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On the use of ICA for hyperspectral image analysis

On the use of ICA for hyperspectral image analysis

引用

IEEE International Symposium on Geoscience and Remote Sensing (IGARSS)

作者： A. Villa J. Chanussot C. Jutten J. A. Benediktsson S. Moussaoui GIPSA-Laboratory Signal & Image Department Grenoble Institute of Technology (INPG) France Department of Electrical and Computer Engineering University of Iceland Iceland Laboratoire des Sciences du Numerique de Nantes Nantes Pays de la Loire FR

Independent component analysis (ICA) is a very popular method that has shown success in blind source separation, feature extraction and unsupervised recognition. In recent years ICA has been largely studied by researchers from the signal processing community. This paper addresses a more in-depth study on the use of this method, applied to hyper-spectral images used for remote sensing purposes. In a first part, source separation is addressed. Since the independence of sources is usually not verified in hyperspectral real data images, ICA, if used alone, is not a suitable tool to unmix sources. We propose a hierarchical approximation for the use of ICA as a pre-processing step for a Bayesian Positive Source Separation method. In a second part, the use of ICA for dimensionality reduction is studied in the frame of hyperspectral data classification. Experimental results show the effectiveness of ICA when used for hyperspectral image pre-processing for the two considered applications.

关键词： Independent component analysis Hyperspectral imaging image analysis Hyperspectral sensors Source separation Blind source separation Feature extraction Signal processing Remote sensing Bayesian methods

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Development and Evaluation of Text Localization Techniques Based on Structural Texture Features and Neural Classifiers

Development and Evaluation of Text Localization Techniques B...

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International Conference on Document analysis and Recognition

作者： Christos Emmanouilidis Costas Batsalas Nikos Papamarkos Computational Systems Unit CETI/R. C. ATHENA Xanthi Greece Image Processing and Multimedia Laboratory Department of Electrical & Computer Engineering Democritus University of Thrace Xanthi Greece Democritus University of Thrace Komotini GR

ISBN: (纸本)9781424445004

This paper presents a text localization approach for binarized printed document images. Emphasis is given to the feature extraction and feature selection stages. In the former, several document structure elements and spatial features, likely to convey useful information, are extracted. In the latter, evolutionary multi-objective feature selection is employed to identify combinations of features with simultaneous good performance in terms of text localization sensitivity and specificity. The selected features are applied to a range of classifiers. Performance results over document image sets from known databases are presented, employing the classifiers with or without feature selection. The results suggest that the hybrid techniques, which utilize the classifiers in combination with the customized pre-processing, feature extraction and feature selection stages, exhibit promising performance on a range of document images.

关键词： Feature extraction Data mining image segmentation Text analysis image analysis image texture analysis Sensitivity and specificity Support vector machines Support vector machine classification image recognition

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Brain Tissue Classification Using Independent Vector analysis (IVA) for Magnetic Resonance image

Brain Tissue Classification Using Independent Vector Analysi...

引用

IEEE Symposium on Bioinformatics and Bioengineering (BIBE)

作者： Yaw-Jiunn Chiou Hsiang-Min Chen Jyh Wen Chai Clayton Chi-Chang Chen Yen-Chieh Ouyang Wu-Chung Su Ching-Wen Yang San-Kan Lee Chein-I Chang Department of Electrical Engineering National Chung Hsing University Taichung Taiwan Department of Radiology College of Medicine China Medical University and Hospital Taichung Taiwan Department of Radiology Taichung Veterans General Hospital Taichung Taiwan Department of Radiological Technology Central Taiwan University of Science and technology Taichung Taiwan Computer Center Taichung Veterans General Hospital Taichung Taiwan Remote Sensing Signal and Image Processing Laboratory Department of Computer Science and Electrical Engineering University of Maryland Baltimore MD USA

The purpose of this study is to present a new method, independent vector analysis (IVA), by extending independent component analysis (ICA) of univariate source signals to multivariate source signals on Magnetic Resonance Imaging (MRI). IVA is utilized to relief the limitation of the conventional ICA approach. The proposed method can resolve the permutation problem during individual ICA runs for group brain MR images. The proposed IVA method in conjunction with support vector machine (SVM), we can effectively separate the different part of gray, white matter and cerebrospinal fluid (CSF) from brain soft tissues. In order to demonstrate the proposed IVA-SVM method, experiments are conducted for performance analysis and evaluation. Simulation results show that using IVA can greatly release from the problem cause from traditional ICA to the situation of analyzing inconsistent results of MR image.

关键词： Brain image analysis Magnetic analysis Magnetic resonance Independent component analysis Magnetic resonance imaging Support vector machines Signal analysis image resolution Signal resolution

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Efficient three-view triangulation based on 3D optimization

Efficient three-view triangulation based on 3D optimization

引用

2008 19th British Machine vision Conference, BMVC 2008

作者： Nordberg, Klas Computer Vision Laboratory Department of Electrical Engineering Linköping University Sweden

ISBN: (纸本)9781901725360

Triangulation of a 3D point from two or more views can be solved in several ways depending on how perturbations in the image coordinates are dealt with. A common approach is optimal triangulation which minimizes the total L2 reprojection error in the images, corresponding to finding a maximum likelihood estimate of the 3D point assuming independent Gaussian noise in the image spaces. Computational approaches for optimal triangulation have been published for the stereo case and, recently, also for the three-view case. In short, they solve an independent optimization problem for each 3D point, using relatively complex computations such as finding roots of high order polynomials or matrix decompositions. This paper discuss three-view triangulation and reports the following results: (1) the 3D point can be computed as multi-linear mapping (tensor) applied on the homogeneous image coordinates, (2) the set of triangulation tensors forms a 7-dimensional space determined by the camera matrices, (3) given a set of corresponding 3D/2D calibration points, the 3D residual L1 errors can be optimized over the elements in the 7-dimensional space, (4) using the resulting tensor as initial value, the error can be further reduced by tuning the tensor in a two-step iterative process, (5) the 3D residual L1 error for a set of evaluation points which lie close to the calibration set is comparable to the three-view optimal method. In summary, three-view triangulation can be done by first performing an optimization of the triangulation tensor and once this is done, triangulation can be made with 3D residual error at the same level as the optimal method, but at a much lower computational cost. This makes the proposed method attractive for real-time three-view triangulation of large data sets provided that the necessary calibration process can be performed.

关键词： Tensors

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Unsupervised approach for building non-parametric background and foreground models of scenes with significant foreground activity

Unsupervised approach for building non-parametric background...

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1st ACM Workshop on vision Networks for Behaviour analysis, VNBA'08, Co-located with the 2008 ACM International Conference on Multimedia, MM'08

作者： Martel-Brisson, Nicolas Zaccarin, André Computer Vision and Systems Laboratory Department of Electrical and Computer Engineering Laval University Québec QC Canada

ISBN: (纸本)9781605583136

Kernel-based density estimation have been successful for background subtraction in complex environments where background statistics at the pixel level cannot be described parametrically. These methods, however, typically requires a training sequence free or mostly free of foreground activity in order to get a good initial estimate of the background distribution. We present an approach for non-parametric statistical modeling of both foreground and background in complex and busy environments without any restrictions or constraints on the scene foreground activity at initialization. Our unsupervised approach uses the difference in relative frequency and probability mass between background and foreground modes to generate foreground and background likelihood functions as well as estimates of foreground and background priors. For each frame, the output is a non-binary mask of foreground probabilities which can be easily combined with spatial and temporal constraints in an intelligent decision process. Results show that our approach performs well in a variety of complex scenarios where foreground probabilities can be as high as 80%.© 2008 ACM.

关键词： Probability

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Building detection from aerial images using invariant color features and shadow information

Building detection from aerial images using invariant color ...

引用

2008 23rd International Symposium on computer and Information Sciences, ISCIS 2008

作者： Sirmaçek, Beril Ünsalan, Cem Computer Vision Research Laboratory Department of Electrical and Electronics Engineering Yeditepe University Istanbul 34755 Turkey

ISBN: (纸本)9781424428816

Robust detection of buildings is an important part of the automated aerial image interpretation problem. Automatic detection of buildings enables creation of maps, detecting changes, and monitoring urbanization. Due to the complexity and uncontrolled appearance of the scene, an intelligent fusion of different methods gives better results. In this study, we present a novel approach for building detection using multiple cues. We benefit from segmentation of aerial images using invariant color features. Besides, we use the edge and shadow information for building detection. We also determine the shape of the building by a novel method. © 2008 IEEE.

关键词： Buildings

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Estimation of proper parameter values for document binarization

Estimation of proper parameter values for document binarizat...

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10th IASTED International Conference on computer Graphics and Imaging, CGIM 2008

作者： Badekas, E. Papamarkos, N. Image Processing and Multimedia Laboratory Department of Electrical and Computer Engineering Democritus University of Thrace 67100 Xanthi Greece

ISBN: (纸本)9780889867192

Most of the existing document-binarization techniques deal with many parameters that require a priori setting of their values. Due to the unknown of the ground-truth images, the evaluation of document binarization techniques is subjective and employs human observers for the estimation of the appropriate parameter values. The selection of the appropriate values for these parameters is crucial and influences to the final binarization. However, there is no predetermined set of parameters that guarantees optimal binarization for all document images. This paper proposes a new technique that allows the estimation of proper parameters values for each one of the document binarization techniques. The proposed approach is based on a statistical performance analysis of a set of binarization results, which are obtained by applying various binarization techniques with different parameter values. The proposed statistical performance analysis can also depicts the best document binarization result obtained by a set of document binarization techniques.

关键词： Parameter estimation

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Regional variance dependant sub-frame reduction for face detection in high definition video frames

Regional variance dependant sub-frame reduction for face det...

引用

2007 Applied imagery Pattern Recognition Workshop, AIPR 2007

作者： Livingston, Adam R. Asari, Vijayan K. Computational Intelligence and Machine Vision Laboratory Electrical and Computer Engineering Department Old Dominion University Norfolk VA 23529 United States

ISBN: (纸本)9780769530666

Statistical learning based face detection systems search multiple scale sub-frames of an image or frame of a video stream with a trained classifier to detect face objects. If the frame is large there will be a large number of these sub-frames. Sections of the frame with a regional variance below a predefined threshold do not need to be searched as it is not possible for a face to exist in these spaces. A preprocessing system to eliminate these low variance regions of a frame is presented in this paper. A top down quad-tree deconstruction of the frame is used to accomplish this task. Regional variance is computed and tested to determine if a given quadrant should be further broken down. If below a predefined threshold that region will be eliminated in a mask image. This procedure is continued until all sections are eliminated or a defined tree depth is reached. The resulting mask image is then smoothed and subjected to thresholding, merging the remaining valid search areas. The resulting filled mask is then used to determine whether a given sub-frame should be sent to a Viola-Jones face detection cascade. Preliminary results show promise in reducing the number of sub-frames that must be considered for detection, increasing the speed of the detection system. © 2007 IEEE.

关键词： Face recognition

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Exploring the use of proper orthogonal decomposition for enhancing blood flow images via computational fluid dynamics

引用

11th International Conference on Medical image Computing and computer-Assisted Intervention, MICCAI 2008

作者： McGregor, Robert Szczerba, Dominik Von Siebenthal, Martin Muralidhar, Krishnamurthy Székely, Gábor Computer Vision Laboratory Department of Electrical Engineering ETH Zürich 8092 Switzerland Department of Mechanical Engineering Indian Institute of Technology Kanpur Kanpur UP 208 016 India

ISBN: (纸本)3540859896

Obtaining high quality patient-specific flow velocity information is not an easy task. Available clinical data are usually poorly resolved and contain a significant amount of noise. We propose a novel approach to integrate computational fluid dynamics with measurement data to overcome this difficulty. By performing a proper orthogonal decomposition of simulated blood flow patterns for a given vascular location with various anatomical configurations it is possible to obtain a basis model for flow reconstruction. This is used to interpolate imaging data intelligently without having to perform a full flow simulation for each individual patient. This work focuses on assessing the feasibility of such a method. © 2008 Springer Berlin Heidelberg.

关键词： Computational fluid dynamics

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Block truncation signature coding for hyeprspectral analysis

Block truncation signature coding for hyeprspectral analysis

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Imaging Spectrometry XIII

作者： Chakravarty, Sumit Chang, Chein-I. Remote Sensing Signal and Image Processing Laboratory Department of Computer Science and Electrical Engineering University of Maryland Baltimore County Baltimore MD 21250 United States

ISBN: (纸本)9780819473066

This paper introduces a new signature coding which is designed based on the well-known Block Truncation Coding (BTC). It comprises of bit-maps of the signature blocks generated by different threshold criteria. Two new BTC-based algorithms are developed for signature coding, to be called Block Truncation Signature Coding (BTSC) and 2-level BTSC (2BTSC). In order to compare the developed BTC based algorithms with current binary signature coding schemes such as Spectral Program analysis Manager (SPAM) developed by Mazer et al. and Spectral Feature-based Binary Coding (SFBC) by Qian et al, three different thresholding functions, local block mean, local block gradient, local block correlation are derived to improve the BTSC performance where the combined bit-maps generated by these thresholds can provide better spectral signature characterization. Experimental results reveal that the new BTC-based signature coding performs more effectively in characterizing spectral variations than currently available binary signature coding methods.

关键词： Managers

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