neurofibrillary plaques and tangles in the brain that leads to neuronal death and dementia. Early diagnosis of AD is still a major unresolved health concern: several biomarkers are being investigated, among which the electroencephalogram (EEG) provides the only option for an electrophysiological information. In this study, EEG signals obtained from 161 subjects - 79 with AD, and 82 age-matched controls (CN) - are analyzed using several nonlinear signal complexity measures. These measures include: Higuchi fractal dimension (HFD), spectral entropy (SE), spectral centroid (SC), spectral roll-off (SR), and zero-crossing rate (ZCR). HFD is a quantitative measure of time series complexity derived from fractal theory. Among spectral measures, SE measures the level of disorder in the spectrum, SC is a measure of spectral shape, and SR is frequency sample below which a specified percent of the spectral magnitude distribution is contained. Lastly, ZCR is simply the rate at which the signal changes signs. A t-test was first applied to determine those features that provide significant differences between the groups. Those features were then used to train a neural network. The classification accuracies ranged from 60-66%, suggesting they contain some discriminatory information;however, not enough to be clinically useful alone. Combining these features and training a support vector machine (SVM) resulted in a diagnostic accuracy of 78%, indicating that these feature carry complementary information.

关键词： Alzheimer's disease EEG Higuchi Fractal Dimension Spectral Entropy Spectral Centroid Spectral Roll-Off Zero-Crossing Rate

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SAM investigations of the segregation of sulphur in polycrystalline α-iron

SAM investigations of the segregation of sulphur in polycrys...

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作者： Eisl, M.M. Reichl, B.M. Böhmig, S.D. Störi, H. Institut für Allgemeine Physik University of Technology Vienna Wiedner Hauptstraße 8-10 ViennaA-1040 Austria Institute for Automation Dept. for Pattern Recognition and Image Processing University of Technology Vienna Wiedner Hauptstr. 8-10 ViennaA-1040 Austria

Scanning Auger microscopy (SAM) and AES investigations of the segregation of non-metal impurity atoms (sulphur, nitrogen, carbon and phosphorus) in polycrystalline high-purity (>99.99%) α-iron samples were performed in the temperature range 500-800°C. At the beginning of the heating, nitrogen segregates homogeneously and quickly but in the equilibrium state the whole surface is covered with nearly half a monolayer of sulphur. During the first measurements, the segregation of sulphur was ruled by a combined bulk and grain boundary diffusion mechanism with a t1/2 law, resulting in a net-shaped structure of the surface concentration of sulphur. After repeated heating and sputtering, the contribution of grain boundary diffusion becomes smaller and the segregation rate of sulphur decreases as a consequence of sulphur depletion of the bulk. The segregation kinetics of the individual grains differ strongly owing to the differences between the extent of sulphur depletion of the grains and can be described very well by a t3/2 law in the initial stage of the segregation process. In a later phase, surface diffusion of sulphur from the grains, which have almost reached the equilibrium sulphur coverage, leads to an almost homogeneous sulphur coverage of the surface with only small and diffuse lateral variations. © 1992 John Wiley & Sons Ltd.

关键词： Sulfur

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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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On fingerprint theorems

On fingerprint theorems

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International Conference on pattern recognition

作者： Wu Lide Xie Zhaohui Comput. Sci. Dept. Fudan Univ. Shanghai China Institute of Image Processing Pattern Recognition Shanghai Jiaotong University Shanghai China

ISBN: (纸本)0818608781

Fingerprint theorems, i.e. under what conditions the primal sketch can determine the image uniquely, are discussed. The weakness of A.L. Yuille and T. Poggio's fingerprint theorem (1986) is pointed out and two novel 1-D fingerprint theorems are presented. Then a practical algorithm based on one of these theorems is given for reconstructing the image from its primal sketch. From the given examples, it is shown that the fingerprint theorems are a substantial improvement over Yuille and Poggio's conjecture that Gaussian function is the only filter which can be used as the basis of a fingerprint theorem. The 1-D fingerprint theorems are generalized to 2-D ones.< >

关键词： Fingerprint recognition Kernel image reconstruction Laplace equations Polynomials Filters Convolution Filtering pattern recognition Shape

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A PERCEPTUALLY ENHANCED BLIND SINGLE-CHANNEL AUDIO SOURCE SEPARATION BY NON-NEGATIVE MATRIX FACTORIZATION

A PERCEPTUALLY ENHANCED BLIND SINGLE-CHANNEL AUDIO SOURCE SE...

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European Signal processing Conference

作者： S. Kubiz B. Gunsel Multimedia Signal Processing and Pattern Recognition Lab. Istanbul Technical University Dept. of Electronics and Communications Engineering

This paper proposes a 2D Non-negative Matrix Factorization (NMF) based single-channel source separation algorithm that emphasizes perceptually important components of audio. Unlike the existing methods, the proposed scheme performs a psychoacoustic pre-processing on the mixture spectrogram in order to suppress audio components that are not critical to human hearing sensation while amplifying the perceptually important ones. This yields the auditory spectrogram referred as sonogram of the observed audio mixture and the individual sources are then extracted by 2D NMF. Test results reported in terms of Signal-to-Distortion-Ratio (SDR), Signal-to-Inference-Ratio (SIR) and Signal-to-Artifact-Ratio (SAR) show that the proposed perceptually enhanced separation improves the quality of decomposed audio sources by 1.5-6.5 dB with a reduced computational complexity.

关键词： Audio matrix decomposition Sonogram factorization complexity classes Spectrogram NMF protocol

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The adaptive subspace map for image description and image database retrieval

The adaptive subspace map for image description and image da...

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8th Meeting of the International Workshop on Structural and Syntactic pattern recognition, SSPR 2000 and 3rd International Workshop on Statistical Techniques in pattern recognition, SPR 2000

作者： De Ridder, Dick Lemmers, Olaf Duin, Robert P.W. Kittler, Josef Pattern Recognition Group Dept. of Applied Physics Delft University of Technology Lorentzweg 1 2628 CJ Delft Netherlands Centre for Vision Speech and Signal Processing Dept. of Electronic and Electrical Engineering University of Surrey Guildford GU2 5XH Surrey United Kingdom

ISBN: (纸本)3540679464

In this paper, a mixture-of-subspaces model is proposed to describe images. images or image patches, when translated, rotated or scaled, lie in low-dimensional subspaces of the high-dimensional space spanned by the grey values. These manifolds can locally be approximated by a linear subspace. The adaptive subspace map is a method to learn such a mixture-of-subspaces from the data. Due to its general nature, various clustering and subspace-finding algorithms can be used. If the adaptive subspace map is trained on data extracted from images, a description of the image content is obtained, which can then be used for various classification and clustering problems. Here, the method is applied to an image database retrieval problem and an object image classification problem, and is shown to give promising results. © Springer-Verlag Berlin Heidelberg 2000.

关键词： Classification (of information)

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Reassignment method for audio sounds classification

Reassignment method for audio sounds classification

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International Conference on Advanced Technologies for Signal and image processing (ATSIP)

作者： Souli Sameh Zied Lachiri Dept. of Genie Electrique ENIT Siqnal Image and pattern recognition research unit BP 37 Le Belvédère Tunisia

ISBN: (纸本)9781467385275

In this paper, we suggest to utilize the reassigned method to improve a recognition of courant life audio signals based on the wavelet features. Reassigned approach generates a new time-frequency description by displacement spectrogram values away from their calculation location. Besides, reassignment method concentrates energy elements by displacing every time-frequency place to its group delay and immediately frequency, which shows precisely different parts of energy. In proposed method we used firstly a translation-invariant wavelet and the local maximum approach, secondly we applied a prototype representation, and eventually a global maximum transformation is implemented. We show the performance of this approach on environmental sounds. The classification result is of the order 93.67 % using one-versus-one SVM classifier.

关键词： Spectrogram Time-frequency analysis Wavelet transforms Support vector machines Prototypes Kernel

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