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The method of image restoration in the environments of dust

The method of image restoration in the environments of dust

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International Conference on Mechatronics and Automation

作者： Wang, Yuanyu Li, Yuanzong Zhang, Tianxu Department of Computer Fundamental Education TaiYuan University of Technology TaiYuan CO 030024 China College of Mechanical Engineering TaiYuan University of Technology TaiYuan CO 030024 China Institute for Pattern Recognition and A.I. State Key Laboratory of Image Processing and Intelligent Control HuaZhong University of Science and Technology WuHan CO 430074 China

ISBN: (纸本)9781424451418

In order to solve the problem of image degradation caused by dust environments, an image degradation model considering multiple scattering factors caused by dust was first established using the first-order multiple scattering method. Then, a dark channel prior principle was applied to present an image restoration algorithm based on the image degradation model. Finally, a particle swarm optimization algorithm was applied to optimize the atmospheric light and the exposure parameters. This optimization algorithm was established according to the criterion of the image evaluation based on kirsch operator with dual threshold. By using the method an optimistic result of image restoration was obtained. The experimental results have shown that the method not only enhanced luminance and contrast, but also discovered more detail edges information. The method provided a foundation for target recognition in the dust environments. © 2010 IEEE.

关键词： Dust

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Modeling multi-source remote sensing image classifier based on the MDL principle: Experimental studies

Modeling multi-source remote sensing image classifier based ...

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IEEE International Conference on Systems, Man and Cybernetics

作者： Huaiying Xia Rukun Hu Bingxin Xu Ping Guo Image Processing and Pattern Recognition Laboratory Beijing Normal University Beijing China School of Computer Science and Technology Beijing Institute of Technology Beijing China

In classification of multi-source remote sensing image, it is usually difficult to obtain higher classification accuracy. In the previous work, the modeling technique for the remote sensing image classification based on the minimum description length (MDL) principle with mixture model is analyzed theoretically. In this work, experimental studies are performed for investigating the modeling technique. With intensive experiments and sophisticated analysis, it is found that the developed modeling technique can build a robust classification system, which can avoid classifier over-fitting training data and make the learning process trade-off between bias and variance. Meanwhile, designed mixture model is more efficient to represent real multi-source remote sensing images compared to single model.

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Kernel ICA applied to feature extraction for image annotation

Kernel ICA applied to feature extraction for image annotatio...

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International Conference on Intelligent Systems Design and Applications (ISDA)

作者： Bingxin Xu Ping Guo Image Processing and Pattern Recognition Laboratory Beijing Normal University Beijing China The School of Computer Science and Technology Beijing Institute of Technology Beijing China

In automatic image annotation, it is often extracting low-level visual features from original image for the purpose of mapping to high level image semantic information. In this paper, we propose a novel method which integrates kernel independent component analysis (KICA) and support vector machine (SVM) for analyzing the semantic information of natural images. KICA, which contains a nonlinear kernel mapping component, is adopted to extract low-level features from the original image data. Then these feature vectors are mapped to high-level semantic words using SVM to annotate images with labels in a given semantic label set. Comparative studies have done for the performance of KICA with traditional color histogram and discrete cosine transform features. The experimental results show that the proposed method is capable of extracting the components of images as key features, and with these features to map into semantic categories, higher accuracy is achieved.

关键词： Feature extraction image color analysis Support vector machines Discrete cosine transforms Histograms Semantics Kernel

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Feature data optimization with LVQ technique in semantic image annotation

Feature data optimization with LVQ technique in semantic ima...

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International Conference on Intelligent Systems Design and Applications (ISDA)

作者： Ziheng Jiang Jing He Ping Guo School of Computer Science and Technology Beijing Institute of Technology Beijing China Image Processing and Pattern Recognition Laboratory Beijing Normal University Beijing China

In order to improve the classifier performance in semantic image annotation, we propose a novel method which adopts learning vector quantization (LVQ) technique to optimize low level feature data extracted from given image. Some representative vectors are selected with LVQ to train support vector machine (SVM) classifier instead of using all feature data. Performance is compared between the methods with and without feature data optimization when SVM is applied to semantic image annotation. Experiment results show that the proposed method has a better performance than that without using LVQ technique.

关键词： Feature extraction Training Semantics Support vector machine classification Testing Neurons

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Statistical parametric mapping of FMRI data using sparse dictionary learning

Statistical parametric mapping of FMRI data using sparse dic...

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IEEE International Symposium on Biomedical Imaging

作者： Kangjoo Lee Jong Chul Ye Bio-Imaging & Signal Processing Laboratory Department Bio & Brian Engineering Korea Advanced Institute of Science and Technology Daejeon South Korea

ISBN: (纸本)9781424441259;9781424441266

Statistical parametric mapping (SPM) of functional magnetic resonance imaging (fMRI) uses a canonical hemodynamic response function (HRF) to construct the design matrix within the general linear model (GLM) framework. Recently, there has been many research on data-driven method on fMRI data, such as the independence component analysis (ICA). The main weakness of ICA for fMRI is its restrictive assumption, especially independence. Furthermore, recent study demonstrated that sparsity is more important than independency in ICA analysis for fMRI. Hence, we propose sparse learning algorithm, such as K-SVD, as an alternative, that decomposes the dictionary-atoms using sparsity rather than independence of the components. For the fMRI finger tapping task data, we employed the K-SVD algorithm to extract the time-course signal atoms of brain activation. The activation maps using trained dictionary as a design matrix showed tightly localized signals in a small set of brain areas.

关键词： Dictionaries Independent component analysis Scanning probe microscopy Magnetic resonance imaging Hemodynamics Sparse matrices Magnetic analysis Fingers Data mining signal design

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A sparse Bayesian learning for highly accelerated dynamic MRI

A sparse Bayesian learning for highly accelerated dynamic MR...

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IEEE International Symposium on Biomedical Imaging

作者： Hong Jung Jong Chul Ye Bio-Imaging & Signal Processing Laboratory Department of Bio and Brain engineering Korea Advanced Institute of Science and Technology Daejeon South Korea

ISBN: (纸本)9781424441259;9781424441266

In dynamic MRI, spatio-temporal resolution is a very important issue. Recently, compressed sensing approach has become a highly attracted imaging technique since it enables accelerated acquistion without aliasing artifacts. Our group has proposed an ℓ 1 -norm based compressed sensing dynamic MRI called k-t FOCUSS, which outperforms existing methods. However, it is known that the restrictive conditions for ℓ 1 exact reconstruction usually cost more measurements than ℓ 1 minimization. In this paper, we adopts a sparse Bayesian learning approach to improve k-t FOCUSS and achieve ℓ 0 solution. We demonstrated the improved image quality using in vivo cardiac cine imaging.

关键词： Bayesian methods Acceleration Magnetic resonance imaging Compressed sensing Focusing High-resolution imaging image reconstruction Costs image quality In vivo

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Application of bhattacharyya distance as a dissimilarity index for automated prediction of epileptic seizures in rats

Application of bhattacharyya distance as a dissimilarity ind...

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International Conference on Intelligent and Advanced Systems, ICIAS

作者： M. Niknazar B. Vosoughi Vahdat M. B. Shamsollahi M. Sayyah Biomedical Signal and Image Processing Laboratory (BiSIPL) School of Electrical Engineering Sharif University of Technology Tehran Iran Physiology and Pharmacology Department Pasteur Institute of Iran Tehran Iran

Seizures are defined as manifest of excessive and hypersynchronous activity of neurons in the cerebral cortex and represent a frequent malfunction of the human central nervous system. Therefore, the search for precursors and predictors of a seizure is of utmost clinical relevance and may even guide us to a deep understanding of the seizure generating mechanisms. In this study we analyzed invasive electroencephalogram (EEG) recordings in rats with experimentally induced generalized epilepsy with a nonlinear method called, dissimilarity index. In order to predict epileptic seizures automatically, Bhattacharyya distance between trajectory matrix of reference window, during an interval quite distant in time from any seizure, and trajectory matrix of present window is employed to measure dynamical dissimilarity index. It is found that employed the method can detect the dissimilarity changes of the neural activity prior to epileptic seizures.

关键词： Electroencephalography Rats Indexes Trajectory Epilepsy Artificial neural networks Phase detection

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Identical fits of nonnegative matrix/tensor factorization may correspond to different extracted event-related potentials

Identical fits of nonnegative matrix/tensor factorization ma...

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International Joint Conference on Neural Networks (IJCNN)

作者： F. Cong A.H. Phan A. Cichocki H. Lyytinen T. Ristaniemi Department of Mathematical Information Technology University of Jyvaskyla Agora Finland Laboratory for Advanced Brain Signal Processing Brain Science Institute RIKEN Japan

Nonnegative Matrix/Tensor factorization (NMF/NTF) have been used in the study of EEG, and the fit (explained variation) is often used to evaluate the performance of a nonnegative decomposition algorithm. However, this parameter only reveals the information derived from the mathematical model and just exhibits the reliability of the algorithms, and the property of EEG can not be reflected. If fits of two algorithms are identical, it is necessary to examine whether the desired components extracted by them are identical too. In order to verify this doubt, we performed NMF and NTF on the same dataset of an auditory event-related potentials (ERPs), and found that the identical fits of NMF and NTF under the hierarchical alternating least squares algorithms corresponded to different desired ERPs extracted by NMF and NTF, moreover, NTF contributed the ERP with much better timing and spectral properties. Such analysis implies that to combine the fit and property of the desired ERP component together helps evaluate the performance of NMF and NTF algorithms in the study of ERPs.

关键词： Time frequency analysis Variable speed drives DSL

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In-orbit performance of the SMOS reference radiometers - results from the early commissioning phase

In-orbit performance of the SMOS reference radiometers - res...

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Microwave Radiometry and Remote Sensing of the Environment, MICRORAD

作者： Juha Kainulainen Andreas Colliander Department of Signal Processing and Acoustics School of Science and Technology Aalto University Aalto Finland Je Propulsion Laboratory California Institute of Technology Pasadena CA USA

In this paper we present a preliminary analysis of the radiometric performance of the three 1.4 GHz Noise Injection Radiometers of the SMOS satellite. We assess the radiometric resolution and stability of the receivers, and the similarity between their measurements. The units aim at measurements of a common antenna temperature, which determines the overall brightness temperature level of SMOS retrievals. For this purpose, we use measurement data gathered during the first two months of the in-orbit operations of the satellite, which was launched in November 2009. The preliminary assessment of the abovementioned performance parameters shows that the units meet the requirements with a margin.

关键词： Antenna measurements Temperature measurement Extraterrestrial measurements Sea measurements Calibration Ocean temperature Noise

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Fast Marine Route Planning for UAV Using Improved Sparse A* Algorithm

Fast Marine Route Planning for UAV Using Improved Sparse A* ...

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International Conference on Genetic and Evolutionary Computing (ICGEC)

作者： Xin Yang Ming-yue Ding Cheng-ping Zhou Institute for Pattern Recognition and Artificial Intelligence (IPRAI) Image Processing and Intelligence Control Key Laboratory of Education Ministry of China Huazhong University of Science and Technology Wuhan Hubei China School of Life Science and Technology Image Processing and Intelligence Control Key Laboratory of Education Ministry of China Huazhong University of Science and Technology Wuhan Hubei China Institute for Pattern Recognition and Artificial Intelligence (IPRAI) State Key Laboratory for Multispectral Information Processing Technologies Huazhong University of Science and Technology Wuhan Hubei China

This paper focuses on route planning, especially for unmanned aircrafts in marine environment. Firstly, new heuristic information is adopted such as threat-zone, turn maneuver and forbid-zone based on voyage heuristic information. Then, the cost function is normalized to obtain more flexible and reasonable routes. Finally, an improved sparse A* search algorithm is employed to enhance the planning efficiency and reduce the planning time. Experiment results showed that the improved algorithm for aircraft in maritime environment could find a combinational optimum route quickly, which detoured threat-zones, with fewer turn maneuver, totally avoiding forbid-zones, and shorter voyage.

关键词： Unmanned aerial vehicles Planning Synthetic aperture sonar Cost function Algorithm design and analysis Heuristic algorithms Leg

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